Knowledge that Transforms

Theoretical and empirical evidence point to the ability of political leaders to manipulate economic policies and leverage local firms to elevate their political careers. Despite this, there is limited understanding of how these career incentives impact the operational dynamics of the firms involved. This empirical study delves into this gap, revealing that city leaders with fewer promotional incentives are more inclined to mobilize state-owned enterprises (SOEs) within their jurisdiction to pursue sustainable development, as indicated by heightened corporate operational efficiency. Our analysis further indicates that the career prospects of city leaders significantly influence the operational efficiency of SOEs by driving a shift in focus from rapid growth to sustainable development and firms’ adoption of disruptive technologies. We posit that this increase in operational efficiency not only benefits the SOEs but also generates unique value for stakeholders, resulting in elevated market capitalization and reduced stock price crash risk. Our findings carry direct relevance to the ongoing discourse on political incentives and contribute to operations management research, shedding light on the intricate ways in which the political environment can impact the operational performance of firms.

Consumers often rely on their social connections or social technologies, such as (automated) system-generated recommender systems, to navigate the proliferation of diverse products and services offered in online and offline markets and cope with the corresponding choice overload. In this study, we investigate the relationship between the consumers’ social connectedness and the economic impact of recommender systems. Specifically, we examine whether the social connectedness levels of consumers moderate the effectiveness of online recommendations toward increasing product demand levels. We study this novel research question using a combination of datasets and a demand-estimation model. Interestingly, the empirical results show a positive moderating effect of social connectedness on the demand effect of online-to-offline recommendations. Further delving into the findings, we also provide empirical evidence that social identification might explain why denser social connectedness with local users accentuates the effects of collaborative-filtering online-to-offline recommendations. Our study enhances the understanding of community factors affecting the efficacy of social technologies in multichannel operations while also extending the social identity theory in operations in the digital realm. The results also have intriguing operational implications for operations managers and practitioners, while suggesting several interesting avenues for future research on social technologies and operations management.

Pricing represents a crucial element in the platform business model search, particularly for startups that face the “cold-start” problem in launching a two-sided marketplace. In a static setting, the literature recommends the “seesaw principle” (i.e., charging a relatively low price, even subsidizing, on the one side and a high price on the other) as the solution. However, little is known about whether the seesaw principle still works in a dynamic setting and how it is influenced by intertemporal factors such as retention capabilities and early period financial pressure. This paper develops a game-theoretic model to examine optimal pricing strategies in a multiperiod setting. We first show that, in the symmetric scenario, the adjustment to the seesaw principle (i.e., the difference between the single- and multiperiod price gaps) increases with the platforms’ retention rate. This insight holds true in the asymmetric case where a startup platform faces greater early period financial pressure than an established competitor. Interestingly, the startup platform should charge a higher price to the less profitable side to differentiate itself and avoid a price war. We also explore the robustness of these insights by considering an endogenous, price-sensitive retention rate.

Critical infrastructures in many countries face the problem of aging and, thus, require significant upgrades to continue serving their purpose for the next few decades, especially in the face of extreme weather events caused by global climate change. Given the urgent need for such improvements and the substantial funding gaps being experienced, prioritizing investments in critical infrastructures is a challenging task for governments. Furthermore, the need to assure equitable solutions, as well as to consider deep uncertainty due to climate change, adds to the complexity of the problem. We seek to address this complexity by developing a set of models that explicitly consider both horizontal and vertical equity, along with efficiency, in prioritizing stormwater infrastructure improvement projects. While horizontal equity seeks to provide equal resources to everyone, vertical equity aims to allocate relatively more resources to vulnerable groups who are disproportionately susceptible to shocks and are more likely to fall into chronic poverty. By differentiating between losses in horizontal equity and vertical equity due to efficiency considerations, the models provide a practical approach to find the right balance among efficiency, horizontal equity, and vertical equity. The initial models are then extended into regret-based optimization models to help address the issue of deep uncertainty. A case study of stormwater infrastructure improvement in the City of Miami is presented, through which the performance of the models is explored both with and without the projected sea-level rise scenarios. The findings highlight the value of the proposed approach in promoting equity while maintaining efficiency.

