Sustainable development demands addressing two core challenges: mobilizing financial resources and aligning stakeholder incentives. This article surveys the operations-finance interface literature through the lens of “Mobilizing Resources” and “Aligning Incentives” framework. We highlight how the literature advances our knowledge of mitigating SME financing constraints and crafting operationally-informed financial contracts to internalize externalities. We identify a critical gap: while theoretical models for incentive alignment are well-established, empirical evidence remains limited due to the difficulty of analyzing unstructured data. To bridge this gap, we present large language models (LLMs) as a rigorous methodological toolkit for empirical operations management research. We outline a four-step framework—Problem Definition, Model Selection, Prompt Engineering, and Validation—and illustrate its application via a case study that extracts novel data on supplier finance programs from corporate 10-K filings. We conclude by proposing a unified research agenda to advance future research at the intersection of operations, finance, and sustainability.
This paper examines how assortment size influences consumer purchasing behavior and retailer profits, focusing on the paradox of choice (PoC), where overly limited or excessive options can diminish purchase intent. Although PoC effects are well-documented in behavioral research, their impact on assortment and pricing strategies in retail remains underexplored. To bridge this gap, we formalize PoC mathematically by modeling the utility of the no-purchase option as a U-shaped function of assortment size. We integrate this PoC framework into several discrete choice models, using the multinomial logit (MNL) model as the focal model. By decomposing the assortment problem into subproblems with fixed assortment sizes, we develop efficient solutions for the assortment optimization and the joint assortment and price optimization problems under the MNL with PoC model. Additionally, we compare the optimal solutions of the classical MNL model and the MNL model with the PoC effect through theoretical analysis and numerical experiments, offering managerial insights to guide firms in optimizing their assortments. We further extend our approach to derive optimal assortment and pricing solutions for the nested logit model employing a linear programming approach. Moreover, we devise a fully polynomial time approximation scheme for the assortment optimization under the mixture of MNL model with the PoC effect. This study advances the integration of behavioral insights into discrete choice models, illustrating how retailers can optimize product assortments and prices by balancing variety with cognitive effects linked to assortment size.
This article aims to study the impacts of disruption risk at different supplier tiers on the performance of both centralized and decentralized supply chains. We consider a three-tier supply chain, containing a tier-0 firm (original equipment manufacturer [OEM]), two potential tier-1 suppliers, and two potential tier-2 suppliers. Either a tier-1 or a tier-2 supplier is susceptible to disruption risk. We solve and analyze the optimal/equilibrium sourcing strategy and production quantities and compare the resulting supply chain network structures and the profits. Our results show that without fixed sourcing cost (and so each firm adopts dual sourcing and the resulting supply chain network is complete), the centralized supply chain suffers a greater profit loss when facing disruption risk at tier-1 suppliers than at tier-2 suppliers. Interestingly, this result reverses in the decentralized supply chain if the disruption risk is high. When fixed sourcing costs are present, the centralized system is less inclined to source from an unreliable tier-1 firm than from an unreliable tier-2 firm. In the decentralized supply chain, with the increase of fixed sourcing cost, the OEM’s sourcing strategy changes from dual sourcing to single sourcing when a tier-1 supplier is unreliable; surprisingly, the OEM may switch from single sourcing back to dual sourcing when a tier-2 supplier is unreliable because of the change in tier-1 suppliers’ sourcing strategy. Our results offer some guidance for firms on their risk mitigation strategies in their supply chains and provide insights into the impact of disruption risk on the supply chain structure.
Combined heat and power (CHP) technology produces both heat and electricity from a single-fuel input, achieving an efficiency (total useful energy output divided by fuel input) as high as 90%. However, using CHP exposes firms to a higher fuel price uncertainty, compared to purchasing electricity at a relatively stable price from the utility and generating heat separately. In this paper, we study the problem of optimizing the design (including capacity and power-to-heat ratio) and operations of a CHP system for an industrial firm facing variable fuel and electricity prices. A standard practice is to design a CHP system to match the thermal demand it serves and retire the legacy boiler, ensuring high energy efficiency. We revisit this standard practice by optimizing the firm’s energy supply system, including CHP design, the decision to retire or retain the legacy boiler, and the joint operation of the CHP system and boiler when the boiler is retained. We formulate the problem as a bilevel optimization, in which CHP design and boiler retirement-or-retention decisions are made at the beginning of a planning horizon, while system operations are optimized in each period. We identify two strategies for mitigating fuel price variability and improving cost efficiency: (1) Retaining the legacy boiler and operating it jointly with the CHP system, and (2) designing the CHP system with excess capacity that may overproduce steam. Both strategies introduce operational flexibility, enabling the energy supply system to switch operating modes in response to fuel price fluctuations. We further identify the market conditions under which each strategy dominates.
