A fundamental principle in operations management holds that increasing the number of servers reduces delays in service systems. To date, no mechanism has been identified that could reverse this effect. We propose, however, that risk aversion can cause increased service capacity to intensify congestion. We study an unobservable M/M/s queue where risk-averse customers choose whether to join based on their anticipated waiting time. We show that, in equilibrium, demand, expected waiting time, expected sojourn time, and the probability of waiting all increase with the number of servers, and that these effects are stronger for more risk-averse customers. We further uncover the mechanism behind this phenomenon: adding servers makes delays less risky (in the sense of second-order stochastic dominance), which increases the sensitivity of demand to capacity as customers become more risk-averse. These patterns are more prevalent in small systems and fade as the system grows. They can also persist when customers differ in their degree of risk aversion, when capacity is increased by raising service speed, and when the system is observable. Our findings reveal a novel trade-off created by customer risk aversion: expanding capacity attracts more customers, but also exacerbates congestion. A manager aiming to reduce waiting times may therefore prefer to de-pool service capacity instead of following the standard approach of pooling, while increasing capacity in the resulting smaller systems to preserve total throughput. When the objective is to maximize profitability, our results further suggest that the cost of additional servers may be offset by the associated increase in revenue when customers are sufficiently risk-averse.

We study how bug bounty programs shape software vendors’ security and release choices. Vendors invest in internal assurance before release to reduce residual vulnerabilities, and after launch they must manage vulnerability discovery, disclosure, and remediation. We develop a game-theoretic model in which a vendor chooses release timing and severity-contingent bounties, anticipating effort by ethical and malicious hackers in a winner-take-all discovery race. The model highlights two linked mechanisms: an incentive channel that shifts first discovery of severe vulnerabilities away from malicious exploitation and toward ethical reporting, and a governance channel in which coordinated disclosure changes how vulnerability information is managed while remediation is underway. We derive closed-form optimal bounties and characterize a feasibility region that sustains positive bounties and interior success probabilities. Within this region, a bug bounty program strictly increases the vendor’s expected profit by reallocating first-discovery probability on severe vulnerabilities from malicious to ethical hackers and by converting part of severe-loss exposure into bounded, pay-for-results expenditures. For private programs, we also solve for the optimal invited set of ethical hackers and show that this optimal set is strictly smaller than the expected number of malicious attackers. Higher bounties raise ethical hackers’ effort and first-discovery probabilities but also increase program cost, and they interact with reputational (non-monetary) incentives. Finally, in the baseline model, BBP adoption conditionally reduces the marginal value of additional pre-release delay and therefore conditionally implies earlier release relative to the no-BBP benchmark. This timing result is a within-model conditional implication; its practical relevance depends on operational readiness, triage throughput, and the vendor’s ability to validate and safely deploy fixes once a valid report arrives. Managerially, BBPs should be viewed as a post-release governance layer that complements strong internal assurance rather than as a substitute for it. Policymakers can support responsible use of BBPs by encouraging timely remediation, transparent post-patch disclosure, and reporting standards that reduce information asymmetry and triage frictions.

Effectively 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 lacking 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.

We consider an assortment optimization problem for a class of online video games where the in-game virtual store has a unique structure with two sections: Featured and Just For You (JFY). All customers (players) are offered the same Featured section assortment, whereas the JFY section is used for personalized recommendations. We model customer choice under a constrained mixture-of-nested-logit model and propose different solution methods for the resulting assortment optimization problems. First, we introduce a novel mixed-integer nonlinear programming (MINLP) formulation. Numerical experiments show that the MINLP formulation generally obtains optimal solutions efficiently, using a variety of instances derived from conversations with our industry partner to mimic the environment found in their video game stores. In addition, we propose three approximate solution methods with theoretical performance guarantees: a fully polynomial time approximation scheme (FPTAS), a mixed-integer linear programming (MILP) formulation, and a heuristic algorithm. To understand the impact of a shared Featured section, we analyze the distribution of display capacity between the Featured and JFY sections. Our numerical experiments highlight that the Featured section plays a critical role in balancing revenue and customer utility. To validate our use of a mixture-of-nested-logit model, we further conduct a simulation study based on ground-truth instances that are independent of the underlying structure of the consumer choice models we consider. The results indicate that our nested structure yields superior performance in terms of both capturing customer behavior and simulation revenue, compared with the mixture-of-MNL model and the current practice of our industry partner. Overall, our paper is the first to study assortment optimization for the gaming industry under discrete choice models; it is also the first to devise both exact and approximate solution approaches for the constrained mixture-of-nested-logit model. Our results provide guidance for effective management of assortments in online video game stores and offer an “assortment” of solution approaches, allowing practitioners to choose one that best suits their environment.

Religious beliefs have often served as a lens through which communities interpret and cope with disasters. Not surprisingly, many non-governmental organizations (NGOs) that support disaster relief have roots in religious traditions. The religious orientation of an NGO is a key concern as it can influence volunteer outcomes and operational performance. This is important because volunteers are an indispensable asset for NGOs, playing a pivotal role in the efficacy of humanitarian aid efforts. This study employs social capital and person-organization fit theories to examine how NGO religiousness influences social capital, volunteer behaviors, and operational performance. It also analyzes how NGO and volunteer religiousness “fit” affects these relationships. The hypotheses were tested using two scenario-based video experiments: Experiment 1, which collected data from 100 students in a laboratory setting, and Experiment 2, which involved 198 online volunteers. Results from Tobit and Poisson regressions indicate that increased NGO religiousness may diminish volunteer social capital, commitment, and operational performance. However, NGO and volunteer religiousness “fit” mitigates the adverse effects of NGO religiousness, enhancing volunteer behaviors. A large-scale survey of 503 respondents supports these findings and provides insights to guide future research into volunteer motivations. This study contributes to the Humanitarian Operations Management literature and informs the strategies of NGOs regarding religious alignments, volunteer recruitment and retention, and operational performance.

