Academic Publications

EXPRESS: Prosocial Project Management in Conflict Areas

2026-02-10T00:00:00+00:00

Production and Operations Management
Armed conflicts, terrorism, and political instability disrupt the implementation of thousands of prosocial projects in the developing world. In this unstable, fast-paced environment, managers may need to change course and adapt projects to new circumstances. Couldadaptive managementbe a solution? Leveraging a groundbreaking pilot run by the World Bank, we evaluate the performance of all 429 adaptive projects implemented between 1998 and 2013, when the Bank launched the “adaptable program loan.” Our study—covering 91 countries, 25 development sectors, and approximately $55 billion in funding resources—examines how armed conflict and adaptability influence project performance. We find that adaptability, reinforced by experience and learning, counteracts conflict’s negative effect on project performance. However, this moderating effect diminishes as project complexity increases and reverses in non-conflict areas as costs escalate and outcomes fall below expectations.

EXPRESS: Substitution or Emergency Order? Averting O-Negative Blood Shortages

2026-02-10T00:00:00+00:00

Production and Operations Management
Blood type substitution is an effective policy for improving the blood supply chain resilience in responding to shortages at hospitals. However, while compatibility of certain blood types provides an opportunity to better manage volatility in supply (donation) and demand (transfusion), in practice, it remains challenging to decide whether to use a compatible blood unit from the on-hand inventory (substitution) or to place an urgent or emergency order. Available evidence indicates that, in Australia, current practices for deciding on blood type substitution over emergency orders result in a significant imbalance in supply and demand for the most widely compatible units: O-negative blood units. This challenge is particularly critical in blood inventory management and represents a significant research opportunity for the operations management (OM) community. It can be addressed using state-of-the-art methodologies in data-driven decision making and perishable inventory under stochastic supply and demand. This study proposes a stochastic optimization model based on sample-average approximation (SAA) to aid the blood-type substitution decisions at hospitals. We take into account the historical demand data, the type and age of blood units in the inventory, as well as the shelf-life variability of the units arriving at hospitals. We compare the SAA results with several policies, including a single-period myopic model, no-substitution, over-order practice, and an empirical guideline. Results indicate that the proposed SAA model outperforms all the above policies. It thus creates significant opportunities for improvement in the management of blood units, as it reduces the imbalance in the supply and demand of O-negative blood units, allowing practitioners to benefit from substitution while avoiding the over-ordering of O-negative units.

EXPRESS: Exploring the Divide between Retail Apps and Light Apps—Insights and Implications

2026-02-10T00:00:00+00:00

Production and Operations Management
The proliferation of mobile commerce channels has fundamentally reshaped retail ecosystems, particularly in digital markets like China where smartphone adoption approaches saturation among internet users. While extant literature has extensively examined the impact of new channel introductions (e.g., online, offline, mobile) on firm performance, less attention has been paid to consumer behavioral nuances within established digital interfaces. Addressing this gap, our study pioneers a comparative analysis of purchasing dynamics across two dominant yet technologically distinct channels: native apps versus light-app channels (e.g., WeChat mini-programs). While both channels share core mobile attributes (e.g., small screen sizes, on-the-go accessibility), their divergent technological architectures (Swift, Kotlin, and Java vs. HTML, CSS, WXML, and WXSS) create systematically differentiated consumer experiences. Through econometric analysis of 185,437 transaction records from a multichannel B2C platform, we reveal that consumers tend to spend more, purchase more items, and exhibit a lower likelihood of product returns when shopping through the light-app channel compared to native apps. More importantly, these behavioral divergences are moderated by product categories, price levels, and discount depths. Our findings contribute to the multichannel retailing literature by providing new insights into consumers’ behavioral differences between the two popular, yet distinct, mobile channels. Based on these insights, we suggest that multichannel retailers should prioritize channel convenience and accessibility and reconsider their investments in mobile native apps. Additionally, retailers should tailor assortments, pricing, and discount strategies to each channel to effectively engage consumers and stimulate purchases. Our research also emphasizes the importance of aligning marketing, operations, and finance strategies in multichannel retailing.

EXPRESS: Equity-Driven Workload Allocation for Crowdsourced Last-Mile Delivery

2026-02-10T00:00:00+00:00

Production and Operations Management
Crowdshipping, a rapidly growing approach in Last-Mile Delivery (LMD), relies on independent crowdworkers to fulfill delivery orders. Building a sustainable network of crowdshippers is crucial for the long-term success of such systems, as participation is primarily driven by fair compensation. This is especially important for workers who rely on crowdwork as their main source of income, making equitable pay not just a matter of fairness but of financial well-being. In this study, we address several key questions that gig-economy platforms concerned with fair pay may ask: How can equity be measured? What are the associated cost implications? And how can potential drawbacks be managed? Our main contribution is the development of a practical, equity-oriented framework tailored to crowdshipping within an LMD environment. Inspired by the real-world operations of several crowdshipping platforms, the framework operates in real time and is built around a bi-objective optimization model that balances equity and cost. This allows us to systematically explore trade-offs and identify the equity measures that most effectively capture this balance. We demonstrate that even a modest reduction in cost efficiency (e.g., 2.5%) can lead to substantial improvements in equity; potentially up to 65%. Our results provide actionable insights for practitioners, including guidance on selecting appropriate equity measures. We also find that the best equity outcomes occur when the crowdshipper pool is kept relatively small. Furthermore, we quantify the performance loss of high- and low-performing crowdshippers as the pool size increases, offering valuable insights for workforce planning and management. Along similar lines, we demonstrate that our framework remains effective in managing vehicle shortages in dynamic environments while achieving comparable levels of equity improvement.

Mean-Risk Traffic Assignment Under the Continuously Distributed Risk-Aversion Factor

2026-02-10T00:00:00+00:00

Operations Research
From Discrete to Continuum: Risk Preference Heterogeneity in Traffic EquilibriumHow do travelers with heterogeneous risk preferences choose routes in congested networks with uncertain travel times? Conventional models characterize risk preference heterogeneity using a small number of discrete user classes. In “Mean-Risk Traffic Assignment Under the Continuously Distributed Risk-Aversion Factor,” Xu, Li, Xie, Chen, and Liu develop a new traffic equilibrium framework that directly accommodates a continuous distribution of risk aversion. The resulting model generalizes the classical Wardrop equilibrium to settings with both stochastic travel times and continuous preference heterogeneity, providing a more refined representation of real-world routing behavior. This study demonstrates that the continuous approach outperforms the discrete approach by not only eliminating the inherent discretization bias—that can mislead infrastructure investment decisions—but also delivers superior computational performance, requiring less run time and memory. More broadly, the framework could be potentially applied to a wide range of biobjective congestion routing games where user preferences are continuously distributed.

Optimal Feature-Based Market Segmentation and Pricing

2026-02-10T00:00:00+00:00

Operations Research
Operationalizing Semipersonalized PricingHow can modern firms leverage feature information to set prices in way that is both profitable and practical? A new study in Operations Research addresses this question by analyzing feature-based market segmentation and pricing (FBMSP), a semipersonalized approach to pricing where firms use customer characteristics to group buyers and set segment-specific prices. Although businesses often rely on heuristic “segment-then-price” methods, in their article the authors show that under realistic statistical assumptions, the jointly optimal segmentation and pricing policy can be computed efficiently. Further, using structural results about the optimal FBMSP, the authors prove that semipersonalized pricing quickly converges to the performance of fully personalized pricing, motivating its use in practice. Finally, in a case study on U.S. home mortgage data, they apply their method and show it significantly outperforms traditional heuristics, achieving near-maximal revenue with only a few segments. This research offers both practical tools and theoretical insights for firms navigating the balance between personalization and implementability in pricing.

The Acceleration of Artificial Intelligence: Rethinking Organization and Work in an Era of Rapid Technological Change

2026-02-10T00:00:00+00:00

Journal of Management Studies
Artificial intelligence (AI) is transforming the epistemic, interactional, and institutional foundations of contemporary organizations, yet management and organization studies are only beginning to theorise the implications of this shift. Existing research often treats “AI” as a singular construct, despite the fact that predictive, generative, agentic, and embodied systems rely on different logics and produce distinct organizational outcomes. This article interrogates the limits of this conceptual flattening and argues that cumulative theorising requires more precise specification of the technological systems under study. Drawing on developments across the field, we demonstrate how different modes of AI reshape core organizational constructs, including expertise, judgement, coordination, authority, and institutional adaptation. We advance a heuristic framework that differentiates among contemporary AI systems and clarifies their distinct affordances. The article concludes by outlining a research agenda that focusses on the shifting loci of agency, new decision architectures, and the normative and institutional challenges introduced by increasingly powerful AI systems.

Does General Solicitation Improve Access to Equity Capital for Small Businesses? Evidence from the JOBS Act

2026-02-10T00:00:00+00:00

Journal of Financial and Quantitative Analysis
Under Title II of the Jumpstart Our Business Startups Act, firms can sell private placement securities to the public via general solicitation (GS) or privately (non-GS). We find that equity offerings under GS tend to be riskier than under non-GS. After accounting for selection, GS issuers are less likely to succeed in i) raising capital, ii) getting venture capital (VC) funding, and iii) exiting via IPO or mergers and acquisitions, and incur substantial brokerage costs for advertising and verifying investor accreditation. However, GS appears to help new entrants and offerings that use registered brokers. The success of Form D financing improves future VC financing and exit outcomes.

Analyzing Consumer Footprints on E-Commerce Platforms: A Multichannel Sequential Search Model with Reference Price

2026-02-10T00:00:00+00:00

Information Systems Research
Accompanied by the popularity of mini-programs, consumers can easily browse products across both the e-commerce native app channel and the mini-program channel, leaving behind extensive click-through and purchase records across various channels. These multichannel footprints contain rich information about consumers’ search preferences, which offer great potential for optimizing platforms’ multichannel management. However, prior studies predominantly focus on single-channel contexts, which may not directly apply to analyzing consumers’ multichannel footprints. This research develops a multichannel sequential search (MSS) model to characterize consumers’ multichannel footprints as a threefold search process consisting of cross-channel, cross-product, and cross-page search, where consumers dynamically update their cross-channel reference prices as they navigate their search journeys. The estimation results of the MSS model reveal that consumers exhibit significant heterogeneity in channel preferences, cross-channel costs, and sensitivity to cross-channel reference prices, contributing to their diverse multichannel footprints. Drawing on a comprehensive understanding of the MSS process, our optimal policy recommends that the platform implement a promotion gap with an average discount percentage of 32% between the app and mini-program channels. The channel-specific promotion strategy fosters a strong cross-channel reference price effect, which uses a smaller promotion to achieve a significant profit improvement over the state-of-the-art model.

Conform or Workaround? A Multilevel Analysis of the Effect of Group Cultural Tightness on Enterprise System Use

2026-02-10T00:00:00+00:00

Information Systems Research
Enterprise systems (ESs) embed industrial best practices into adopting organizations through their system features, but employees frequently “work around” the system to get work done—sometimes in helpful ways, sometimes in risky ones. Our research shows that group cultural tightness (strongly enforced norms within a team) is a powerful lever for ES governance: tighter groups reliably increase conforming use while reducing both internal and external workarounds, and this pattern holds across Chinese and U.S. contexts and multiple organizational settings. Importantly, not all workarounds are equal. Evidence from multisourced, longitudinal field data indicates that conforming use and internal workarounds can improve job performance, whereas external workarounds harm performance—likely by creating fragmented processes, data gaps, and compliance and security exposure. For practice and policy, the message is clear: organizations should strengthen team-level norms and accountability to curb harmful “outside-the-system” behaviors, while simultaneously creating safe, sanctioned channels to surface, evaluate, and integrate beneficial internal workarounds (e.g., controlled extensions, approved templates, and rapid governance reviews). This balanced approach supports performance, compliance, and continuous improvement without shutting down frontline problem-solving.