Background: Education, like other service operations, depends on the quality of its delivery channel. Broadband has become a critical delivery channel for learning in the modern education system, but access and speed remain uneven across U.S. communities. Aim: Scholars disagree on how broadband access affects educational outcomes, and prior work often overlooks broadband speed. We extend prior work by assessing how both broadband access and speed together influence U.S. math proficiency. Methods: We analyze a national panel of U.S. school districts from 2017 to 2022 and link broadband measures to student proficiency with a dynamic panel model. Results: School districts with greater average access and smaller within-district gaps tend to have higher math proficiency. Nationally, a one percentage-point increase in broadband access corresponds to a 0.545 percentage-point increase in math proficiency, a 1.22% increase relative to the average baseline. We find speed also matters: gains rise with faster available connections, then reverse at very high speeds. Conclusion: Access alone is not enough. Our study shows that broadband policy should move beyond “more is better” and consider broadband speeds that maximize learning. As one headmaster noted: “The internet is essential, but proceed with caution!”
We investigate the incentive for a retailer to share private demand information with two rebate-offering manufacturers who sell substitutable products through the retailer. We show that the retailer’s incentive to share information depends on the proportion of rebate-sensitive consumers, the competition intensity, and whether the retailer can charge a side payment for sharing the information. When the retailer cannot charge a side payment, we show that he will not voluntarily share information with a monopolistic manufacturer, but he may do so with none, one or both of the manufacturers when there is competition. Interestingly, we find that more intense competition or a smaller proportion of rebate-sensitive consumers may benefit a manufacturer if it induces the retailer to share information with her. When the retailer can charge a side payment, we consider the two cases when he either contracts concurrently or sequentially with the manufacturers for sharing the information. We show that the retailer always prefers concurrent contracting, which induces the system-optimal information sharing decision, over sequential contracting.
Driven by advances in machine learning (ML), smart products improve over time through data-driven insights as ongoing user interactions generate usage data that enable the training, evaluation, and refinement of underlying algorithms. However, when firms implement strategies to collect more data to enhance product quality and profits, they must also consider strategic consumer behavior that may lead to unintended negative consequences. Specifically, consumers may intentionally postpone purchases in the early stages of a product’s development, anticipating future quality improvements and price reductions, which in turn complicates data collection during this critical period. Considering advancements in disruptive technologies, this study examines the critical yet underexplored economic impact of ML on pricing strategies. Few studies have focused on how ML influences profit maximization in the presence of strategic consumers. To address this gap, we develop two-period game-theoretic models that employ two dynamic pricing strategies, responsive versus preannounced pricing, to investigate how firms developing smart products adapt to the disruptive impact of ML, considering the behavior of strategic consumers. Our study provides several significant implications. First, we find that ML impacts firms’ profits by impacting consumers’ strategic behaviors in opposite directions. Second, under both dynamic pricing strategies, prices may initially be low and may either rise or decline over time. Third, we demonstrate that, different from findings in the existing literature on strategic consumer behavior, preannounced pricing policies are generally not optimal for the firm when its ability to leverage ML is relatively limited, and consumers are less strategic. Overall, this study makes three contributions to the literature. First, we clarify the impact of ML on a smart product firm’s profit. We find two effects in the application of ML: (1) a positive effect associated with ML (the “ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>M</mml:mi> <mml:msup> <mml:mi>L</mml:mi> <mml:mo>+</mml:mo> </mml:msup> </mml:math> effect”) and (2) a negative effect (the “ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>M</mml:mi> <mml:msup> <mml:mi>L</mml:mi> <mml:mo>−</mml:mo> </mml:msup> </mml:math> effect”). Second, this study highlights a fundamental economic mechanism for smart products in the presence of strategic consumers. Finally, it provides a decision-making tool for smart product firms to select an optimal dynamic pricing strategy.