Third-party distribution channels or intermediaries have become ubiquitous across a wide range of industries, offering firms access to new prospects and an opportunity to expand their customer base. Although prior work has investigated the short-term aggregate demand implications of intermediaries, the long-term customer relationship perspective has remained unexplored. Using customer-level data from a large U.S. hotel brand, we show that customers acquired via travel intermediaries (such as online travel agents or OTAs) have persistently different behaviors on several dimensions that matter for long-term value. Compared to customers acquired through brand-owned (direct) channels, intermediary-acquired customers spend 4.1% less per stay and purchase 3.8% less frequently. Intermediary-acquired customers, while displaying stronger channel inertia and lower multichannel engagement, purchase across a wider variety of brands (multibrand behavior). When we combine these behavioral estimates into a customer lifetime value (CLV) computation, we find that while intermediary-acquired customers have positive CLV, their CLV is 19.94% lower than customers acquired through brand-owned channels, revealing, for the first time, the long-term implications of such customer acquisition strategies. Through an optimal allocation model we show that, although the CLVs are lower for intermediaries, a firm maximizing customer value typically invests in both channels: the optimal share allocated to intermediaries rising proportionally with the intermediary’s acquisition efficiency and accessible prospect pool, and falling when the hotel is operating at capacity. In sum, our results show that although using intermediaries may be a viable strategy for customer acquisition, the purchase behaviors of these customers are significantly and meaningfully different from customers acquired via brand-owned channels, thus urging managers to adopt a more nuanced ‘frenemies’ approach to building a channel portfolio.

Health information exchanges (HIEs) facilitate the secure, electronic sharing of patient medical information/records across providers, enabling healthcare professionals to access timely, comprehensive data and thereby improve care coordination and quality. Yet, despite these expected benefits, empirical evidence shows that healthcare professionals often spend substantial time and effort interacting with HIE platforms without discernible productivity gains. Moreover, although HIEs are promoted as a potential solution for addressing geospatial disparities in healthcare, their impact on healthcare professionals’ productivity across urban and rural hospitals remains unclear. Using data envelopment analysis (DEA) to construct a measure of healthcare professionals’ productivity and applying the difference-in-differences (DiD) approach, we investigate the impact of HIE adoption on healthcare professionals’ productivity in urban and rural hospitals in the United States (U.S.). Our findings show that hospitals that have adopted HIE experience a significant increase in healthcare professionals’ productivity. However, this effect is more pronounced in urban hospitals than in rural hospitals. We attribute this result to urban healthcare professionals having workflows with greater information intensity and higher technology capabilities than healthcare professionals in rural hospitals. Furthermore, our study reveals that HIE adoption improves communication and the quality of clinical decision-making among urban healthcare professionals, but not among those in rural hospitals. We also find that the productivity gains from HIE adoption are greater for nurses than for physicians. We discuss the theoretical and practical implications of these findings.

Specialized investment platforms significantly influence financial service operations by enhancing collective intelligence and uncovering trading opportunities. However, the large volume of data, particularly the prevalence of noise, hinders effective decision-making. Opinion leaders constitute a distinct and influential group of users on these platforms, and the question of whether their presence can help financial institutions make better decisions remains open. To address this key operational question, we focus on two indicators in investment platforms: investor disagreement and prediction accuracy. Our findings reveal that the presence of opinion leaders leads to reduced investor disagreement and increased accuracy in predicting future stock returns. This effect is more pronounced when opinion leaders participate earlier in a discussion or contribute posts that are more innovative, longer, or express vivid opinions. Our subsequent experiment further reveals the psychological and cognitive mechanisms behind these findings: the presence of opinion leaders enhances investors’ cognitive abilities by providing them with more information and boosts their confidence in the information they acquire. These results contribute to information cascade theory by demonstrating how varied communication patterns result in different qualities of crowd decision-making. Additionally, we contribute to the operations management literature by highlighting the crucial role of opinion leaders in enhancing both financial services and social media operations. We suggest that financial institutions develop trading strategies based on opinion leaders’ posts and that platforms tailor features to leverage opinion leaders’ participation.

Recently, leading manufacturers have started adopting information and communication technologies (ICTs) to assist operators in capturing assembly operational errors. These manufacturers choose to develop internal ICTs and decide whether to implement proactive experiential learning to continuously improve the rate of recognizing assembly operational errors, or acquire external ICTs from third-party technology providers that increase the rate of recognizing assembly operational errors but fail to achieve experiential learning. These two ICT adoption models may cause different effects on assembly operations, which subsequently affects manufacturers’ profitability. However, there is a limited body of relevant research that focuses on this topic. Hence, our study aims to bridge this gap by examining a manufacturer’s decisions for the ICT adoption, internal experiential learning, and subsequent pricing strategy. First, we reveal that it is sometimes beneficial for the manufacturer to forgo ICTs rather than adopt ICTs. This is because the lower recognition capability leads to a weaker scale effect, reducing the positive effect of ICTs on operational error losses. Next, we find that even if the internal learning effect is stronger, the manufacturer may still choose to acquire external ICTs. Moreover, as the operational error loss increases, the manufacturer or third-party technology provider may sometimes invest less in ICTs. Further, our results show that the ICT adoption benefits consumer surplus and social welfare, and external ICTs sometimes generate more social welfare but less consumer surplus than internal ICTs. Additionally, we find that the ICT adoption changes the relationship between the assembly operational accuracy and product demand. Following the ICT adoption, our study offers invaluable insights to managers on how ICTs can yield better assembly operational outcomes.