Sustainability-Oriented Institutions and the Success of Green Reward-Based Crowdfunding Campaigns

2026-02-10T00:00:00+00:00

Entrepreneurship Theory and Practice
Previous research has produced mixed evidence on the success of green crowdfunding campaigns. We investigate whether country-level environmental institutions help explain these inconsistencies by moderating backers’ responses. While such institutions may enhance campaign legitimacy, they can also reduce the perceived urgency to contribute by offering alternative support channels. Analyzing a large Kickstarter dataset with machine learning, we find that green campaigns are less likely to succeed, particularly in countries with stronger environmental institutions. Post-hoc analyses point to a crowding-out effect of institutional support, which dampens individual backing, and suggest that omitted variable bias may partly account for prior conflicting results.

Mapping the landscape of research findings: Generalization across contexts in strategic management research

2026-02-11T00:00:00+00:00

Strategic Management Journal
Knowledge accumulation requires that we understand whether and when relationships identified in any research setting generalize to others—that is, suggesting domains where results hold (or not). Strategy scholars carefully identify how theoretical mechanisms operate in their chosen research contexts, but attend less to whether their findings apply in other contexts. Accordingly, we recommend reframing empirical contexts, typically described in terms ofnominalcategories (e.g., industries, countries, or time periods), by highlightingcontextual attributesof the nominal settings (e.g., industry concentration or technological modularity), which in turn reflect more abstractconceptualcategories (e.g., uncertainty, interdependence, and variance) across potential research contexts. This approach can help integrate prior findings and suggest future study contexts to better enhance our understanding of the research landscape.Managerial SummaryAcademic research in strategic management tends to derive findings in very specific nominal settings—particular industries, years, and regions. Since strategy practitioners operate across a wide variety of industries and regions, they need to assess whether available research findings are applicable in their own settings. We suggest that understanding whether and when research findings apply to unstudied settings can be facilitated by categorizing research settings using more abstract conceptual constructs (such as environment uncertainty, variance across firms, or interdependence between firms), rather than by the traditional emphasis on nominal settings. We discuss a variety of research setting attributes (such as industry concentration and technological modularity) that can aid the translation of extant research findings to settings where practitioners are operating.

Disaggregated Economic Accounts

2026-02-11T00:00:00+00:00

The Quarterly Journal of Economics
We develop a system of disaggregated economic accounts. The system breaks down national accounting positions into bilateral flows between consistently defined groups of consumers (“consumer cells”), groups of producers (“producer cells”), the government, and the rest of the world. We disaggregate the full circular flow of money, including consumer spending, labor compensation, firm profits, trade in intermediates, foreign trade, and government transactions, while satisfying all national accounting identities. We implement the disaggregated system for small region-by-industry cells in Denmark and present stylized facts, including variation in domestic spending shares, local and urban bias in consumer spending, and a pattern of “triangular flows” across regions. Cell-level measures of “spending intensity” capture how much spending by a cell contributes to the income of cells experiencing unemployment after a shock. Using a general equilibrium model, we show that fiscal transfers are more effective in stimulating aggregate GDP when they target cells with high spending intensity on unemployed cells. Knowledge of the disaggregated economic accounts helps governments select more effective policies.

A Note on “Corporate Provision of Public Goods”

2026-02-11T00:00:00+00:00

Management Science
Morgan and Tumlinson (2019, Management Science 65(10), 4489–4504) study a game in which citizens first fund a company venture or purchase bonds, and then bondholders and the firm’s manager, acting on behalf of the shareholders, contribute to a public good. One of their major results is that the venture is funded with positive probability in all trembling-hand perfect Nash equilibria. The purpose of this note is to prove the existence of economies in which the venture is not funded in all trembling-hand perfect Nash equilibria.This paper was accepted by Joshua Gans, business strategy.

Is Multicloud the Future? Desirability of Compatibility in Cloud Computing Market

2026-02-11T00:00:00+00:00

Management Science
Leading online service providers, as major customers of cloud computing services, are increasingly adopting a multicloud strategy to enhance service delivery across different locations. However, compatibility issues among various cloud platforms complicate the implementation of this strategy. To address this challenge, cloud providers can offer a multicloud management platform that acts as a bridge between distinct cloud environments. Although such a platform can enhance customers’ multicloud experience, it also entails additional costs for the cloud providers. Our paper examines cloud providers’ compatibility decisions and the impact of compatibility on competing cloud providers and their customers. Our findings reveal that, when compatibility is established in equilibrium, cloud providers consistently set higher prices for their resources than when compatibility is absent. Additionally, we find that a cloud provider with greater market dominance always attracts more demand, whereas the rival with smaller market dominance may experience a demand decrease. Interestingly, when a cloud provider establishes compatibility in equilibrium, the competitor’s profit always increases, although customer welfare can decline under compatibility. These insights, along with other findings presented in the paper, can support cloud providers in deciding when to establish compatibility and how to price their resources effectively. Furthermore, we provide guidelines for policymakers on when compatibility could enhance customer welfare, supporting effective implementation of compatibility strategies.This paper was accepted by D. J. Wu, information systems.Funding: S. Kumar thanks Temple Center for International Business Education and Research for partially supporting this research.Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.01359 .

Team Iungens Practice: A Behavioural Perspective to Bridge Demographic Faultlines and Relationship Conflicts for Team Creativity

2026-02-11T00:00:00+00:00

Journal of Management Studies
Although employee diversity can contribute to team creativity, extant research has shown that, when diverse employees form strong demographic faultlines, subgroup identity separation and intensified relationship conflicts may be harmful to team creativity. Therefore, effective remedies in managing relationships and bridging identities between subgroups are urgently needed in these teams. Building on brokering research, we introduce team iungens practice (TIP) – so named from a Latin phrase meaning ‘joins or unites’ – as a new team process that team members or leaders can engage to forge and strengthen connections or facilitate new coordination among team members. We propose that teams can effectively utilize this behavioural strategy to bridge structural (faultline) and process (conflict) divides rooted in subgroup identity separation, thereby mitigating their negative impacts on team creativity. In Study 1, we developed and validated a scale to measure TIP. In Study 2, we found that TIPs effectively mitigated the positive effect of demographic faultline strength on relationship conflict and attenuated the negative impact of relationship conflict on team creativity from a multisource and multistage survey study. We demonstrate the value of TIPs as a behavioural strategy for managing demographic faultlines and relationship conflicts in teams.

Privacy trade-offs in international markets

2026-02-11T00:00:00+00:00

Journal of International Business Studies
Even as data privacy regulations expand globally, their strategic implications for international business remain underexplored. Prior research mostly treats data privacy regulation as a compliance issue, overlooking how firms strategically manage cross-border variations and respond to stakeholder pressures. Drawing on institutional economics and stakeholder theory, this article conceptualizes two trade-offs generated by data privacy regulations: (1) a regulatory, cost–benefit trade-off between privacy compliance costs and customer privacy empowerment and (2) a firm performance, temporal trade-off in which early compliance costs undermine short-term outcomes, before longer-term, trust-based benefits emerge. An international event study of ten regulations across 24 countries affirms that data privacy regulations impose short-term financial setbacks but generate long-term gains. These effects vary across contexts: At regulation, industry, and firm levels, conditions in which internal pressures to minimize risk dominate (intense privacy regulation, strong data dependence, limited resources) intensify early losses. At national levels, environments dominated by external pressures for credible compliance (high formal and informal institutional effectiveness) offset short-term losses and magnify long-term benefits. For international business theory, this study reframes data privacy regulation as a strategic opportunity and highlights how internal versus external legitimacy pressures shape trade-offs that firms must balance to navigate cross-border compliance demands.

Financial Climate‐Risk Measurement, Impact Funds, and Green Transitions

2026-02-11T00:00:00+00:00

Journal of Accounting Research
Regulators are contemplating or mandating precise measurement of financial climate‐risk exposure to promote sustainable investments. We show that such mandates can be counterproductive in the presence of social funds that catalyze change by subsidizing the adoption of cleaner production technologies. Firms can exploit a social fund's impact motive by measuring their climate‐risk exposure imprecisely. This strategic imprecision prevents the fund from distinguishing between firms that require subsidies and those that would switch to clean technologies for financial reasons alone, thereby increasing the ex ante subsidies firms can extract. A by‐product of this rent‐seeking behavior is that firms adopt clean technologies more frequently than would be jointly efficient under precise measurement. Our analysis suggests that the regulatory push for precise climate‐risk measurement can reduce social funds' impact and the frequency of green transitions.

Work Design and Compensation Strategies in a Post‐ COVID World: Evidence From Online Job Postings

2026-02-11T00:00:00+00:00

Human Resource Management
This study examines how the COVID‐19 pandemic, as a disruptive external shock, has reshaped organizational practices, with particular emphasis on changes in work design and compensation strategies. Drawing on a unique dataset of over 1.8 million online job postings from Chinese publicly listed firms, we employed a large language model to systematically quantify work design characteristics. Using a difference‐in‐differences approach, we found that the pandemic significantly enhanced skill variety, task identity, task significance, autonomy, feedback, and work‐from‐home feasibility. Notably, these effects varied by occupational nonroutineness: occupations with high nonroutineness exhibited larger increases in task significance but smaller increases in other work design characteristics. Moreover, work design characteristics mediated the relationship between the pandemic and salaries, though these indirect effects were attenuated for nonroutine occupations. Additionally, we observed post‐pandemic adjustments in firms' compensation, including notable declines in performance‐related pay and insurance benefits, accompanied by increases in subsidies. These findings provide new insights into how organizations redesign work and adjust compensation strategies in response to large‐scale external shocks.

Embracing Complexity in HRM Research: A Call for System and Process Perspectives

2026-02-11T00:00:00+00:00

Human Resource Management
Human resource management (HRM) is inherently complex. It involves systems of principles, practices, and activities operating at individual, group, organizational, and macro levels, which are interlinked through complex processes. Yet, empirical research has not kept pace with this conceptual richness. We hope to inspire HRM scholars to address complexity. We draw on theoretical frameworks that explicitly conceptualizesystems as systems,processes as processesandsystems as processescomplemented by empirical examples (mainly from adjacent fields) to illustrate how these modes of thinking have helped solve complex problems and how this could be applied to HRM. Embracing complexity in HRM is not about making research harder for its own sake but about seeing the field as it truly is dynamic, interconnected, and evolving. We need to shift our attention from isolated variables toward patterns, processes, and interdependencies across time. This requires richer data, deeper collaboration, and methodological boldness, and opens new possibilities for understanding the patterns and dynamics that shape people and organizations. Complexity in HRM invites us to see connections rather than fragments, to uncover the dynamics beneath the surface, and to design insights that matter. Perhaps the future of HRM research lies not in simplifying complexity, but in embracing it.