Natural disasters disrupt retail operations by simultaneously triggering demand surges and supply interruptions. Restricted access to affected areas raises transportation costs and complicates replenishment, while stores that remain open experience increased demand from displaced consumers and stockpiling behavior. Ensuring product availability becomes critical, forcing retailers to rely on suppliers who can deliver under constrained conditions. We examine whether supplier proximity mitigates these operational disruptions by comparing the sales evolution of products manufactured near each store (“nearby”) to those produced farther away (“distant”). We combine weekly retail scanner data, manufacturing location information, and FEMA disaster declarations for three North Atlantic hurricanes. Using a triple-difference design, we compare changes in sales of products from nearby versus distant suppliers before and after the event, across affected and unaffected stores. Across the three hurricanes, sales of products from nearby suppliers in operational stores located in affected areas increase by 7%–11% relative to products from distant suppliers. Moreover, we find that stockouts of products from nearby suppliers are less likely to occur in the aftermath of the disaster. Furthermore, we find that in two of the three events, products from nearby suppliers experience a price reduction compared to those from distant suppliers. These results show that supplier proximity is an actionable resilience lever: nearby suppliers could be more likely to deliver to affected areas when replenishment is challenging, enabling retailers to maintain product availability and continue serving customers. The findings offer guidance for preparedness planning, emphasizing the value of geographically diversified sourcing and coordination with nearby suppliers.
Effective utilization of disaster warning signals is crucial for mitigating impacts and enhancing operational resilience in disaster management. Although an emerging area of scholarly interest, the literature remains fragmented and lacks a unifying framework to guide research and practice. To consolidate knowledge and direct future research, this study conducts a systematic review of the literature on disaster warning signal research published in leading journals in operations management, management science, operations research, and related supply chain and logistics. Building upon the classic communication model proposed by Shannon, this study develops a conceptual framework for systematically classifying the relevant literature according to three dimensions of a warning signal: (1) signal type (natural, engineering, behavioral, informational, composite), (2) signal transmission (source, channel, receiver), and (3) signal purpose (directing the response of the authority, guiding the protection of the public). We then cross-tabulate this framework with the disaster management domain (e.g., disaster phase, type, and function) and the data domain (e.g., data type, analytics techniques). By synthesizing academic contributions with practical challenges, we articulate the specific value that operations management research on warning signals offers to disaster management practice. Finally, we propose a structured agenda for future research focused on the intersections of signal, disaster, and data domains.
In today’s fast-paced digital world, consumers demand instant access to online content and are intolerant of delays, making website speed a key competitive advantage in attracting web traffic. Google’s Speed Update and Core Web Vitals have further emphasized the significance of website speed in web traffic competition. This study examines how firms strategically compete for web traffic by managing website speed, focusing specifically on two distinct strategies: response-based and capacity-based. Under response-based competition, firms first set their desired website speed (or equivalently, website response time), subsequently determining the necessary website capacity. In contrast, in capacity-based competition, firms initially select the website capacity level, which in turn determines the website response time. We analyze a duopoly scenario in which two firms compete for web traffic. Although website speed and capacity are functionally related, surprisingly, firms sometimes compete more aggressively under response-based competition. Interestingly, the aggression of response-based competition can sometimes increase firms’ profits. We also show that when firms freely choose the decision process, firms sometimes engage in a mode of competition in equilibrium, which yields a lower profit for the capacity provider (e.g., computing capacity provider) than the alternative mode. We further show how the cloud provider can increase profit by strategically inducing firms to engage in a preferred mode of competition. This is achieved by lowering the unit price of renting capacity related to that mode of competition. This strategic price reduction can lead to faster websites for consumers, an increase in the provider’s revenue, and consequently an increase in the cloud provider’s profit under a cost-efficiency condition. The profit of firms can sometimes increase too, implying a win-win-win for all the parties, namely, firms, consumers, and the provider.