TIME/DATE: 2025.9.22  10:00

CLASSROOM: A402

ABSTRACT

While Diagnosis-Related Group (DRG) payment schemes are widely adopted to incentivize cost reductions, many countries and regions persist in using Fee-for-Service (FFS) payment for outpatient services. This hybrid reimbursement system may prompt providers to make strategic responses on reducing treatment cost, shifting mild inpatients to outpatient care, and unbundling a portion of inpatient costs as outpatient expenses. To evaluate the behavioral incentives of this payment structure and the associated impacts, we first examine the regulator’s optimal solutions regarding cost reduction, care shifting, and cost unbundling aimed at maximizing social welfare. The analysis reveals that regulatory goals should be different according to providers’ outpatient care capabilities. Based on the yardstick competition framework, we then investigate providers’ equilibrium decisions. Our findings demonstrate that traditional yardstick competition fails to meet regulatory goals, as providers’ cost unbundling behavior diminishes their incentives to reduce costs and shift care. To facilitate regulatory goals and enhance social welfare, we further propose several possible approaches. First, for providers with low outpatient care capabilities, a coarser DRG grouping model is advisable, whereas a finer model is more suitable for high-capability providers. Second, stringent monitoring and regulation of cost unbundling practices are essential. Additionally, for high-capability providers, implementing yardstick competition for minor inpatient treatments yields significant efficiency and should be recommended.

GUEST BIO

王杉，现任中山大学管理学院副教授、逸仙优秀学者。2019年于上海交通大学安泰经济与管理学院获博士学位，研究方向聚焦于含复杂行为与约束的服务系统设计及优化，尤其关注医疗保健这一特殊应用领域，研究目标为实现医疗服务的及时、有效与高效供给。相关成果发表于Management Science、M&SOM等国内外权威期刊，获国家自然科学基金优秀青年科学基金项目资助，并荣获教育部高等学校科学研究优秀成果奖（人文社会科学）二等奖、POMS CHOM最佳论文奖等国内外学术奖项。目前担任Health Care Management Science副主编。

TIME/DATE: 2025.9.25  10:00

CLASSROOM: A505

ABSTRACT

Organizations often decouple formal policies from actual practices to gain legitimacy, yet such strategies carry risks as legitimacy pressures increasingly demand accountability, transparency, and measurable outcomes. To sustain legitimacy over time, firms must adapt their decoupling strategies—but research has paid limited attention to how these strategies evolve. Drawing on a ten-year study of China’s HNTE certification program, where technology firms sought legitimacy as innovative entities, we identify two types of R&D-related decoupling and track their evolution. We find that after obtaining certification, firms reduce their initial decoupling but do not fully comply with regulatory expectations; instead, they shift to subtler, less detectable forms of decoupling. These dynamics are amplified under stronger formal regulatory pressures and informal social expectations. Our findings advance understanding of the temporal dynamics of decoupling and offer practical insights for managing legitimacy in complex regulatory environments.

GUEST BIO

王鹤丽教授现为新加坡管理大学李光前商学院战略管理讲席教授（Janice Bellace Professor）、研究生院院长、长江学者讲座教授，曾担任Academy of Management Journal和Academy of Management Review的副主编，以及Management Organization Review的咨询编辑。王鹤丽教授的研究聚焦于企业资源基础理论、战略人力资本、利益相关者管理和企业社会责任，众多研究成果发表于战略与组织领域顶级期刊，包括Academy of Management Review、Academy of Management Journal、Strategic Management Journal、Organization Science和Journal of International Business Studies等。

TIME/DATE：2025.9.19   10:00

CLASSROOM：A502

ABSTRACT

This work studies offline dynamic pricing without data coverage assumption, thereby allowing for any price including the optimal one not being observed in the offline data. Previous approaches that rely on the various coverage assumptions such as that the optimal prices are observable, would lead to suboptimal decisions and consequently, reduced profits. We address this challenge by framing the problem to a partial identification framework. Specifically, we establish a partial identification bound for the demand parameter whose associated price is unobserved by leveraging the inherent monotonicity property in the pricing problem. We further incorporate pessimistic and opportunistic strategies within the proposed partial identification framework to derive the estimated policy. Theoretically, we establish rate-optimal finite-sample regret guarantees for both strategies. Empirically, we demonstrate the superior performance of the newly proposed methods via a synthetic environment. This research provides practitioners with valuable insights into offline pricing strategies in the challenging no-coverage setting, ultimately fostering sustainable growth and profitability of the company.