Civil War–Induced Displacement and Human Capital

2026-02-12T00:00:00+00:00

The Quarterly Journal of Economics
We study the impact of conflict-induced displacement on human capital and occupational shifts, focusing on the Mozambican civil war (1977 - 1992), during which millions of civilians were forced to flee to the countryside, cities, and neighboring countries. Reconstructing the wartime mobility histories of the surviving population, we examine the consequences of multiple displacement trajectories within a unified framework. First, we characterize the education and sectoral employment of the universe of (non-)displaced. Second, we exploit differences in relocation trajectories among extended kin members during their schooling years. Displacement is associated with significant gains in education. Third, employing a “movers design,” we show that minors displaced earlier to better districts experienced an increase in educational attainment. Focusing on moves during the intensification of the war and when comparing members of the same household, regional childhood exposure effects remain strong, whereas spatial sorting becomes negligible. Fourth, we jointly estimate place-based, spatial sorting, and uprootedness effects, showing that all forces are at play. Fifth, a small survey in Mozambique’s largest northern city reveals long-term effects: internally displaced report higher education than their siblings who stayed behind, but lower social capital and worse mental health relative to locals. Our findings demonstrate that displacement shocks can foster human capital accumulation, even in very low-income settings, albeit at the cost of enduring social and psychological traumas.

Reducing Manual Labeling Effort in Imbalanced Data Sets: Active Learning for Detecting Illicit Massage Business Reviews

2026-02-12T00:00:00+00:00

Operations Research
Smarter Labeling to Detect Hidden Human Trafficking RisksHuman trafficking investigators face the immense challenge of sifting through vast amounts of online data to uncover illicit activities. In their article, Reducing Manual Labeling Effort in Imbalanced Data Sets: Active Learning for Detecting Illicit Massage Business Reviews, Tobey, Mayorga, Bosisto, and Özaltın present a novel framework that uses reinforcement learning–based active learning to reduce the burden of manual data labeling, improving detection of illicit massage business reviews on Yelp. By strategically selecting the most informative reviews for expert annotation, the approach achieves strong performance despite limited and imbalanced data sets, easing the emotional and time costs of reviewing disturbing content. The study demonstrates that their method outperforms benchmark active learning strategies, remains effective even with large query batches, and generalizes across regions. Beyond combating human trafficking, the framework offers a scalable solution for other domains with scarce, sensitive, or costly-to-label data.

Online Demand Fulfillment Problem with Initial Inventory Placement: A Regret Analysis

2026-02-12T00:00:00+00:00

Operations Research
Studying How Inventory Placement Shapes Online Fulfillment PerformanceA growing share of e-commerce operations involves deciding not only how to fulfill incoming orders, but also where to position inventory beforehand. In “Online Demand Fulfillment Problem with Initial Inventory Placement: A Regret Analysis,” Arlotto, Keskin, and Wei examine how these two decisions interact. The authors introduce a joint regret framework that evaluates the performance of a fulfillment policy together with the initial allocation of inventory across warehouses. Their analysis shows that probabilistic fulfillment inevitably accumulates regret that grows with the length of the planning horizon, regardless of how inventory is initially placed. By contrast, when combined with an appropriate offline inventory placement, the score-based approach achieves a regret bound that remains stable over time and scales only with the size of the system. The study offers insight into the role of initial placement in shaping fulfillment policy performance.

Tail-Driven Nonparametric Estimation for State Price Densities

2026-02-12T00:00:00+00:00

Management Science
This paper proposes and implements a novel nonparametric method for estimating the state price density (SPD) over the entire state space, including the tails. This SPD estimator achieves shape consistency properties in theory, particularly at the tails. Monte Carlo simulations demonstrate the accuracy and robustness of our method. In particular, our estimator accurately captures the risk-neutral tail distribution, which is often underestimated by existing alternative methods. In an empirical analysis based on Standard and Poor’s 500 options data, we evaluate the out-of-sample performance of our SPD estimation method and demonstrate that the estimates can serve as effective indicators for market conditions and exhibit predictive power for asset returns. Combining these perspectives, we suggest that our SPD estimator renders a valuable tool for risk management and asset pricing.This paper was accepted by Kay Giesecke, finance.Funding: The research of C. Li was supported by the Guanghua School of Management, the Center for Statistical Science, the High-Performance Computing Platform, and the Key Laboratory of Mathematical Economics and Quantitative Finance (Ministry of Education) at Peking University as well as the National Natural Science Foundation of China [Grant 72173003]. The research of X. Song was supported by the Guanghua School of Management, the Center for Statistical Science, and the Key Laboratory of Mathematical Economics and Quantitative Finance (Ministry of Education) at Peking University as well as the National Natural Science Foundation of China [Grants 72373007 and 72333001]. The research of Y. Wan was supported by the School of Management Science and Engineering and the Coordinated Innovation Center for Computable Modeling in Management Science at Tianjin University of Finance and Economics as well as the Tianjin Municipal Education Commission [Grant 2022SK188].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03236 .

Expectations Matter: When (Not) to Use Machine Learning Earnings Forecasts

2026-02-12T00:00:00+00:00

Management Science
We comprehensively examine the usefulness of machine learning technology to predict a firm’s earnings and offer three main findings. First, although prior literature suggests machine learning can offer better earnings forecasts than analysts, we show that this result is highly sensitive to machine learning model specification choices (i.e., 80% of evaluated machine forecasts fail to beat analysts). Second, we examine why the most accurate machine learning forecast consistently beats analysts, finding that they correct for predictable analyst biases that are both linear and nonlinear and largely relate to analysts’ prior forecast errors, forecasted earnings levels, and the firm’s stock price. Finally, we find that investors’ earnings expectations, as revealed through stock prices, largely—but do not fully—correct for these predictable analyst biases, with delayed price realization up to nine months. In additional analysis, we find that optimal machine learning specification choices remain stable over time and that, although the machine’s outperformance narrows in recent periods, it remains substantial among small-cap stocks. Overall, our study moves beyond the question of whether machine forecasts are superior to human forecasts and instead focuses on which machine forecast specifications matter, as well as when and why machine forecasts are most superior. In so doing, we provide code and estimates for the most accurate machine forecast specification and demonstrate that investors’ expectations appear to largely (but not fully) align with them.This paper was accepted by Suraj Srinivasan, accounting.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05808 .

Supply Chain Guardianship: Why Some Firms Intervene When Other Firms Commit Fraud

2026-02-12T00:00:00+00:00

Journal of Operations Management
In supply chains, firms often become aware of illegal actions committed by their partners, prompting the critical question: when and why do those who know become those who act? Drawing on industry examples of supply chain fraud, we introduce the concept of supply chain guardianship to study how firms respond to potential fraud committed by their supply chain partners. We identify key influences on supply chain guardianship and refine these insights into hypotheses, which we test across four behavioral experiments (n= 1000). Study A finds that the tone at the top of an organization can promote supply chain guardianship and that state moral disengagement is negatively correlated with it. Study B manipulates process moral disengagement and finds that it reduces guardianship behavior. Although the network position of the supply chain guardian emerges as important in industry examples, we do not find a significant effect in the experiments. We replicate and validate these findings in Studies C and D. This study offers an initial foundation for a behavioral theory of interfirm fraud responses in supply chains and offers practical insights into how firms can increase supply chain guardianship across macro‐, meso‐, and microlevels.

A CEO-Driven Process Model of Firm Responsiveness to Secondary Stakeholder Demands: The Roles of CEO Values and Passion

2026-02-12T00:00:00+00:00

Journal of Management
Secondary stakeholder demands represent increasingly important strategic issues for firms. However, the principal drivers of firm response remain poorly understood. We extend research on firm responsiveness to secondary stakeholders by introducing a new theoretical perspective to detail the cognitive processes through which key CEO attributes—namely, personal values and passion—shape the trajectories of firm responsiveness. We first outline key aspects of secondary stakeholder demands to explain why CEO values and passion are important determinants of a firm’s response to secondary stakeholder demands. We then explain how a CEO’s values and passion influence the process of firm responsiveness. Finally, we extend our arguments to illustrate how values and passion-infused responses may influence social (dis)approval among key organizational stakeholders. By focusing on the cognitive processes of CEOs, we offer novel theoretical contributions to stakeholder theory, strategic leadership research, and the literature on firm social evaluations.

How Do Different Remote Work Arrangements Impact Employee Job Satisfaction and Retention?

2026-02-13T00:00:00+00:00

Management Science
We study how working remotely impacts employee job satisfaction and retention using unique data on the remote work arrangements of nearly 165,000 employees. Our findings show that the positive association between working remotely more frequently and job satisfaction diminishes substantially after controlling for employee compensation, occupation, demographics, and workplace characteristics (e.g., feeling appreciated at work). Moreover, remote work is associated with a higher intention to leave the firm after considering these other factors. This suggests that workplace characteristics beyond remote work have a more consequential association with job satisfaction and retention than remote work itself. Using variation in job tasks and worker–manager relationships, we find that working remotely more frequently confers higher job satisfaction for employees in low-coordination roles or those who perceive their direct supervisors unfavorably. The findings suggest that remote work plays a more limited role in shaping job satisfaction and retention relative to broader workplace characteristics, and that its value depends on how well work arrangements are matched to employees’ strengths, coordination requirements, and managerial context.This paper was accepted by Shiva Rajgopal, accounting.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.07017 .

Information Provision and the Curse of Knowledge

2026-02-13T00:00:00+00:00

Management Science
Common wisdom suggests that the “curse of knowledge” (COK), whereby better-informed individuals are unable to ignore their private information when forecasting others’ beliefs, reduces the quality of communication. We study how this bias affects costly information provision by a founder who wants to raise financing from an outside investor. When the founder exhibits COK about the content of her communication, there is less information provision and payoffs tend to be lower for both players. However, we show that when the founder exhibits COK about the context of her message, the bias can lead to more information production and better investment decisions. Moreover, this can exacerbate the conflict of interest between the founder and the outsider.This paper was accepted by Ranjani Krishnan, accounting.Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.01839 .

The politics of impact: How political ideology shapes perceptions of the environmental impact of individual actions

2026-02-14T00:00:00+00:00

Journal of Consumer Psychology
Although consumers who engage in the same sustainable behaviors objectively have the same environmental impact, this research finds that people's perceptions of that impact are subjective and systematically shaped by political ideology. Seven studies demonstrate that conservatives tend to perceive their sustainable actions to have less of a positive impact on the environment than liberals do, which predicts conservatives' lower engagement in sustainable behaviors. This effect occurs not just because of their own climate change beliefs, but also because of the lower observed prevalence of sustainable behaviors in their in‐group. Consistent with this mechanism, when (a) the behavior is presented in a domain where it is seen as more prevalent among conservatives' ingroup members (i.e., health vs. sustainability), (b) the message emphasizes the prevalence of sustainable actions within the ingroup, or (c) impact is explicitly communicated, conservatives' perceptions of impact and their willingness to engage in sustainable behaviors increase. This work contributes to the literature on political ideology, highlighting effective ways to promote sustainable behavior across the political spectrum.

Strategic positioning and accelerating regulatory clearance for new ventures

2026-02-15T00:00:00+00:00

Strategic Management Journal
How do new ventures strategically position their products to accelerate regulatory clearance? We investigate this question in the medical device industry, where regulatory clearance is a prerequisite for market entry. We examine 239 new firms' 510(k) device clearances by the FDA between 2001 and 2019. Our approach combines quantitative analysis of firms' claims of similarity with extensive field research. We find that strategic positioning significantly impacts time to clearance, and that effective strategies evolve over time. For first products, firms accelerate clearance by citing fewer reference products while strategically positioning relative to same‐category products and to category exemplars; for second products, firms accelerate clearance by referencing their own products. These findings extend positioning theory into regulatory contexts and reveal how a firm's optimal strategic positioning changes across successive products.Managerial SummaryWe study how new medical device startups can speed up regulatory approval by strategically positioning their products for the FDA. Analyzing 239 firms' 510(k) clearances from 2001 to 2019, we find that the way companies frame their products for regulators plays a key role in how quickly they are cleared. For their first products, firms benefit from referencing fewer existing devices and aligning with well‐known or similar products. For their second product, referencing their own previously cleared device helps accelerate the process. Our findings show that positioning is a powerful tool for navigating regulatory hurdles. We highlight how the most effective strategy changes as a company grows, offering practical guidance for leaders bringing new medical technologies to market.

