How should retailers and sellers distribute inventory across a large network of (potentially hundreds of) distribution centers? Motivated by the emerging challenges of inventory allocation facing volatile demand, we develop distributionally robust network inventory management policies with cross-fulfillment using a scenario-based approach that is adaptable to feature information and demand correlation. We analytically demonstrate the value of scenario modeling for a two-location newsvendor problem. The key technical development is a computationally efficient linear programming formulation for large networks, which we further incorporate into a dynamic inventory policy combined with look-ahead approximation. We validate the superior performance of the proposed policies using real-world transaction data from a leading logistics service provider and a major retailer in China. The developed policy outperforms existing data-driven approaches and can save up to 19% of operating costs over the current policy employed by the company.
Understanding passengers' waiting behavior is crucial in the ride-sourcing market, as it offers valuable insights into customer preferences, tolerance thresholds, and decision-making processes. During the waiting period, passengers receive updated delay announcements; however, the remaining wait time in these updates may not decrease as anticipated due to the platform's limited predictive accuracy or unexpected traffic conditions. Such expectation disconfirmation can lead to increased frustration and, in some cases, result in customers abandoning the service. In this study, we perform an empirical analysis of passengers' waiting behavior in four metropolitan areas in China using the survival analysis model. Our findings reveal an asymmetric response to expectation disconfirmation during the waiting process and display dynamic sensitivity to disconfirmation based on sunk waiting time. Furthermore, we introduce the concept of willingness-to-wait (WTW) to examine passengers' waiting behavior with the combined effects of sunk waiting time and expectation disconfirmation. The results indicate that WTW follows a non-linear decline and exhibits distinct patterns across different areas and time periods. These insights provide actionable guidance for platforms to optimize subsequent delay announcements, enhancing their ability to improve service quality.
User data is central to the operations of many online platforms, enabling personalized services for users and targeted advertising for advertisers. However, as users become increasingly aware of privacy issues associated with platform usage, they may alter their usage levels and engagement patterns, such as switching between singlehoming (using one platform exclusively) and multihoming (using multiple platforms). These shifts in user behavior are not only reshaping platform dynamics but also creating ripple effects for advertisers. In this study, we develop a game-theoretic model to explore how privacy concerns influence user strategies, advertiser decisions, and platform performance, and to assess the effectiveness of common privacy practices implemented by platforms. Our analysis generates several interesting insights. First, we demonstrate that as privacy concerns increase, users are motivated to shift from singlehoming to multihoming, especially when their privacy concerns are relatively low. This shift occurs because multihoming allows users to limit their data exposure on any single platform, thereby reducing the potential for platforms to exploit their data in depth. Second, we find that heightened privacy concerns can unexpectedly benefit platforms by increasing user traffic and aggregate usage, which makes platforms more appealing to advertisers and increases advertiser demand. Third, our analysis reveals that increased privacy concerns can reduce the market dominance of the large platform with strong data capabilities, narrowing the gap in market share between competing platforms. Furthermore, we discuss important managerial and policy implications regarding partial data use and privacy protection, offering insights into how platforms can balance user privacy and platform performance. Finally, we extend our model in several directions to demonstrate the robustness of our key findings and obtain additional insights.
The SPOT framework integrates supply (S), product (P), ownership (O), and technology (T) networks to offer a holistic and dynamic lens for supply chain analysis. Its construction relies on diverse data sources such as supplier-customer relationships, ownership records, product compositions/key equipment, and patent data. Two actionable, multi-phase SPOT implementation roadmaps are introduced: one for enhancing supply chain resilience through integrated risk diagnosis and mitigation strategies, and the other for improving sustainability by diagnosing high-emission components and deploying low-emission alternatives via technology and supply networks. Three illustrative cases—mineral commodity supply resilience, China's TOP 1000 Program for sustainability, and GE HealthCare China’s supply chain strategies—demonstrate how SPOT inspires research questions and guides thought experiments along four key dimensions of supply chain systems. Amid geopolitical tensions and trade wars, adopting a multilayered network perspective of SPOT helps spot a wide range of research opportunities in global supply chains, including supply chain reconfiguration, global expansion of Chinese firms, integrated risk propagation across supply and ownership networks, multi-dimensional network design, and technology-driven network adaptations. By providing a systematic and practical framework, SPOT empowers researchers, firms, and policymakers to analyze and monitor complex supply chain systems, spot vulnerabilities, and navigate this elusive world.