The persistent inequities in American cities—long recognized and lived by black and Hispanic people and other minorities, the poor and working class, and others disadvantaged by urban systems—have been vaulted into the broader public consciousness over the past decade. Lessening entrenched urban inequalities is now at the top of the national policy agenda, suggesting a need and opportunity for more urban operations management. On what issues and how might this work occur? Operations management was deeply intertwined with urban planning in research and practice from the 1950s through the 1970s, at which point the fields diverged. To build a case for what perspectives and approaches a modern urban operations management agenda might employ to address inequity, I synthesize historical and contemporary planning theory with the debates among reflective operations scholars in the 1950s-1970s over work on cities. Modern operations scholars can look to planning, and especially to recent major shifts in its thinking on race and class, to address urban operations that disadvantage some city residents and overly advantage others. This urban operations agenda should be empirical, equity-oriented, and community-focused in order to best resonate with planners and the city residents they serve. In reengaging with planners to tackle the modern range of urban policy problems, operations analysts have a chance to contribute practical clarity on how cities work and can be made more livable for all residents.

Fashion sellers are sometimes critiqued for selling low durability products, resulting in waste. Blame is also directed at consumers, who purchase new fashions despite having accumulated a closet full of prior fashions. The “slow fashion” movement encourages sellers to produce higher durable products, thus supporting less frequent purchases by consumers. Using an infinite-time model and considering strategic consumer behavior, including their ability to accumulate a “closet” of varieties over time, we analyze the seller’s profit-maximizing price and product-durability decisions. We initially assume a static price but later analyze the potential profit gains from dynamic pricing. When analyzing a heterogeneous consumer market, we initially allow customers to vary (distributed uniformly) in their sensitivity to fashion. Subsequently, we explore alternative distributions for consumers’ fashion sensitivity and the correlation between their fashion sensitivities and product valuations. Using this framework, we show how the seller’s optimal price and durability decisions yield distinct shopping segments, which we refer to as the minimalist versus trend-chasers. We find that if the degree of fashion uncertainty is moderate, the seller’s optimal choice of product durability will support the emergence of both behaviors. As the variety uncertainty expands, if the seller’s costs are sufficiently low, it will support a throwaway culture via disposable products. Otherwise, given high costs, the seller optimally targets a slow fashion-type outcome, with consumers targeting reuse (with durability) rather than variety. Our findings shed light on consumers’ optimal purchasing behaviors in relation to both market parameters and the firm’s pricing and durability decisions.

This paper studies how capacity collaboration can benefit two competing firms. We consider a two-stage model where capacity decisions are made in the first stage when there are significant uncertainties about market conditions, and then production decisions are made in the second stage after most of these uncertainties are resolved. We vary the degree of collaboration between the two firms in their capacity and production decisions, examining multiple models and comparing the outcomes. We find that a firm can benefit from collaboration even with its competitors. Interestingly, the firms do not have to make production decisions jointly to realize the benefits of collaboration. Additionally, while collaborative capacity investment proves beneficial, collaborating on production with existing capacity can often yield greater benefits. We find that the advantages of collaboration are most pronounced when competition intensifies, demand fluctuates significantly, and investment costs are high.

We investigate how language, an essential part of culture, affects manufacturing firms’ supply chain operations management practices, including the cash conversion cycle and its components. Based on the Sapir–Whorf hypothesis, which theorizes that a language's structure may affect how its speakers think, prior studies have established that using the future tense to describe future events increases one's mental distance from the future, reducing a person's concern about it. Building upon this foundation, we hypothesize that firms in weak future-time reference countries are likely to be better prepared for future volatility in demand for their products and therefore carry higher inventory to avoid potential stockouts. We also hypothesize that firms in weak future-time reference countries are more apprehensive about long-term relationships with their customers and hence extend longer credit terms to them. Finally, we hypothesize that firms in weak future-time reference countries have longer operating and cash conversion cycles due to carrying higher levels of inventory and extending longer credit terms to customers. The empirical results using a large global sample of 193,625 firm-year observations from 45 countries support our hypotheses. In terms of economic significance, on average, the cash conversion cycle of firms in weak future-time reference countries is ∼ 11% longer than that of firms in strong future-time reference countries. We also find that the effect of language is dominant over the influence of traditional cultural dimensions. Together, the results suggest that time encoding in the language of a firm is a determining factor in its supply chain operations.