GUEST BIO

Zeyu Bian is an Assistant Professor in the Department of Statistics at Florida State University. He received his Ph.D. in Biostatistics from McGill University in 2022. His research focuses on reinforcement learning and causal inference, with applications in dynamic and personalized pricing.

TIME/DATE：2025.9.16   13:30

CLASSROOM：Room 305, Tongji Building A

TIME/DATE：2025.7.11   13:30

CLASSROOM：Room 2207, Tongji Building A

ABSTRACT：

This paper examines how racial stereotypes and perceptions of prior discrimination affect the rehiring of NBA head coaches. It finds that while race may lead to negative biases, these can turn into a positive asset if relevant performance information is available. Organizations aware that a qualified Black coach might have been unfairly dismissed due to racial bias may see the coach as a “hidden gem” and rehire them more readily. However, if a coach’s past performance seems to confirm negative stereotypes, they face further discrimination. The race of the coach’s former terminating manager also matters: if the dismissal occurred under a manager of a different race, implying possible taste-based discrimination, the coach’s chances of rehiring improve; if under a manager of the same race, however, the dismissal is seen as reflecting the coach’s true ability, lowering rehiring chances. Overall, perceived racial bias influences whether a candidate is seen as undervalued or genuinely unqualified, affecting their rehiring prospects.

GUEST BIO：

Tracy Li is currently a postdoc fellow at Fudan University. Dr. Li graduated from Singapore Management University with a Ph.D. in Strategic Management & Organization. Previously, he worked as a lecturer at Curtin University, Australia. His research focuses on strategic human capital resources, resource-based view, and institutional theory. His work has been accepted at the Strategic Management Journal and conditionally accepted at the Journal of Business Research. His working papers have also been nominated as a finalist for the Best Dissertation Award at SMS and for the IACMR 2025 Best Conference Macro Paper Award, respectively.

TIME/DATE: 2025.7.9  10:00

CLASSROOM: A1201

ABSTRACT:

App-based, on-demand transport systems have transformed mobility by enabling real-time assignment of passengers to vehicles. But behind this flexibility lies a fundamental tension: short-term operational decisions can produce long-term system consequences. This talk will explore these temporal frictions in assigned on-demand systems—where passengers and vehicles are matched before meeting—through the lens of three key challenges:

1. Vehicle Dynamics: We show how assignment decisions lead to the Increasing Gap Dynamics, a negative feedback cycle where vehicles tend to concentrate.

2. Anticipatory Design: From routing to pickup/dropoff locations, the best decisions often depend on future, uncertain requests. We present anticipatory methods that improve service while confronting real-world user acceptance limits.

3. Unreliability: Sharing a vehicle creates dependencies among users. We introduce the shareability shadow concept to quantify and mitigate such unreliability.

Together, these challenges underscore the limits of myopic optimisation without foresight, and the need for adaptive, forward-looking strategies in shared mobility systems.

GUEST BIO：

Andres Fielbaum is a Mathematical Engineer with an MSc in Transport Engineering and a PhD in Systems Engineering from the University of Chile. He is currently a Lecturer (Assistant Professor) in the School of Civil Engineering at the University of Sydney, where he leads the project “Next Generation of On-Demand Public Transport: Strategies and Algorithms”, funded by the Australian Research Council’s competitive DECRA scheme. Previously, he was a postdoctoral researcher in the Department of Cognitive Robotics at TU Delft. His research focuses on public and on-demand transport, transport networks, emerging mobility technologies, and algorithmic approaches to transport system design.