Sniping—the practice of placing the initial bid in the final seconds of an online auction—is widely observed on eBay, yet its causal impact on bidder welfare remains contested because of the endogenous and strategic nature of bidding in real markets. We address this gap by developing a theoretical model of bidder heterogeneity and empirically examining the causal impact of sniping on winner surplus using a unique dataset of Xbox 360 console auctions on eBay. We show that snipers systematically target higher-value auctions—on average 5.8% more expensive than those won by early entrants—yet secure substantially greater payoffs. Leveraging instrumental-variable (IV) regression with a value-type instrument constructed from collective valuation to mitigate endogeneity, we find that sniping increases winner surplus nearly eight-fold, corresponding to an average gain of about $40 per auction. To investigate the underlying mechanism through which sniping increases winner surplus, we conducted a complementary counterfactual analysis based on propensity-score matching (PSM). The analysis reveals that, by concealing bids until the close, sniping suppresses price escalation and reduces final auction prices by roughly $33 (≈7% of item value) relative to early entry. Subsample analysis further shows that strategic sniping behavior is especially pronounced in higher-stake auctions. These results highlight bid timing as a central strategic lever in online auctions, demonstrate that sniping enhances buyer welfare under common-value uncertainty, and provide auction platforms with a systematic framework to anticipate when and where sniping is most likely to occur.
Recent press coverage of piracy and digital goods touts the practice of subscription (as opposed to selling) as a “piracy killer.” However, the effectiveness of digital goods subscriptions remains controversial in terms of the profitability for different supply chain members, including content providers and retailers. Specifically, the dearth of existing studies concerning business model choices in distribution channel structures indicates that the literature has yet to provide a comprehensive answer to this question. Therefore, we develop an analytical model to investigate the optimal business model choices for digital goods firms in the presence of digital piracy in a centralized supply chain (CSC) or a decentralized supply chain (DSC), explicitly considering heterogeneous consumer usage rates (both heavy and light). Unique to the current literature, we find that illegal copies serve as a substitute for a different set of consumers, depending on the business model used by the firms. In particular, there are circumstances under which the firms optimally allow heavy-usage consumers to adopt illegal copies in the subscription model. In contrast, illegal copies can also serve as a substitute for the light-usage consumers in the selling-ownership model. Unlike current literature, we identify situations in a CSC whereby the selling-ownership model (a) is more profitable and (b) has fewer illegal goods adopted than the subscription model when piracy is present in the market. When analyzing a DSC, we find that the existence of piracy can actually aid in the coordination of the supply chain because digital piracy serves as a shadow competitor that effectively mitigates the double marginalization between the two supply chain partners. More specifically, there are situations where both players prefer the subscription model, and there are other situations where they both prefer the selling-ownership model. This study bridges the literature gap between business models for digital goods and the impact of digital piracy. Our findings provide a possible explanation for the coexistence of various business models within digital goods markets, particularly when piracy is prevalent. Furthermore, we introduce actionable plans for practitioners such as providing incentives to retailers that may be apathetic to eradicating piracy and enhancing supplementary subscription-based services for better coordination.
To address climate change, firms that pursue carbon neutrality might adopt various CO 2 abatement technologies, including purchasing carbon credits; investing in third-party CO 2 capture and storage; or redesigning products, processes, buildings, and energy sources to abate CO 2 emissions. Existing research yields mixed evidence of the effects of such actions, especially technological choices, on shareholder value, though. Building on the Natural Resource-Based View, this article seeks to establish the relationship of different CO 2 abatement technologies with shareholder value, using multiple event studies of firm-level carbon neutrality announcements and technology-level CO 2 abatement announcements issued in the United States between 2014 and 2024. Of the various CO 2 prevention (insetting) technologies, product and production technologies effectively create shareholder value by establishing a difficult-to-imitate source of competitive differentiation, whereas investments in green transportation, renewable energy, and buildings exert neutral effects on shareholder value. Among the various CO 2 control (offsetting) technologies, investments in CO 2 capture and storage technologies destroy shareholder value. Although purchasing carbon credits has a neutral effect in general, they can destroy shareholder value when announced in isolation, unbundled from other abatement technologies. These nuanced findings indicate that CO 2 control technologies do not undermine shareholder value, as long as they offer an inexpensive means to adjust investments in offsets over time, to complement the insetting efforts. This research thus expands sustainable operations literature by clarifying the relationship between CO 2 abatement technologies and shareholder value, while also providing initial cues of some underlying mechanisms. These insights in turn can inform managers and policymakers seeking to decrease risk while also accelerating climate transitions.