Multi-party carpooling emerges as a burgeoning shared transportation scheme whereby the trip shared by each driver is shared among multi-party riders whose itineraries coincide. Confronting the information asymmetry and the voluntary self-interested nature of bilateral participants in matching and pricing operations, this study designs Multi-party cArpooling SupporTed stratEgy-pRoof (MASTER) double auction mechanisms considering personalized carpooling constraints. First, in a scheduled carpooling scenario, two [Formula: see text] mechanisms that masterfully blend the ideas of the famed trade reduction method and multi-stage approach are proposed which implement distinct group bid determination approaches for responding to different market conditions. Second, in an on-demand carpooling scenario, two parameterized [Formula: see text] mechanisms that integrate frustration-based promotion to proactively prioritize matching and deferentially compensate riders based on their waits are contrived which also endow the platform with operational flexibility to agilely pursue alterable operational objectives by adjusting promotion strength. We prove theoretically that the proposed mechanisms satisfy strategy proofness, budget balance, individual rationality, and asymptotic efficiency under mild conditions. Experimental results reveal that multi-party carpooling constitutes a multi-win solution under higher rider-driver ratios whilst it could be detrimental to drivers otherwise, which can be ameliorated by favoring the driver side in determining promotion strength. Simulation studies manifest that our proposed auction mechanisms could bring benefits concerning allocation efficiency and service responsiveness compared with their academic and practical counterparts. We also shed light on choosing among alternative mechanisms according to market conditions and operational orientations.

Prolonged emergency department (ED) length of stay (LOS) is associated with detrimental effects on patient care quality and outcomes. There is evidence that certain groups of patients experience longer LOS based on their gender or race, especially with regard to the part of LOS that is attributable to waiting to be seen by a clinician. This work tackles the patient prioritization and placement aspects of ED operations with the goal of improving throughput and wait time in a fair, equitable way. We present a novel Mixed Integer Linear Programming (MILP) predictive-prescriptive formulation that incorporates a breakdown of predicted patient ED LOS into actionable pieces. We incorporate considerations for fairness and reformulate the MILP formulation into a compact and computationally tractable formulation that can be solved efficiently in real time. To deal with uncertainty, we propose a sampling-based solution, and provide provable guarantees regarding its convergence, stability and sample complexity. The proposed solution increases the throughput of the ED by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>50</mml:mn> <mml:mo>−</mml:mo> <mml:mn>100</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> and decreases the average wait time by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>50</mml:mn> <mml:mo>−</mml:mo> <mml:mn>75</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> compared to current hospital practice. In addition, the method is near-optimal in terms of throughput, and produces high-quality solutions in terms of average wait time compared to a clairvoyant oracle. Our proposed approach demonstrates desirable properties when it comes to fairness in patient prioritization, illustrating a path for addressing hidden biases in patient ED wait times and hospital operations as a whole. This work was conducted in collaboration with a large US academic medical center. Data from more than 40,000 patient visits were used to shape and evaluate the predictive-prescriptive models. An important practical contribution is translating a complex algorithm’s output into recommendations that can be operationalized in the context of existing processes in the ED. Specifically, we develop an interpretable metamodel that is trained to mimic the predictive-prescriptive algorithm’s decisions and provides a transparent set of rules for patient placement. The method will be used by the hospital to improve patient flow and quality of care as well as to support more fair and consistent bed allocation decisions.