The Present Value of Future Market Power

2026-02-16T00:00:00+00:00

The Review of Financial Studies
We introduce a present-value identity relating a firm’s market value to expected future markups, output growth, discount rates, and investments. Distinguishing current from expected markups reveals five empirical facts: (1) Expected markups account for half the rise in U.S. firm values since 1980. (2) The rise in aggregate expected markups reflects market-share reallocation toward high-expected-markup firms and within-firm increases. (3) Expected markups are linked to intangible investments. (4) They relate negatively to discount rates over time but (5) positively to abnormal returns across firms. Finally, variation in long-term expected markups is primarily associated with asset prices rather than current markups.

EXPRESS: Pricing Limited Capacity under Consumer Deliberation

2026-02-16T00:00:00+00:00

Production and Operations Management
The pricing of limited capacity is a pivotal issue in many business contexts such as event ticket sales and online flash sales. Prior studies overlook consumer deliberation behavior in the presence of supply limits, leading to ineffective pricing decisions. This motivates investigation of consumer deliberation behavior under supply limits, and the capacity-constrained firm’s pricing and operational strategies. We first show that severe supply limits inhibit consumers’ costly deliberation, while moderate supply limits enhance consumer deliberation as the availability enhancement effect of deliberation reaches its highest potency. Accordingly, the firm with intermediate capacity tends to adopt a normal pricing strategy, i.e., pricing as in the market of informed consumers. However, when the capacity is relatively low (sufficiently high), the firm can charge a relatively high (low) price to induce (deter) consumer deliberation, i.e., transgressive (regressive) pricing; when the capacity is sufficiently low, the firm should adopt a prior value pricing strategy because consumers never deliberate. Second, on the supply side, it is not always necessary to fully utilize the limited capacity in supplying products, and for firms with high capacity, a low-quality strategy may be preferable even if quality improvement is costless. Finally, extensions to imperfect learning and heterogeneous deliberation costs show the robustness of our baseline model and offer new insights. Interestingly, the presence of demand uncertainty may benefit firms. Our study sheds light on the pricing of limited capacity and elucidates several strategic decisions when considering consumer deliberation behavior.

Advisor–Advisee Research Overlap and Its Implications for Scientists’ Early-Career Performance in the United States

2026-02-16T00:00:00+00:00

Organization Science
A genealogical training process, in which senior (advisor) scientists mentor and train junior (advisee) scientists is one of the core organizational features of modern science. In this paper, we examine a key question faced by all junior scientists during their training: What impact does an advisee’s research agenda overlap with his or her advisor have on the advisee’s career-relevant performance outcomes? To answer this question, we constructed a novel, bibliometric-record-based data set on 11,289 U.S. biomedical scientists (advisees) who were trained in 5,632 principal investigator advisors’ labs between 1985 and 2009. We examined the relationship between advisor–advisee research overlap and an array of performance outcomes for emerging scientists, revealing a consistently positive relationship between high advisor–advisee research overlap and the junior scientist’s early-career funding outcomes. We further provide evidence that this positive relationship rests upon enhanced tacit knowledge transfer, as well as providing suggestive evidence for the boundary conditions of an intellectual independence imperative and potential competition between advisors and advisees. Taken together, these findings provide a more complete understanding of how advisor–advisee relationships shape new scientists’ performance during their early careers.Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.17601 .

Lending Leniency: The Relationship Between High-Status Affiliations and Consumer Acceptance of Products in Contested Markets

2026-02-16T00:00:00+00:00

Organization Science
Markets are often sites of ongoing contestation regarding the acceptability of various product features and production practices. Although prior research has explored how producers resolve moral controversies, less attention has been paid to how they convince consumers of their products’ moral acceptability when consensus remains elusive. This study addresses this gap by examining a prominent tactic: producers’ strategic affiliations with high-status moral advisors. We theorize that such affiliations reassure consumers, making them more likely to accept reduced financial returns for products bearing a strong stamp of moral approval. We test and find support for this argument using data on 1,540 Shariah-compliant bonds, or sukuk, where there has been ongoing debate over what product features are allowed according to Islam. We find that sukuk endorsed by high-status Shariah scholars (sheikhs) have significantly lower coupon rates, indicating consumers’ willingness to accept reduced financial returns in exchange for moral reassurance. Additionally, the impact of high-status endorsements weakens as sukuk adhere more closely to strict moral interpretations, highlighting a compensatory relationship between status signals and substantive product features. Supplementary analyses reveal that issuers are more likely to seek endorsements from high-status moral advisors when their products are complex or opaque. Overall, this research helps to build a more comprehensive picture of the tactics producers use to overcome the challenges of contested moral markets.

Optimal Tax Timing with Transaction Costs

2026-02-16T00:00:00+00:00

Management Science
We develop a dynamic portfolio model incorporating capital gains tax (CGT), transaction costs, and year-end taxation. We find that even tiny transaction costs can lead to significant deferral of large losses and that transaction costs affect loss deferrals much more than gain deferrals. Our model can thus help explain the puzzle that even when investors face equal long-term/short-term CGT rates, they may still defer realizing large capital losses for an extended period of time, displaying the disposition effect. In addition, we find that misestimating transaction costs is costly. We also provide several unique, empirically testable predictions and shed light on recently proposed tax policy changes.This paper was accepted by Agostino Capponi, finance.Funding: M. Dai was supported by the National Natural Science Foundation of China [Grants 72432005 and 12071333], the Research Grants Council of Hong Kong [Grants 15217123, 15212324, 15213422, and T32-615/24), and The Hong Kong Polytechnic University [Grants P0039114, P0042456, and P0042708]. Y. Lei was supported by the National Natural Science Foundation of China [Grant 72271112]. C. Yang acknowledges support from the Research Grants Council, University Grants Committee, Hong Kong [Grants ECS 24207621 and GRF 14207723].Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.03118 .

Simple and (Approximately) Optimal Mechanism in Efficiency and Equality Tradeoff

2026-02-16T00:00:00+00:00

Management Science
Many large cities have adopted a policy of restricting the number of new vehicle licenses in order to address the challenges of traffic and air pollution problems. An important question that then arises is how to allocate these limited new license quotas among a large set of demanders while taking both social efficiency and equality into consideration. Inspired by this practical problem, we study the problem of designing simple and optimal mechanisms to tradeoff between efficiency and equality in a similar public goods allocation problem. We first propose a truthful two-group mechanism framework that is both general and simple and then use it to compute the optimal solution that maximizes social efficiency while guaranteeing a certain level of equality. Interestingly and surprisingly, under some natural conditions regarding the players’ private values, we show that the optimal mechanisms within the proposed framework are always the mechanisms that first run an auction and then run a lottery (ATL for short). In addition, beyond the framework and those conditions, we prove that the ATL mechanism can always guarantee at least [Formula: see text] ([Formula: see text] is the minimum equality level) of the optimal solution efficiency. This approximation ratio can in practice be further improved to near-optimal when resources are scarce and the natural conditions hold. We further establish a theoretical connection between these natural conditions and common value distributions, demonstrating that these conditions are widely satisfied in practice. Finally, we implement several experiments to verify the ATL mechanism’s optimality and robustness.This paper was accepted by Chung Piaw Teo, optimization.Funding: This work was supported by the National Natural Science Foundation of China [Grants 72401252, 72192830, 72192832, 62472428, 72192804, 72301235, 72394360 and 72394363]; the Public Computing Cloud, Renmin University of China, the fund for building world-class universities (disciplines) of Renmin University of China; the Beijing Natural Science Foundation [Grant Z220001].Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.02785 .

EXPRESS: Market Effects of Inattention: Theory and Evidence from Left-Digit Bias

2026-02-16T00:00:00+00:00

Journal of Marketing Research
A large body of research shows that even when information is accessible, consumers often fail to attend to it. To what extent and under what conditions can firms profit from such consumer inattention? We study this question theoretically and empirically in the used car market, focusing on the widely documented left-digit bias. Theoretically, firms can profit from targeting the most inattentive consumers even when there is an active decentralized market for used goods trading. Leveraging a detailed dataset of millions of automobile transactions from a seven-year period, we find that consumers exhibit inattention in the form of left-digit bias to the odometer, and such inattention is estimated to be significantly more for consumers who buy from firms. Compared to private sellers, car dealerships transact with ex-post significantly more left-digit-biased consumers. Dealerships sell more vehicles with odometer readings below round numbers, sell them faster, and extract higher margins from these vehicles. Our results imply that intermediaries can “skim” consumers with specific behavioral biases and extract meaningful surplus from selling to them.

Joining Decision‐Making, Moral Thinking, and Collective Action: Grand Challenges as a Phenomenology of Deliberation

2026-02-16T00:00:00+00:00

Journal of Management Studies
This conceptual article argues that the mutual relevance ofgrand challengesand organization and management studies is best approached phenomenologically. Rather than constituting objects to be theorized or denoting special empirical contexts, grand challenges structure researchers’ attention and shape their interpretations of the processes and systems of deliberation through which collective action is coordinated. From this perspective, grand challenges require researchers to innovate their understandings of deliberation and to ensure that newly generated knowledge is redirected towards management and policymaking. The article integrates the Carnegie School theory of organization with French pragmatic sociology’s theory of justification, oreconomies of worth, to develop a phenomenological model of situated deliberation that links decision‐making with moral reasoning. This model highlights deliberation’s articulated, evaluative, contestable, and trans‐institutional character, as well as its grounding in the cognitive capacities and sociality of actors and observers – regardless of the scale, scope, or stratification of the underlying coordination problems. Building on this framework, the article advocates that grand challenge researchers adopt the standpoint ofentrepreneurial observers: actors anchored by socio‐economic and scientific commitments who envision the integration of previously disjointed social systems of deliberation to orient collective action.

How Gender Diversity and Equality in R&D Departments Jointly Affect Firm-Level Innovation Through Innovation Capabilities

2026-02-16T00:00:00+00:00

Journal of Management
Innovation research has long shown that firms depend on their R&D personnel to strengthen their innovation capabilities, sparking scholarly interest in how to best staff R&D departments for competitive advantage, with a focus on gender diversity. However, left unexamined are the mechanisms linking gender diversity in R&D departments and firm-level innovation. Further, despite growing interest in the conditions under which diversity may enhance innovation, researchers have not adequately addressed how department-level gender rank inequality—the disproportionate concentration of men and women at various hierarchical ranks—interacts with numerical diversity to affect innovation outcomes. We address both limitations by integrating insights from the information/decision-making perspective on diversity and research on inequality with research on innovation capabilities. Specifically, we develop and test a model explaining how R&D department gender diversity and gender rank equality jointly affect three firm-level innovation capabilities: breadth of external knowledge search, use of external knowledge, and quality of the innovation implementation process. We also examine the indirect effects of gender diversity on firm-level innovation, conditional on gender rank equality, via these capabilities. Our analyses of 552 corporate R&D departments over 12 years show how gender diversity and gender rank equality in R&D departments jointly relate to higher levels of all three firm-level innovation capabilities and, indirectly, to greater firm-level innovation. Our findings suggest that improving a firm’s innovation capabilities and performance requires both increased representation of women in R&D departments and proportional representation of men and women at all ranks of R&D departments.