We consider a joint admission and aggregate service rate control problem in a service system. Admission control involves deciding which arriving customers to admit and which to reject. Aggregate service rate control focuses on determining the staffing level and/or service rate for the system. We consider a general reward structure and a convex service cost structure to capture many variations that arise in practice. We show that, under specific structural properties of the revenue and cost functions, the joint optimization of admission and aggregate service rate decisions can be analyzed to produce interesting and implementable policies. Specifically, for systems with a linear service cost structure, we identify a critical operational threshold for the arrival rate. For arrival rates below this threshold, the optimal policy is to close the system and reject all customers. Above this threshold, the optimal policy is to admit all customers and choose an aggregate service rate that depends on the actual arrival rate. In contrast, for systems with a strictly convex service cost structure, under certain conditions, we identify two operational thresholds for the arrival rate. When the arrival rate is below the lower threshold, it is optimal to close the system. It is optimal to admit all customers and choose an arrival-rate-dependent aggregate service rate for arrival rates between the two thresholds. Above the higher threshold, the optimal admission rate and the optimal aggregate service rate do not change any further. We further demonstrate the value of joint optimization by comparing it with two natural benchmarks—optimizing admission rate alone and optimizing aggregate service alone. In stationary arrival settings, we show that joint optimization not only informs the critical decision of whether to operate but also shows that optimizing admission rate or aggregate service rate alone can lead to significant profit losses. We then extend the analysis to nonstationary arrivals with real-world call center data, which further demonstrates the practical value of joint optimization.
Nonprofit organizations (NPOs) rely heavily on fundraising activities to generate the resources necessary to fulfill their missions and sustain their programs and services. While fundraising is inherently labor-intensive, current research often overlooks the return on labor investment when evaluating performance. Furthermore, NPOs face a critical decision regarding the allocation of fundraising teams across diverse solicitation methods to generate revenue, yet existing literature presents conflicting evidence on the impact of revenue concentration and lacks context-specific recommendations. To address these gaps, we examine the comparative dynamics of revenue concentration versus diversification strategies, specifically focusing on when and how to implement them, with a goal of enhancing the organization's fundraising productivity, defined as fundraising revenue per full-time equivalent. Utilizing a three-year panel dataset from 105 food banks, we demonstrate that a revenue concentration strategy enhances fundraising productivity, and this effect can be amplified by prioritizing highly personalized solicitation methods. On the other hand, organizational age marginally and negatively moderates the positive impact of revenue concentration on productivity. Our post hoc analyses on different age segments indicate that an increased degree of revenue concentration can significantly enhance fundraising productivity for the youngest food banks, while having no statistically significant effect for the more mature ones. Through additional post-hoc analyses, we show that grant-writing and personal solicitation are the main drivers behind the positive moderating role of prioritizing highly personalized solicitation methods, and that increasing the proportion of full-time fundraisers could also amplify the positive impact of revenue concentration on productivity. We also observe a positive impact of revenue concentration on alternative performance metrics, such as fundraising effectiveness (measured as total fundraising revenue) and revenue growth (measured as the year-over-year rate of increase in total fundraising revenue). However, we find evidence on the negative impact of revenue concentration on financial stability (measured as the amount of deviation from predicted fundraising revenue). Consequently, NPOs must carefully tailor their revenue concentration strategies to align with their specific performance goals at different organizational ages while considering other operational factors, such as the nature of the adopted solicitation methods and labor mix.