TIME/DATE: 2025.7.5   14:30-16:00

CLASSROOM: A1922

ABSTRACT:

Given AI’s abilities to process “big data” and perform the tasks consistently, algorithms are expected to outperform humans in many circumstances. We therefore see vast implementations of algorithmic decision-makers, advisors, and recommenders in our business and society, with the least mature category of algorithmic decision-makers and the most mature category of algorithmic recommenders. With the recent emergence and rising of generative AI, new forms of applications with more capabilities than ever have emerged. In this seminar, I will introduce a series of published and ongoing research works that aim to explore the impact of the various AI-empowered applications and their interactions with humans from the IS perspective.

GUEST BIO:

Dr. Xitong Li is a professor of information systems at HEC Paris and a research fellow of Hi! PARIS, the joint research center between HEC Paris and Polytechnic Institute of Paris (QS World University Ranking Top 50). His primary research interests are in the economics of information and AI technologies, including social media, FinTech, digital marketing, online education, human-AI/algorithms collaboration. His primary research methods include applied econometric analysis, field and laboratory experiments. Xitong’s research appears in leading international journals, such as Management Science, Information Systems Research, Management Information Systems Quarterly, Production and Operations Management, Journal of Operations Management, Journal of Management Information Systems, and various ACM/IEEE Transactions. Xitong’s research has been granted by ANR AAPG France (solo PI), equivalent to National Science Foundation (NSF) in the US. His research has also been granted by Hi! PARIS Research Fellowship. Xitong currently serves as an Associate Editor for Information Systems Research, a top journal in the information systems field. He also served as a guest senior editor for Production and Operations Management, a top journal in the operations management field. Xitong received INFORMS Information Systems Society (ISS) Sandy Slaughter Early Career Award in 2022, and the HEC Foundation Researcher of the Year Award in 2023. Xitong served as a Program Co-chair of the 42nd International Conference on Information Systems (ICIS) 2021, the premier international conference in IS. He received a Ph.D. in management from MIT Sloan School and a Ph.D. in engineering from Tsinghua University.

TIME/DATE: 2025.7.2   10:00

CLASSROOM: A1201

ABSTRACT:

On-demand food delivery (OFD) services have experienced a significant surge in popularity in recent years, which poses various operational challenges for service operators such as Meituan, Doordash, among others. Many studies have been established to investigate this new type of urban transportation. However, the OFD related literature is still at an early stage where there lacks fundamental study in decision-making analysis, network equilibrium analysis, empirical analysis, etc. In this talk, three recent research works of my research group will be introduced. First, an analytical model will be presented. It captures the complex interplay of the OFD system by considering adjustable service region size, order bundling, and batch-matching processes. Various managerial insights are derived in mathematical language and depicted in numerical experiments. Second, the OFD service is analyzed in a network context, where the spatial heterogeneity and network effects are well considered. In the network model, drivers’ traveling behavior appear to be more complex since it involves both individual choice behaviors and the dispatching decisions from the platform. The three market players and a central platform are modeled in a Stackelberg leader-follower game structure where their behaviors and the network matching equilibrium are analyzed. Finally, we study the uncertainty of the OFD service, specifically on the customers’ order cancellation behaviors. Our findings reveal distinct cancellation patterns across different stages of the order fulfillment process: the matching and pick-up stage.

GUEST BIO:

Dr. Jintao Ke is an Assistant Professor in the Department of Civil Engineering at the University of Hong Kong (HKU). Dr. Ke received his B.S. degree (2016) in Civil Engineering from Zhejiang University, and his PhD degree (2020) in Civil and Environment Engineering from Hong Kong University of Science and Technology. His research interests include on demand mobility services, transportation big data analytics, multimodal transportation system optimization, transportation pricing, spatiotemporal traffic prediction, etc. The vision of his research is to develop novel models, algorithms, and conduct data-driven quantitative analyses to better manage, operate, and regulate various types of emerging mobility services. He has published more than 50 SCI/SSCI indexed research papers in top-tier journals in the field of transportation research and data mining, such as Transportation Research Part A-F, IEEE Transactions on Intelligence Transportation System, IEEE Transactions on Knowledge and Data Engineering, IEEE Internet of Things, Computer-Aided Civil and Infrastructure Engineering. He has been ranked as the World’s Top 2% most-cited scientists by Stanford University since 2023. He is serving as an Editorial Board Member of Transportation Research Part C, Transportation Research Part E, and Travel Behavior and Society.