Founding Experience and Tech-Based Ventures’ Innovation: The Mediating Role of Absorptive Capacity

2026-02-16T00:00:00+00:00

Entrepreneurship Theory and Practice
Drawing on insights from the knowledge-based view of the firm, we introduce a path that links founders’ founding experience to organizational design choices in tech-based ventures that instill absorptive capacity, which, in turn, allows them to produce innovation output at scale. We hypothesize that founders’ recognition of the importance of knowledge management, and their acquisition of skills to implement knowledge management systems effectively, underlie this venture-building path. We also suggest that this path is stronger when experienced founders have been exposed to venture capital investors in their prior ventures. We test our theoretical predictions with a longitudinal sample of 1,560 U.S. tech-based ventures.

Financial Intermediaries and the Yield Curve

2026-02-17T00:00:00+00:00

The Review of Financial Studies
I study the yield curve dynamics in a general equilibrium model with financial intermediaries facing financing constraints. When constraints bind, intermediaries reallocate their portfolios, causing deadweight losses in aggregate consumption, thus affecting savers’ marginal utility. Because the yield curve is a forecast of marginal utility, intermediaries’ constraints show up, via general equilibrium forces, in long-term yields. I show that the mechanism connecting intermediaries’ constraints and long-term yields produces highly nonlinear interest rate dynamics and a positive real term premium in equilibrium. I extend the analysis to the nominal yield curve using a simple Taylor rule.

Algorithmic Pricing and Liquidity in Securities Markets

2026-02-17T00:00:00+00:00

The Review of Financial Studies
We study “Algorithmic Market Makers” (AMs) that use Q-learning algorithms to set prices for a risky asset. We find that while AMs successfully adapt to adverse selection, they struggle to learn competitive pricing strategies. This failure is driven by limited experimentation and noisy feedback regarding the profitability of undercutting a competitor. Consequently, an increase in AMs’ profit volatility tends to result in less competitive market outcomes. These features leave identifiable patterns: for example, AMs earn higher rents in the absence of adverse selection, and their bid-ask spreads respond asymmetrically to symmetric shocks to their costs. (JEL D43, G10, G14)

Cooling Auction Fever: Evidence from the Housing Market

2026-02-17T00:00:00+00:00

The Review of Financial Studies
We study the effects of underquoting, the practice of setting listing prices below sellers’ reservation values, on housing auctions. Laws introduced in Australia to deter underquoting lead to higher listing prices, but also to declines in sales prices and sales probabilities. We develop a quantitative model to formalize predictions under different assumptions about bidders’ information and rationality. The effects of the laws are matched by a version of the model in which participating bidders overbid. While both behavioral biases arising during the auction and switching costs can explain overbidding, empirical and survey evidence points to behavioral biases as the main mechanism. (JEL D10, D80, G40, R30)

Decision Theory for Treatment Choice Problems with Partial Identification

2026-02-17T00:00:00+00:00

Review of Economic Studies
We apply classical statistical decision theory to a large class of treatment choice problems with partial identification. We show that, in a general class of problems with Gaussian likelihood, all decision rules are admissible; it is maximin-welfare optimal to ignore all data; and, for severe enough partial identification, there are infinitely many minimax-regret optimal decision rules, all of which sometimes randomize the policy recommendation. We uniquely characterize the minimax-regret optimal rule that least frequently randomizes, and show that, in some cases, it can outperform other minimax-regret optimal rules in terms of what we term profiled regret. We analyze the implications of our results in the aggregation of experimental estimates for policy adoption, extrapolation of Local Average Treatment Effects, and policy making in the presence of omitted variable bias.

Survey of Data-driven Newsvendor: Unified Analysis and Spectrum of Achievable Regrets

2026-02-17T00:00:00+00:00

Operations Research
Unifying the Regret Spectrum in Data-Driven NewsvendorThe data-driven newsvendor problem seeks to optimize inventory decisions using samples from an unknown demand distribution. Although this problem has attracted significant attention, previous studies have typically analyzed specific distribution classes or regret definitions in isolation. In “Survey of the Data-Driven Newsvendor Problem: Unified Analysis and Spectrum of Achievable Regrets,” Chen and Ma present a unified analysis that synthesizes these settings and simplifies existing proofs. The study utilizes a notion of clustered distributions defined via the cumulative distribution function (CDF). This approach demonstrates that the achievable regret covers the entire spectrum of convergence rates between $1/\sqrt{n}$ and $1/n$. Beyond the theoretical unification, the authors show through simulations that this CDF-based notion accurately predicts the empirical regret and captures how the difficulty of the problem evolves with sample size. This work provides insights into understanding the value of data in the newsvendor problem and, more broadly, decision making under uncertainty.

The Need for Fees at a DEX: How Increases in Fees Can Increase DEX Trading Volume

2026-02-17T00:00:00+00:00

Management Science
We model endogenous trading and liquidity provision at a decentralized exchange (DEX) and demonstrate that increasing DEX trading fees can increase DEX trading volume. DEXs employ a mechanical pricing rule whereby price impacts decrease with inventory that DEXs acquire by offering fee revenues to investors. Consequently, higher DEX fees can incentivize higher inventory, thereby reducing price impacts. Moreover, the reduction of price impact can offset the increase in fees so that the marginal cost of DEX trading declines despite charging a higher trading fee. In turn, lower DEX marginal trading costs lead to an increase in DEX trading volume.This paper was accepted by Agostino Capponi, finance.

Acquirer Strategic Orientations, Integration Decisions, and Performance

2026-02-17T00:00:00+00:00

Journal of Management Studies
Integration decisions are not isolated, as they are embedded in an organizational context. Using a multi‐country sample (Nordics, German speaking Europe, and China) of small‐ and medium‐sized acquirers, we explore the influence of firm strategic orientations on how managers conceptualize acquisitions, make integration decisions, and impact acquisition performance. Both market‐ and entrepreneurial‐oriented firms coordinate activities following an acquisition, but they do so differently. Entrepreneurial‐oriented acquirers use human integration to align target managers with common goals and reinforce their decision‐making autonomy. In contrast, market‐oriented acquirers strive for functional integration and use human integration to reduce target firm managers' decision‐making autonomy. Thus, achieving coordination after an acquisition can follow different paths that are closely aligned with the strategic orientation of the acquirer. In other words, different strategic orientations guide managers' decisions, resulting in different paths to acquisition success.

Accounting Rules and the Labor Market for Accountants

2026-02-17T00:00:00+00:00

Journal of Accounting Research
In this study, I explore how accounting rules—in particular the restrictiveness of GAAP—have impacted the labor market for accountants. I find that when the rules become more restrictive, there are fewer students majoring in accounting and fewer accountants and auditors overall. The overall number of accounting positions that firms recruit for does not decrease when the rules become more restrictive; however, the nature of accountants' work changes. There is less focus on tasks such as applying judgment, thinking creatively, and thinking critically and more focus on determining compliance. Despite the decrease in accountants, earnings for accountants do not increase, and the wage distribution becomes more compressed. I supplement these analyses with a survey‐based field experiment and find that the salience of restrictiveness heightens students' views of accounting as a profession where they are unable to use creative and critical thinking. Overall, the findings suggest that restrictive regulation can shift the task content of occupations and reduce the pool of individuals interested in the profession.

Mediated Reference Point Shifts: Breaking Free of the Family DNA

2026-02-17T00:00:00+00:00

Entrepreneurship Theory and Practice
Institutional alignment comprises the integration of sanctioned organizational models and standardized managerial roles, thus reconstructing traditional organizations as contemporary actors with homogenous global identities. We adopt this perspective to illuminate the processes by which a business family homogenizes to conform to the global world order, through extensive use of advisors and management consultants. To do so, we narrate the experience of a third-generation Israeli-based multinational enterprise. We find that the cumulative adoption of standardized corporate organizational models periodically shifts the family’s decision-making reference points, enabling it to expand its product and geographic scope and act as a global player.

The Double-Edged Sword of Entrepreneurial Orientation: Product Recalls and the Role of COO Power

2026-02-17T00:00:00+00:00

Entrepreneurship Theory and Practice
This study examines how entrepreneurial orientation (EO) influences firms’ likelihood of product recalls. Integrating EO and upper echelons theory, we first argue that EO’s bold, variance-enhancing actions increase a firm’s product recall risk by diverting managerial attention away from quality control. Using 23 years of data on U.S. public firms, we find that EO increases recall likelihood. Second, we argue that this relationship is moderated by chief operating officer (COO) power, and the effectiveness of COO power is contingent on the firm’s product life cycle context. Empirical analyses support our theory and offer new insights about EO’s potential downsides.

Collision in the boardroom: Director skill interdependence and corporate entrepreneurship in technology‐intensive firms

2026-02-18T00:00:00+00:00

Strategic Management Journal
Board human capital theory posits that directors' skills shape firm behavior. Most studies, however, examine one skill type at a time, assuming that each director contributes independently of the other skills represented on the board. We introduce the concept ofdirector skill interdependence, theorizing that a director's influence depends on the skills of fellow directors and their committee assignments. Focusing on entrepreneurial directors in technology‐intensive firms, we find that they increase resource allocation toward corporate entrepreneurship (CE); however, this effect diminishes as the number of finance‐skilled directors increases, whether on the board or on its corporate development committee. These findings challenge the view of directors' skills as isolated inputs. Instead, the effects of directors' skills are contingent on the board's skill composition and committee structure.Managerial SummaryIn technology‐intensive firms, directors with entrepreneurial skills are often expected to stimulate corporate entrepreneurship (CE). Our findings suggest this relationship is less straightforward. While entrepreneurial directors do increase investment in CE, their impact weakens when directors with finance skills are prevalent on the board or its corporate development committee. These results underscore the importance ofdirector skill interdependence—the idea that a director's influence depends on the skills and roles of fellow directors. Boards should therefore consider not only who is appointed, but also how directors' skills align with one another and how those skills are deployed through committee assignments. Preventing the dominance of conflicting skill sets may enhance the board's ability to support innovation, venturing, and long‐term strategic renewal.

Latent Heterogeneity in the Marginal Propensity to Consume

2026-02-18T00:00:00+00:00

Review of Economic Studies
We estimate the unconditional distribution of the marginal propensity to consume (MPC) using clustering regression applied to the 2008 economic stimulus payments. By deviating from the standard approach of estimating MPC heterogeneity using interactions with observables, we can recover the full distribution of MPCs. We find households spent between 4 and 133% of the rebate within a quarter, and individual households used rebates for different goods. While many observable characteristics correlate individually with our estimated MPCs, most of these relationships disappear when tested jointly. Notable exceptions are income and the average propensity to consume, which correlate positively with the MPC. Household observable characteristics explain only 8% of MPC variation, highlighting the role of latent heterogeneity.

Coarse Bayesian Updating

2026-02-18T00:00:00+00:00

Review of Economic Studies
Studies have shown that the standard law of belief updating—Bayes' rule—is descriptively invalid in various settings. In this paper, I introduce and analyze a generalization of Bayes' rule—Coarse Bayesian updating—accommodating much of the empirical evidence. I characterize the model axiomatically, show how it generates several well-known biases, and derive its main implications in static and dynamic settings. Each axiom expresses a property of Bayes' rule but, conditional on the others, stops just short of making the agent fully Bayesian. The model employs standard primitives, making it suitable for applications; I demonstrate this by applying it to a standard setting of decision under risk, leading to a close relationship with the Blackwell information ordering and comparative measures of cognitive sophistication and bias.