Racial bias in pulse oximetry—a critical noninvasive diagnostic tool that estimates blood oxygen levels using light absorption—has been shown to systematically overestimate oxygen saturation in Black patients, potentially delaying the detection and treatment of hypoxemia. While prior work has focused narrowly on mortality within selected clinical populations or on immediate testing and treatment disparities in emergency department settings, this study investigates how pulse oximetry bias affects a broader and more representative intensive care unit (ICU) cohort, emphasizing highly consequential operational outcomes: supplemental oxygen delivery, within-hospitalization ICU readmission, and remaining hospital length of stay. Using patient-level data from the MIMIC-IV database, we apply a moderated mediation framework to trace the causal pathway from race to treatment to operational failure. We find that under peripheral oxygen saturation-only monitoring, Black patients are 5.2 percentage points less likely than White patients to receive supplemental oxygen. This undertreatment significantly increases the probability of an unplanned ICU readmission, with oxygen delivery reducing readmission risk by 4.8 percentage points, a 37% relative reduction. Furthermore, a separate operational load analysis using ordinary least squares regression indicates that such ICU bounce-backs are associated with an 86% increase in remaining hospital length of stay, pointing to a substantial downstream capacity penalty. To evaluate the mitigating effect of more accurate diagnostic information, we leverage arterial oxygen saturation (SaO 2 ) measurements as a quasi-intervention. We find that the racial disparity in oxygen therapy is substantially attenuated and becomes statistically indistinguishable from zero when SaO 2 readings are available, consistent with mitigation of the bias-treatment-outcome cascade. Our findings reveal how diagnostic inaccuracy is an upstream process defect that propagates through clinical decision-making, reinforcing structural disparities and degrading system performance. We contribute to healthcare operations management by modeling technological bias as a driver of costly clinical rework and demonstrating that targeted process changes—such as confirmatory SaO 2 testing—can improve healthcare equity and hospital efficiency.
Personalized treatment planning requires various patient-level considerations including personal risk factors and contraindications. However, existing algorithms for facilitating treatment planning frequently fail to account for uncertainties in their recommendations arising from the frequent updating of risk-scoring tools. We propose an algorithmic framework called Data-driven Augmentation of Treatment Planning via Clinical Role Model Generation and Selection (DreAMS). DreAMS integrates risk-scoring tools and data-driven optimization to augment treatment planning by identifying clinical role models, i.e., low-risk patients whose physiological measurements and medications can inform treatment planning for high-risk patients. The problem of optimally generating clinical role models amidst uncertainty in frequently updated risk-scoring tools can be tractably reformulated by leveraging two data sources: (i) a patient-specific database ensuring actionability and (ii) historical data from risk-scoring tools to mitigate risks of erroneously recommending high-risk role models. We develop greedy and active-learning algorithms to solve this problem and derive complexity bounds. We present a case study using multiple datasets containing patients at risk for atherosclerotic cardiovascular disease (ASCVD). DreAMS effectively augments treatment planning for high-risk patients despite frequent updating of ASCVD risk-scoring tools, selecting role models whose predicted ASCVD risk falls within acceptable levels in over 60% of high-risk patients and outperforming benchmarks by over 20%.
Decentralized applications (DApps)—digital applications operating on blockchain platforms—are transforming diverse industrial sectors. As blockchain platforms feature protocol-based governance, the mechanisms driving DApp adoption differ fundamentally from those in traditional digital environments. Drawing on observational learning theory, we conceptualize individual DApp adoption as decision interdependence driven by the visible actions of others. Building on platform governance theory, we identify three core governance-driven characteristics—decentralization, trustlessness, and token incentives—as key contextual moderators of this interdependence. We empirically investigate these dynamics on Ethereum, the world’s largest DApp platform, leveraging an unprecedented dataset covering its entire history through July 2025. Our findings confirm that decision interdependence is a significant driver of individual DApp adoption decisions, and that this effect is further amplified by blockchain’s unique governance characteristics: the effect of decision interdependence strengthens with higher decentralization and higher trustlessness, and is more pronounced for DApps offering token incentives. Extensive robustness checks, including DID and DDD analyses, alternative measures of blockchain characteristics, and accounting for copycat competition, reinforce these findings. By uncovering how blockchain’s distinctive governance mechanisms reshape decision interdependence, this study advances technology adoption theory beyond socially embedded settings and highlights how platform design fundamentally conditions user decision-making. Platform designers and DApp developers can leverage these insights to strategically calibrate blockchain governance features to amplify peer-driven adoption, advancing the operations management literature on blockchain-enabled digital platforms.