TIME/DATE: 2025.7.3   13：30

CLASSROOM: A408

ABSTRACT:

Online linear programming (OLP) has gained significant attention from both researchers and practitioners due to its extensive applications, such as online auction, network revenue management, order fulfillment and advertising. Existing OLP algorithms fall into two categories: LP-based algorithms and LP-free algorithms. The former one typically guarantees better performance, even offering a constant regret, but requires solving a large number of LPs, which could be computationally expensive. In contrast, LP-free algorithm only requires first-order computations but induces a worse performance, lacking a constant regret bound. In this work, we bridge the gap between these two extremes by proposing a well-performing algorithm, that solves LPs at a few selected time points and conducts first-order computations at other time points. Specifically, for the case where the inputs are drawn from an unknown finite-support distribution, the proposed algorithm achieves a constant regret (even for the hard “degenerate” case) while solving LPs only O(log log T) times over the time horizon T. Moreover, when we are allowed to solve LPs only M times, we design the corresponding schedule such that the proposed algorithm can guarantee a nearly O(T^(1/2)^(M−1)) regret. Our work highlights the value of resolving both at the beginning and the end of the selling horizon, and provides a novel framework to prove the performance guarantee of the proposed policy under different infrequent resolving schedules. Furthermore, when the arrival probabilities are known at the beginning, our algorithm can guarantee a constant regret by solving LPs O(log log T) times, and a nearly O(T^(1/2)^M) regret by solving LPs only M times. Numerical experiments are conducted to demonstrate the efficiency of the proposed algorithms.

GUEST BIO:

Dr. Guokai Li is an incoming Postdoctoral Fellow at McGill University and Queens University, where he will work with Professors Stefanus Jasin, Murray Lei and Alys Liang. He received his B.S. in Industrial Engineering from Xi’an Jiaotong University and his Ph.D. in Data Science from The Chinese University of Hong Kong, Shenzhen. Dr. Li’s research interests lie in online resource allocation, revenue management, and OM-marketing interface. His papers have been published in M&SOM, WINE conference (CCF-A), and NRL. He has received research awards such as the POMS-HK Student Paper Competition (Finalist) and the ORSC-DS Student Paper Competition (Winner). He also serves as ad hoc reviewers for several leading journals, including OR, M&SOM and POM.

Date & Time: Fri. 4, July 2025, from 10:00 AM to 11:30 AM (Beijing Time)

Place: Tongji Building A2101

ABSTRACT

Online product and service ratings have great value for both sellers and consumers. Prior research, however, often treats online ratings equally even if the individuals who generate these ratings have very different backgrounds. This study examines how tourists differ from locals when they generate online ratings. We find that, relative to locals, tourists exhibit an upward bias when they rate restaurants. More specifically, a consumer as a tourist is at least 13.4% more likely than as a local to give a higher rating (versus all lower ones) to a restaurant. We explore possible mechanisms underlying this tourist bias. Based on data from an online review platform for restaurants, we first confirm the phenomenon of upward tourist bias in online ratings at both reviewer- and restaurant-level with multiple robustness checks. Then we conduct a series of analyses from reviewer-, restaurant-, cuisine- and city-level to identify factors leading to such a bias. We are able to examine the reasons related to consumption pattern such as restaurant price/service/environment, cuisine authenticity, tourists’ evaluation process, differences between city sizes, etc. We find that individuals’ change in focus (from location, cooking, and price to service, environment, and emotions) and change in evaluation process (from cognitive to affective) can induce the tourist bias in ratings. We also discuss theoretical and practical implications for online review platforms, product retailers, and consumers.

Keywords: consumer behavior; word-of-mouth; online review generation; online ratings; tourist bias