Narratives about the Macroeconomy

2026-02-18T00:00:00+00:00

Review of Economic Studies
We study narratives about the macroeconomy—the stories people tell to explain macroeconomic phenomena—in the context of a historic surge in inflation. In our empirical analysis, we field surveys with more than 10,000 US households and 100 academic experts, measure economic narratives in open-ended questions, and represent them as Directed Acyclic Graphs. Households’ narratives are strongly heterogeneous, coarser than experts’ narratives, focus more on the supply than the demand side, and often feature politically charged explanations. Moreover, narratives shape how households form inflation expectations and interpret new information, which we demonstrate in a series of experiments. Informed by these findings, our theoretical analysis incorporates narratives into an otherwise conventional New Keynesian model and demonstrates their importance for aggregate outcomes through their effect on agents’ expectations.

Knowing Enough to Be Dangerous: The Problem of “Artificial Certainty” for Expert Authority When Using AI for Decision Making and Planning

2026-02-18T00:00:00+00:00

Organization Science
This study examines how experts who use advanced artificial intelligence (AI) technologies that generate highly detailed and realistic representations can create what we term “artificial certainty,” which we define as the illusion that complex future outcomes are definitively knowable, even though they are inherently uncertain. Through a comparative study of two urban planning organizations using the same AI simulation tool, we show how this artificial certainty emerges from the ways process experts create and deploy AI-generated representations. The findings reveal three interconnected representational practices that shape how laypeople perceive the certainty of a representation: controlling the level of detail, shaping stakeholder engagement, and constructing the model’s meaning. We find that when process experts emphasize enhancement—amplifying technological capabilities within these practices—stakeholders mistake representations for reality, undermining expert authority. Conversely, when process experts engage in modulation—tempering how AI outputs are presented and integrated into decision making—they preserve the authority necessary to keep uncertainty alive. These findings reconceptualize process expertise as a distinct form of interpretive work that helps maintain useful levels of uncertainty in the face of growing pressures toward artificial certainty. Based on these insights, we develop a critical distinction between representations of the future versus representations for the future, offering new ways to theorize decision making under uncertainty as organizations increasingly deploy sophisticated AI systems.Funding: This work was supported by the National Science Foundation [Grants SES-1057148 and SES-2051896].

Online Learning and Optimization for Queues with Unknown Arrival Rate and Service Distribution

2026-02-18T00:00:00+00:00

Operations Research
End-to-End Optimization for Queues Beyond PTOMost queueing optimization methods rely on a predict-then-optimize (PTO) paradigm, which first estimates key model primitives (e.g., demand rates and service-time distributions) and then optimizes system decisions using these estimates as if they were exact. In practice, however, queueing performance formulas are often highly sensitive to estimation errors, especially under congestion, making such approaches fragile and potentially misleading. In “Online Learning and Optimization for Queues with Unknown Arrival Rate and Service Distribution,” X. Chen, G. Hong, and Y. Liu develop a new end-to-end online learning framework for jointly optimizing pricing and capacity decisions in service systems, called LiQUAR (learning in queue with unknown arrival rate). LiQUAR is specialized to queueing systems by explicitly leveraging workload dynamics and transient behavior, allowing it to learn directly from arrival and service data without requiring prior knowledge of the demand function or the service-time distribution. Designing online learning algorithms for queues poses unique challenges: Data are temporally correlated, system dynamics are disrupted by policy updates, and performance is highly sensitive to congestion. LiQUAR addresses these challenges through queue-aware algorithm design and analysis, establishing regret bounds that capture the effect of traffic intensity and demonstrating superior performance to PTO and gradient-based reinforcement learning methods, particularly in heavily loaded systems.

Gender Gap in Debt Renegotiation

2026-02-18T00:00:00+00:00

Management Science
We study the role of gender in corporate debt renegotiation. Using in-court reorganizations from Brazil, we find that male-led creditors are more likely to reject plans put forward by female-led debtors. Although we cannot definitively identify the underlying mechanism driving this pattern, our findings align more consistently with gender-based discrimination compared with alternative explanations. This behavior may be costly: Female-led debtors deliver higher recoveries under reorganization than under liquidation, implying that rejections potentially create deadweight losses. Supporting a miscalibrated-beliefs explanation, we observe that the gender-based rejection gap narrows when male creditors have prior exposure to successful female-led businesses, pointing to a potential path for mitigation.This paper was accepted by Bo Becker, finance.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05802 .

The Impacts of “Baby-Friendly” Hospital Designations

2026-02-18T00:00:00+00:00

Management Science
This study considers a voluntary hospital certification program and how it shapes patient health outcomes. The Baby-Friendly Hospital Initiative designates participating hospitals as “Baby-Friendly” if they adhere to various practices thought to promote breastfeeding. We estimate the effect of being born in a Baby-Friendly hospital on breastfeeding initiation and infant health. Identification relies on a within-mother analysis that compares siblings who were born in the same hospital: one when the hospital was not certified and another when the hospital was certified. Data come from South Carolina birth certificates from 2010 to 2019 linked to subsequent emergency department records, hospital records, and death certificates. The results indicate that Baby-Friendly accreditation has no widespread effects on breastfeeding initiation. It does, however, increase breastfeeding rates among a subset of Black mothers with a history of bottle-feeding. Infants exposed to the designation are more likely to leave the hospital in under two days, and there is also some evidence of subsequent reductions in healthcare utilization in the next year. We do not find precise changes in all-cause one-year mortality, although we do observe a marginally significant reduction in deaths caused by “extreme immaturity.”This paper was accepted by Anita Carson, healthcare management.Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2024.06422 .

Eliciting Supplier Cooperation for Value Chain Decarbonization: A Field Experiment with Smallholder Farmers in India

2026-02-18T00:00:00+00:00

Management Science
Many firms are pledging to reduce greenhouse gas emissions across their value chains. However, this requires their suppliers to also adopt more climate-friendly practices for decarbonization. This can involve addressing gaps in not only the suppliers’ ability but also, their willingness to adopt such practices, which can be challenging if the suppliers perceive the practices as risky or potentially detrimental to their economic well-being. We examine the effectiveness of relational investments to help mitigate this challenge, arguing that such investments can serve as a signal of the firm’s commitment toward joint value creation. In a field experiment conducted with supplier farmers in a multinational firm’s agricultural supply chain in India, we examine the impact of two kinds of relational investments in providing the farmers with customized agricultural services that they valued. In the first intervention, the firm’s field officers offered the farmers support specific to the crop that the firm sourced from them. The second intervention additionally involved bringing in expert agronomists to also provide the farmers with support on broader agricultural matters of interest to them. Relative to a control condition in which the farmers only received training on the relevant climate-friendly practices, both interventions improved the farmers’ adoption of the recommended practices. The second intervention was more impactful than the first in improving the farmers’ practice adoption as well as their retention with the firm. Exploratory mediation analysis and post-experiment field interviews suggest that these findings are partly driven by the farmers’ positive perception of the firm’s relational investments.This paper was accepted by Anita McGahan, business strategy.Funding: The authors acknowledge financial support from INSEAD [Kurt Björklund Research Fund].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05744 .

Nudging Green-but-Slow Shipping Choices in Online Retail

2026-02-18T00:00:00+00:00

Management Science
Faster e-commerce fulfillment often comes at a cost to the environment because of energy-intensive transportation modes and more frequent, less consolidated last-mile deliveries. Despite these environmental implications, most retailers feel pressure to provide fast fulfillment to remain competitive. But what if consumers could be nudged to voluntarily choose green-but-slow shipping? In practice, a variety of information strategies are used to nudge such decisions, but it is unclear which strategy is best and why. This study examines these questions in two logistical contexts, each involving a different type of process change to support green-but-slow shipping: shipping mode and order consolidation. We first introduce a conceptual framework for selecting information strategies based on their influence on customer perceptions related to key stakeholders in each context: delivery convenience (self), environmental impact (society), and sustainability intention (retailer). Then, drawing on behavioral research and industry practices, we identify and test the effect of a comprehensive set of information strategies (i.e., Process, Green Label, Green Process, and Green Outcome) through controlled experiments involving 3,800 participants. Experimental results suggest that different logistical contexts require distinct nudging strategies, aligning with our conceptual framework. We find that including a green label is particularly effective for the shipping mode context, especially when combined with outcome information. In contrast, process information is more promising in the order consolidation context. Also, adding financial incentives can enhance the effectiveness of information strategies without a clear environmental signal, whereas pairing them with a simple green label may be effective for low eco-conscious individuals.This paper was accepted by Elena Katok, operations management.Funding: This work was supported by the Institute on the Environment, University of Minnesota [MF-0009-23].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00239 .

Can Autonomous Vehicles Solve the Commuter Parking Problem?

2026-02-18T00:00:00+00:00

Management Science
This paper investigates how autonomous vehicles (AVs) may reshape morning commute travel patterns and impact parking in a central business district (CBD). We develop a continuous-time game-theoretic traffic model that takes into account key economic deterrents to driving, such as parking fees and traffic congestion, and characterize the departure time and parking location (inside or outside the CBD parking area) patterns of commuters in equilibrium. Our analysis shows that all AV commuters may choose to park outside the CBD in equilibrium, increasing both vehicle hours and vehicle miles traveled as compared with the case with all human-driven vehicles. This change increases the total system cost and suggests a potential for CBD land use changes (e.g., repurposing CBD parking spots as commercial and residential areas) after the mass adoption of AVs. To reduce the total system cost, an urban planner may regulate commuters’ decisions by adjusting parking fees and/or imposing congestion tolls as a short-term measure, or adjusting infrastructure, for example, converting CBD parking spaces to drop-off spots for AVs. Our results indicate that these measures can reduce the total system cost substantially (e.g., up to [Formula: see text] when calibrating our model to data from Pittsburgh).This paper was accepted by Chung Piaw Teo, optimization and decision analytics.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01213 .

Qualitative Research on Incumbents’ Responses to Discontinuous Technologies: Distilling an Integrative Framework of Context

2026-02-18T00:00:00+00:00

Journal of Management
Scholars have long and effectively used qualitative-inductive methodologies to understand the heterogeneous responses of established organizations—so-called incumbents—to emerging discontinuous technologies. However, after nearly 30 years of nuanced, contextualized qualitative research in this area, there is a unique opportunity—and essentially a need—for its distillation, integration, and critical reflection. In particular, while qualitative studies on heterogeneous incumbent responses aspire to develop “theories of the middle range,” and, thus, to acknowledge boundary conditions and contextual nuances, in practice, they rarely discuss a given theory’s range systematically. This disconnect limits the comparability, generalizability, and integration of findings. To address this instability, we inductively and critically review 127 qualitative studies on incumbents’ responses to discontinuous technologies published between 1998 and 2024. The central outcome of our review is an integrative framework of core contextual dimensions of incumbent responses to emerging discontinuous technologies, organized along six attributes related to the overarching domains of technology, market, and organization and institutions. Our distilled framework provides a taxonomic map for systematically comparing different empirical contexts of incumbent adaptation to discontinuous technologies and critically considering the boundary conditions of qualitatively induced theorizing in this regard. Our framework also enables us to present an encompassing program for future qualitative research on incumbent heterogeneity, one of the core phenomena underlying the overall process of creative destruction.