Firms commonly use audits to ensure their suppliers comply with social responsibility standards. Yet audits are imperfect: a type I error occurs when a compliant supplier is mistakenly flagged as non-compliant, and a type II error occurs when a non-compliant supplier is overlooked. We study an assembly network in which a firm sources inputs from two suppliers and designs an audit program under a limited budget. For each audited supplier, the firm determines the audit accuracy and the associated type I and type II error rates. Upon a failed audit, the firm decides whether to rectify the supplier or substitute it with a compliant one. We show that the firm’s audit strategy depends on the relative costs of substitution and rectification, as well as the allocated budget. When both mitigation costs are high or one of them is low, the firm always prioritizes addressing a specific type of error, regardless of the budget level. When both mitigation costs are moderate and substitution is cheaper than rectification, the budget level becomes pivotal. With a higher budget, the firm may prioritize reducing type I errors and substituting flagged suppliers to leverage cost advantages of substitution. With a tighter budget, the firm instead prioritizes reducing type II errors and relies on rectification to avoid potential reputational damage from type I errors. Interestingly, even in a symmetric network, the firm may allocate the budget unevenly: assigning more resources to one supplier to take advantage of low-cost substitution, while allocating less to the other and relying on rectification when needed. Surprisingly, as the budget increases, the firm may shift from auditing both suppliers to auditing only one, improving audit accuracy for the audited supplier while accepting a fixed expected loss from the unaudited one. Moreover, a larger budget may increase the probability that a non-compliant final product reaches the market, because the firm may switch from eliminating type II errors to controlling type I errors. The optimal audit program differs sharply from the one that ignores type I errors, which can result in inefficient budget allocation, misguided mitigation efforts, and avoidable reputational and economic losses. Our findings highlight the importance of jointly managing both types of audit errors and strategically adapting the audit scope, intensity, and corrective actions in socially responsible sourcing.
How do information technology (IT) and R&D investments jointly shape the global dispersion of multinational manufacturers’ operations and their production costs? We argue that IT enhances a firm’s ability to leverage R&D-induced technological competence across different regions, facilitating the geographic dispersion of internal supply chain units and subsidiaries. In addition, IT helps firms extract greater value from geographic dispersion in terms of reduced production costs via enhanced coordination, control, and connectivity. Using rare archival firm-level data on US-based multinational manufacturers for the 1994–2009 period, we test these arguments and document two major findings. First, IT intensity positively moderates the relationship between R&D intensity and geographic dispersion. Second, IT intensity negatively moderates the relationship between geographic dispersion and production costs. Together, these findings highlight the critical role of IT in amplifying the effect of R&D on geographic dispersion, and in turn, the effect of geographic dispersion on production cost advantages. Overall, the findings suggest that investments in IT systems and resources help mitigate coordination challenges in leveraging technological competence for global dispersion and managing dispersed global supply chains for superior firm performance.
Businesses selling in online marketplaces often enhance their offerings with short delivery times, but short delivery times typically result in low delivery-time conformance—the probability of on-time delivery, often published in the form of delivery-time ratings. As a result, short delivery times may simultaneously enhance and undermine the perceived value of online offerings. Marketplace operators can moderate this trade-off between delivery time and delivery-time conformance by making delivery-time ratings available or not. We (i) characterize the conditions for the emergence of the trade-off, (ii) identify the mechanisms that may allow managers to preempt the trade-off, and (iii) outline the conditions under which it is profitable for online retailers to solicit and publish delivery-time ratings. We model and compare alternative scenarios for the supply chain of a retailer and a supplier that offer a product in an online marketplace and determine prices and promised delivery times. We develop analytic models and support them with data provided by Cainiao’s logistic network. Differences of outcomes across scenarios reveal that the aforementioned trade-off can be preempted by simultaneously making delivery-time ratings available and coordinating the prices and promised delivery times. Disclosure of ratings can reduce delivery times but is profitable only if the disclosure increases system-wide value. Confirming this finding, analyses of Cainiao’s data indicate that the availability of delivery-time ratings can reduce the average promised delivery time by 3.9 h (or 7.4%). Several extensions show that our results hold for different contexts, including both drop-shipping and retailer-managed fulfillment models. In conclusion, our results suggest when and how supply chains in online marketplaces can use pricing to make the offering of shorter delivery times a dominant strategy.