From Shield to Sword: How Data Privacy Can Undermine Data Security

2026-02-18T00:00:00+00:00

Information Systems Research
What is the point in hacking computer systems when organizations voluntarily disclose personal data to anyone who asks convincingly? We show that the European GDPR is paradoxically exploitable for identity theft despite being designed to protect personal data. Subject access requests (SARs) according to its “right of access” (Article 15) can be weaponized by impersonating a victim and submitting fraudulent SARs in their name. We task attackers with stealing the personal data of three volunteers (highly privacy aware person, average user, and semipublic figure) in a real-world setting. These attacks could be replicated by just about anyone. Yet, they obtained sensitive personal data, including addresses, phone numbers, national ID and bank account information, and insurance data. Based on 718 submitted SARs and 21 interviews with data protection officers, we tell a frightening, yet fascinating story of how these identity thefts unfold, expose flaws in how organizations process SARs, and uncover a systemic weakness in the GDPR. We analyze the underlying factors enabling such attacks, assess their real-world impact, and explore mitigation options for individuals, organizations, and lawmakers. Our insights have important implications for how data privacy and data security interrelate and how we manage and regulate them.

The Disquiet of Quiet Quitting: Definitional Clarity, Theoretical Pathways, and Future Research

2026-02-18T00:00:00+00:00

Human Resource Management
Quiet quitting (QQ) has emerged as a prominent topic in both popular press and academic research, reflecting shifts in employees' engagement, effort allocation, and responses to contemporary work pressures. This review synthesizes findings from 11 papers published in a recent Special Issue onThe Disquiet of Quiet Quitting. We integrate conceptual, empirical, and methodological insights from these papers and other recent literature to clarify what QQ is and what it is not. We highlight the multidimensional nature of QQ, distinguishing deliberate and passive forms, reactive versus value‐driven motivations, and variations in scope and behavioral expression. We then propose a 2 × 2 typology of quiet quitters (Protesters, Faders, Boundary Setters, and Indifferent Drifters) constructed along two key dimensions, intentionality and motivational basis, to capture the heterogeneity of behaviors and underlying motives. Finally, we discuss implications for theory, measurement, and practice, emphasizing how QQ signals broader dynamics in employment relationships, including fairness, well‐being, and sustainable engagement, and we identify directions for future research, including longitudinal, multi‐level, and cross‐cultural investigations.

The Macroeconomic Impact of Climate Change: Global Versus Local Temperature

2026-02-19T00:00:00+00:00

The Quarterly Journal of Economics
This paper estimates that the macroeconomic damages from climate change are an order of magnitude larger than previously thought. Exploiting natural global temperature variability, we find that 1○C warming reduces world GDP by over 20% in the long run. Global temperature correlates strongly with extreme climatic events, unlike country-level temperature used in previous work, explaining our larger estimate. We use this evidence to estimate damage functions in a neoclassical growth model. Business-as-usual warming implies a present welfare loss of more than 30%, and a Social Cost of Carbon in excess of $1,200 per ton. These impacts suggest that unilateral decarbonization policy is cost-effective for large countries such as the United States.

Time Variation in Extrapolation and Anomalies

2026-02-19T00:00:00+00:00

Management Science
We find that the degree of extrapolative weighting in investors’ beliefs (DOX) has strong predictive power for a broad set of overreaction-related anomalies in the stock market. The average return spread of these anomalies is about [Formula: see text] per month following high DOX periods and [Formula: see text] per month following low DOX periods. In sharp contrast, DOX has opposite, but weaker, predictive power for underreaction-related anomalies. In addition, the predictive power of DOX is robust after controlling for a broad set of economic forces. Moreover, most of the DOX effect on long-short anomaly returns derives from the short legs of these overreaction-related anomalies, suggesting that time variation in DOX leads to more time variation in overpricing than in underpricing, probably because of short-sale impediments.This paper was accepted by Lukas Schmid, finance.Funding: Z. Su received financial support from Lingnan University [Faculty Research Grants DB25A5 and 103664]. Y. Wang received financial support from the National Natural Science Foundation of China [Grant 72503264]. J. Yu received financial support from the National Natural Science Foundation of China [Grants 72141304 and 72342020].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.06850 .

Transaction Sequencing and House Price Pressures

2026-02-19T00:00:00+00:00

Management Science
We use a unique data set of individual housing transaction histories from Norway and a novel shift-share design to show that temporary changes in the transaction sequencing decisions of moving homeowners—whether to buy first and then sell or vice versa—cause temporary local increases in house prices and seller liquidity. Our findings are consistent with a simple theory of how transaction sequencing decisions impact house prices in a frictional housing market. We also provide the first causal estimate of the elasticity of house prices to market tightness, which is around 0.1. Overall, our findings highlight the importance of trading frictions and supply-demand imbalances for housing market dynamics.This paper was accepted by Bo Becker, finance.Funding: This work was supported by Norges Forskningsråd [Grant 274678: Frictions in the Housing Market].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.08149 .

In Search of Shares: Benchmarked Ownership, Short Covering, and Price Efficiency

2026-02-19T00:00:00+00:00

Management Science
We examine whether ownership by benchmarked investors constrains short sellers’ ability to quickly cover their positions following positive earnings news and thereby exacerbates price pressure from short covering. We find that high benchmarked ownership is related to greater price overshooting in highly shorted stocks at positive earnings announcements. This effect is driven by benchmarked ownership amplifying both price and volume impacts of short covering. Exploring shocks from Russell index reconstitutions and examining other settings such as management guidance and analyst recommendation revisions yield similar inferences. Overall, our findings highlight a novel mechanism that benchmarked ownership can constrain short-selling activities.This paper was accepted by Eric So, accounting.Funding: The authors acknowledge financial assistance from their respective schools.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.07231 .

Bond Risk Characteristics and Factor Risk Premia

2026-02-19T00:00:00+00:00

Management Science
Assuming no arbitrage and that a polynomial in the time to maturity approximates spot-rate increments, we develop a consistent approach to estimate factor risk premia in the bond market. This approach is attractive, as the underlying risk factors are the time-varying coefficients of the polynomial specification, and the factor loadings correspond to familiar bond risk characteristics, such as duration and convexity. Our two-factor model—with a positive (negative) duration-factor (convexity-factor) risk premium—is easy to estimate and provides straightforward economic intuition for the concave term structure of bond risk premia and the downward-sloping term structure of bond Sharpe ratios. Our model decomposes the conditional factor risk premia into a “status quo” component—the expected factors’ realizations when expected spot-rate increments are zero—and the factors’ forecasts. The status quo factor risk premia provide measures of the state of fixed income markets that both outperform alternative measures and are easily estimated in real time; moreover, the underlying risk factors and their loadings are familiar to and readily interpreted by investors and traders.This paper was accepted by Lukas Schmid, finance.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00212 .

High-investment human resource practices and firm performance in the context of national education systems and labor markets: A cross-national meta-analysis

2026-02-19T00:00:00+00:00

Journal of International Business Studies
Organizations—especially multinational corporations (MNCs)—do not always realize comparable performance gains from investing in people across national borders. Prior research has attempted to explain such variation in the effects of high-investment human resource practices (HIHRPs) by emphasizing their fit with national cultural values, yet empirical support for this cultural fit perspective has been limited. To address this gap, we focus on national institutional factors that more directly shape how HIHRPs influence performance. Drawing on human capital resources (HCR) theory and institutional perspectives in International Business (IB), we identify two institutional conditions—national education quality and labor market flexibility—that moderate HIHRP effectiveness. We theorize that high-quality education systems enhance the HIHRPs–performance relationship by supplying workers with strong general human capital and that this effect is amplified in flexible labor markets where employees are motivated to contribute. A meta-analysis of 1,285 effect sizes from 170,938 organizations across 29 countries offers nuanced evidence: HIHRPs were more effective when national education quality and labor market flexibility were both highorboth low than when they were mismatched. These findings advance comparative and strategic HRM research and guide organizations about how comprehensively or selectively to invest in managing people in what institutional contexts.

NFT Disruption in Platform Competition: Evidence from Trading Card Collectibles

2026-02-19T00:00:00+00:00

Information Systems Research
The rise of blockchain-based platforms is reshaping how digital and physical products interact, with important consequences for platform strategy and market regulation. Evidence from the introduction of NBA Top Shot shows that digital collectibles can directly reduce prices, sales volume, and market value in adjacent physical markets, indicating a clear cannibalization effect. However, the impact is highly uneven: Physical products with close digital substitutes and low digital scarcity become especially vulnerable, whereas low-cost items in certain segments can experience market expansion as new collectors enter through digital channels. These patterns demonstrate that digital transformation alters competition not only between platforms but also across product categories, making strategic decisions around scarcity, product design, and release timing critical for firms managing multiformat ecosystems. For practice, the findings highlight the need for companies to anticipate cross-market spillovers when launching digital offerings and to manage physical and digital product lines in an integrated way. For policy, the results underscore the importance of establishing transparent standards for digital collectibles and providing clear consumer guidance, while recognizing that many NFTs function as collectible goods rather than financial securities and may warrant distinct regulatory treatment.

Perceived Pain or Gain: Role Identity, Gratification, and the Well-Being of Hybrid Entrepreneurs

2026-02-19T00:00:00+00:00

Entrepreneurship Theory and Practice
Hybrid entrepreneurship—pursuing a venture while maintaining paid employment—can enhance or undermine well-being. Based on a longitudinal, qualitative study, we identify two distinct trajectories shaped by envisioned future selves: delayed entrepreneurial gratification with cumulative strains on well-being, and present gratification with supportive effects on well-being. Drawing on role identity theory, we theorize how role internalization and identity centrality relate to the well-being experiences of hybrid entrepreneurs and introduce gratification and rationalization as ways to handle strains on well-being. Our findings offer a deeper understanding of the divergent well-being experiences in hybrid entrepreneurship.

Interpreting Violence: How Community Context Shapes Corporate Responses to Street Protests

2026-02-19T00:00:00+00:00

Administrative Science Quarterly
Violence that erupts in communities invites complex interpretations that can create a dilemma for business leaders about how to respond. Investigating how organizations respond to violence in protests, we build on the community embeddedness literature and propose that business leaders’ responses to protest violence depend on their perceptions of whether the violence is justified. Leaders may view reported protest violence as evidence of either social disorder or a valid grievance in the community where the violence occurs and where their companies are headquartered. We theorize that business leaders’ interpretations of violence are influenced by their community’s recent history: A history of protest violence unrelated to the social cause underlying the current protest weakens the managerial perception that the social issue is relevant to the community, while a history of grievance-validating events strengthens this perception. Using hand-collected data on corporate announcements following the 2020 Black Lives Matter (BLM) movement, we find that firms are less likely to announce diversity actions in response to reported protest violence in communities marked by persistent violence in previous non-BLM protests, but they are more likely to do so in communities with records of more police shootings. These same community conditions also shape the effect of violence on whether firms decide to publicly endorse the movement when they announce their corporate actions. Demonstrating that violence reshapes the ways that organizations respond to protests, we discuss the implications of our findings for research on violence, social movements, and corporate activism.

Payment for Order Flow and Option Internalization

2026-02-20T00:00:00+00:00

The Review of Financial Studies
Option wholesalers specialize in purchasing and executing against retail option order flow. Orders are internalized via auctions (which provide price improvement) and the limit order book. Designated market makers (DMMs) have a key advantage in internalizing limit order book trades: they obtain the first five contracts of any order they bring to an exchange where they are a DMM. We exploit variation in DMM assignments and allocation rules to highlight how these rules create a barrier to entry in option wholesaling that does not exist for equity wholesaling, protecting wholesaler profits and high option PFOF.