This paper analyzes the academic evolution and educational progress of Operations and Supply Chain Management in China. The field has transformed over recent decades. It has transitioned from a knowledge-importing phase to indigenous innovation. We show that China has built a massive educational infrastructure and research system. This study proposes an innovative Institutional–Dynamic–Organizational (IDO) model to systematically evaluate this trajectory. Historical bibliometric data analysis from top-tier journals reveals a sharp shift in research momentum. Publications by China-based scholars in frontier domains grew dramatically. This academic evolution analytics offers profound contributions and actionable insights for Western economies and global supply chain systems.
This Part II paper analyzes the industrial transformation and policy governance of Operations and Supply Chain Management in China. Chinese supply chains are changing fast. The system is shifting from labor-based cost advantages to technology-driven value. Key markers include high-density automation, industrial artificial intelligence, and green operations. Concurrently, national policies serve as critical institutional drivers. This study highlights a distinctive state-led governance innovation known as the “Chain Chief System” (supply chain lead system [SCLS]). The SCLS mechanism combines government administrative coordination with lead enterprise leadership. We utilize an innovative Institutional-Dynamic-Organizational model developed in Part I paper to evaluate this triadic ecosystem. Finally, we provide a balanced assessment of operational performance trade-offs under embedded state governance. Our findings reveal how institutional buffering reduces systemic risk costs while introducing compliance burdens and affecting market agility. This study offers key insights for global managers navigating supply chain resilience and uncertain industrial environments.
This article presents a methodological approach that employs empirical analysis to investigate the adverse operational effects of congestion within a court system setting. Central to this study is the premise that prolonged lead time generates additional tasks, consequently increasing congestion by consuming additional processing resources, a phenomenon we term “the congestion vortex.” To explore this subject, we leverage a large-scale rich dataset from the Israeli court system which provides a comprehensive map of case-related judicial tasks in all civil cases in the system for more than 7 years. We also utilize two instrumental variables, exogenous shocks to the system, to mitigate potential reverse causality between judges’ effort and lead time. Our results indicate that the longer a case lingers in the system, the greater the judicial workload it creates, in terms of invested effort in producing judicial decisions. Furthermore, we demonstrate that extended lead time increases the likelihood of a case concluding with a judgment on its merits, further escalating judicial effort.
The surge in product returns poses significant financial and environmental challenges, particularly for seasonal goods, as retailers must often discard or liquidate returned items at the end of a short selling season. To address these challenges, we propose a tiered-refund policy integrated with return restoration . Under this policy, the retailer offers full refunds for early returns and only partial refunds for late returns, creating a supply of early returns that can be restored and resold as new. By employing a two-period model to capture the essential dynamic of early and late returns, and accounting for consumer valuation uncertainty and the disutility of returning early, we characterize the retailer’s optimal late-return refund, initial inventory level, and restoration quantities. We investigate the impact of the proposed tiered-refund policy with return restoration relative to two benchmarks: a restrictive short-return-window policy and a lenient full-refund policy. Our analytical results show that, even without restoration, the tiered-refund policy dominates the short-return-window policy, and outperforms the full-refund policy unless consumer valuations are low. When restoration is feasible, the tiered-refund policy becomes even more attractive compared to the full-refund policy for lower consumer valuations and strictly dominates if restoration costs are not prohibitively high. We also find that the operational flexibility created by restoration leads the retailer to lower the optimal late-return refund in order to incentivize early returns. Numerical experiments using practice-calibrated parameters show that the tiered-refund policy with restoration increases profits by an average of 11% relative to the short-return-window policy and 7% relative to the full-refund policy, with maximum gains reaching 38%. Furthermore, the policy reduces total environmental impact by an average of 1.5% and 5% against the respective benchmarks, with reductions of up to 13%. These sustainability gains are driven by converting otherwise discarded returns into usable products and by reducing initial inventory requirements through restoration. Finally, we find that tiered refunds and restoration are complementary in improving profitability.