Do The Effects of Nudges Persist? Theory and Evidence from 38 Natural Field Experiments

2026-02-20T00:00:00+00:00

Review of Economic Studies
We formalize a research design to uncover the mechanisms underlying long-term reductions in energy consumption caused by a widely implemented nudge. We consider two channels: technology adoption and habit formation. Using data from 38 natural field experiments, we isolate the role of technology adoption by comparing treatment and control homes after the initial resident moves, which discontinues the treatment for a home. We find that fully half of energy reductions persist in the home after treatment ends and show this persistence is consonant with a technology adoption channel. The role of technology in creating persistent behaviour change has important implications for designing behavioural interventions and evaluating their long-term social impacts.

EXPRESS: Effects of Peer Voting and Followers on User Contribution to Online Knowledge Sharing Platforms: Evidence from a Field Experiment

2026-02-20T00:00:00+00:00

Production and Operations Management
Online knowledge-sharing platforms such as Quora and Zhihu (a leading knowledge-sharing platform in China) often use both peer voting and followers to encourage user knowledge contributions. However, these platforms diverge in whether they highlight followers, upvotes, or both as reputation symbols. To better understand the consequences of these platform design choices, we conduct a field experiment with 1,696 focal users on Zhihu where we exogenously increase upvotes or/and followers for treated users over a 53-day intervention period. We monitor focal users’ activities for 303 days, covering both pre- and post-intervention periods. We further use a large language model to gauge the quality of 12,998 answers contributed by these users during this time. We find that increasing upvotes significantly boosts users’ answer contributions (in volume, total length, and quality), while increasing followers has mostly no overall effect on contributions. Additionally, increasing upvotes can encourage contribution for up to 100 days, particularly among lower-reputation (i.e., with fewer upvotes or followers), less active, female users, and those answering soft topic questions. In contrast, increasing followers only reduces contribution volume (not length or quality) for higher-reputation, more active, and male users. Moreover, while peer voting primarily only affects answer contributions, the follower treatment leads to negative spillovers, reducing users’ upvoting on fellow users’ answers, following fellow users, or purchasing Zhihu Lives hosted by fellow users. Our research suggests the possibility that users on knowledge-sharing platforms may associate peer voting with contributions, treating it as peer recognition that offers intrinsic motivation. In contrast, users may interpret additional followers as a symbol of status enhancement, which could, in some cases, dampen their motivation to contribute. Overall, our study suggests that platforms aiming to foster active, high-quality contributions should highlight upvotes rather than followers as a reputation symbol. To our knowledge, this study is among the first field experiments to identify and compare the causal effects of peer voting and followers on user contribution to knowledge-sharing platforms.

EXPRESS: Raise the Expectation Bar and Lower the Tolerance? Effects of Being Listed in a Restaurant Guide on Customers’ Online Rating Behavior

2026-02-20T00:00:00+00:00

Production and Operations Management
Creating curated guides or lists (e.g., Top 100 Places to Eat) is a common operational strategy employed by platforms as part of their reputation systems to assist customers in decision-making. Prior research has largely highlighted the effects of such guides on customer word-of-mouth (WOM) volume and valence. Moving beyond these aggregate WOM metrics, we extend this stream to examine how being listed in a guide (BLG) influences customers’ likelihood of providing ratings and rating composition across valence types (compliments, complaints, and neutral ratings), drawing on the zone of tolerance (ZOT) framework. Using large-scale empirical data, we find that BLG increases customers’ likelihood of rating but reduces overall valence, with a higher proportion of complaints and neutral ratings and a lower proportion of compliments. The effect is particularly pronounced among merchants with additional high-quality signals (e.g., higher prices, greater popularity, and superior prior ratings). Evidence from both observational data and a randomized experiment further reveals the underlying mechanisms, showing that BLG increases customer involvement and raises desired expectations more dramatically than adequate ones. These triangulated findings contribute to the literature by providing in-depth insights into how and why BLG affects customer rating behaviors and thus, merchants’ WOM. Our findings underscore the importance of strategic operations management for platforms when designing and managing their reputation systems, with significant implications for technology management.

Fair Fares for Vehicle Sharing Systems

2026-02-20T00:00:00+00:00

Operations Research
Vehicle sharing systems—such as bike, scooter, and car sharing—play a key role in urban transportation, yet algorithmic pricing can lead to inequalities across locations. This paper studies how platforms can incorporate fairness into pricing decisions and the resulting outcomes. We propose two notions of fairness: price fairness, which limits price differences across locations, and access fairness, which equalizes the proportion of demand at each location that has access to the system, determined by affordability and availability. Using a stylized two-location model, the paper analyzes how fairness constraints affect platform revenue, consumer surplus, and social welfare. Although price fairness can sometimes increase consumer surplus at both locations, access fairness always reduces consumer surplus at both locations. A convex relaxation approach is developed for larger networks, and a case study using New York City data illustrates the tradeoffs between fairness and efficiency in practice.

Evaluating the Efficacy of Providers’ Compensation Contracts in Improving Participant Retention for Clinical Studies

2026-02-20T00:00:00+00:00

Management Science
In this work, we aim to analyze a clinical study sponsor’s decisions regarding monetary payments to participants and compensation for providers (investigators and coordinators) for their efforts to improve participant retention in the study. To this end, we first consider a centralized model where the sponsor decides the monetary payments to participants and the providers’ efforts. We then identify the optimal contracts for the providers under the two decentralized team structures: the sponsor-investigator (SI) model and the outsourcing (OM) model. We further analyze three widely adopted compensation contracts for the providers: fixed (FC), linear (LC), and conditional linear (CLC) given a decentralized structure. Our theoretical analysis shows that the expected retention cost with optimal contracts under decentralized structures is at most 40% higher than that under the centralized model. However, in practical instances, this cost increase is, at the most, 8% on average. A comparison of the FC, LC, and CLC contracts reveals that it is sufficient for the sponsor to choose between the FC and the LC contracts under the SI model, whereas under the OM model, there exist cases where the sponsor is better off adopting the CLC contract. Furthermore, the sponsor’s expected retention cost when choosing the best of the three contracts is at most 6% (10%) higher on average relative to that for the optimal compensation contract under the SI (OM) model. Given a decentralized structure, we also identify cases where the optimal contract offers significant benefit over the three contracts observed in practice.This paper was accepted by Elena Katok, operations management.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02973 .

Micro‐Processes of Constrained Innovation: A Field Study of Constraint‐Handling Practices in Base of the Pyramid Innovation Projects

2026-02-20T00:00:00+00:00

Journal of Management Studies
This study addresses the problem of handling constraints in innovative projects. Using a qualitative research design on base of the pyramid (BOP) innovation projects, we examine how creativity emerges from constraints. By investigating responses to experienced constraints from 60 BOP innovation projects, we identify specific micro‐processes used to deal with constraints, which we introduce as constraint‐handling practices. Moreover, we investigate the interplay between these constraint‐handling practices and the nature of the constraints in the resultant sequence of such micro‐processes, distinguishing between goal and task constraints. Drawing from theory on effectuation and causation, we explain how different constraint‐handling practices are implemented in innovation projects in response to these constraints. We demonstrate that instead of relying on an either/or mode of effectuation versus causation, actors in constrained BOP innovation projects exhibit repeated cycles of both effectuation‐ and causation‐based constraint‐handling practices.

Banking system stability: a global analysis of cybercrime laws

2026-02-20T00:00:00+00:00

Journal of International Business Studies
We examine the role of cybercrime legislation globally in shaping the stability of the banking system. We compile a novel dataset covering the enactment of cybercrime legislation in 132 developed and developing countries to test this research question empirically. We find that the enactment of cybercrime laws enhances the stability of the banking sector. This key finding holds across a comprehensive suite of robustness tests, including alternative measures of bank stability and model specifications. We document significant cross-sectional heterogeneity, with the effect being more pronounced in countries with heavier penalties for illegal cyber activities and legal frameworks that hold banks accountable for their cybersecurity practices. Moreover, the positive impact is stronger in jurisdictions with greater international legal cooperation and effective enforcement mechanisms. We further investigate two channels (i.e., funding liquidity and operational risk) through which cybercrime laws may influence bank stability. Our results indicate that these laws can significantly bolster bank stability by enhancing funding liquidity and mitigating operational risk. Overall, our study provides evidence of the crucial role of cybercrime legislation in fostering a secure and resilient banking environment. It offers new insights into how these laws contribute to bank stability on both individual and systemic levels.

AI Governance and the Decentralization of Technology Production: An Investigation of AI-Based IPA Bots

2026-02-20T00:00:00+00:00

Information Systems Research
We revisit the centralization–decentralization tension in the context of decentralized technology production at the AI frontier, focusing on Intelligent Process Automation (IPA) bots as a salient manifestation of the democratization of AI. IPA bots combine robotic process automation with AI technologies and process mining based on deep, mindful domain expertise. We collaborate with a Fortune 200 multinational to study how IPA projects yield successful governance outcomes of utilization and repeatability. Our research reveals that traditional centralized mandates, when paired with the unique learning and adaptive capabilities of AI systems, can actively suppress utilization and stifle organizational reuse of the bots. Conversely, democratization is no panacea. While empowerment of business users through decentralization usually leads to successful outcomes for the deployed bots, when certain boundary conditions are crossed, these same decentralized efforts can produce fragmentation in the form of one-off solutions. Our research challenges core tenets of the IT governance canon, demonstrating that centralization–decentralization logic at the AI frontier must expand to account for the evolution of governance, process, and technological characteristics to realize effective AI governance.

How Signals of Silence Sustain Sexual Harassment and What to Do About It

2026-02-20T00:00:00+00:00

Human Resource Management
Sexual harassment has persisted for decades as an open secret within organizations, creating an ongoing challenge for Human Resource practitioners. Many employees experience or witness harassment yet say nothing. When they contemplate complaining, they are discouraged from doing so. Some still muster the courage to speak out about these abuses, but find their complaints ignored, downplayed, or dismissed by those in charge. Building on prior research, we propose that these practices add up to signals of silence, which we conceptualize and empirically operationalize. Drawing on social information processing theory, we explicate how these signals help sustain and perpetuate sexual harassment. We further argue that supervisors can counteract these harmful signals of silence by modeling ethical leadership. We investigated these ideas with two sets of studies comprising seven independent samples. In Study 1 (Ntotal= 2649 participants), Phases 1 through 5, we developed and validated a higher‐order aggregate measure of harassment signals of silence (Harassment SOS scale) comprising three interrelated elements: being silent, silencing others, and not listening. In Study 2 (Ntotal= 1111 participants), we used field samples collected from two North American police departments to test the relationship between signals of silence and experiences of harassment, along with the role of ethical supervision in mitigating the harmful effects of silence. We discuss the implications of these findings for research and practice, including the implementation of relevant HR policies and practices.

Damned If You Do, Damned If You Don’t: How Counterfactuals Shape Early-Stage Investment Decision-Making

2026-02-21T00:00:00+00:00

Entrepreneurship Theory and Practice
This study explores the role of investors’ counterfactual thinking regarding errors of commission and omission and how these relate to willingness to invest in new ventures. Drawing from behavioral economics and psychology, we focus on the salience of prospective counterfactuals at the screening stage. Our experimental findings demonstrate that increasing the salience of commission-related counterfactuals, particularly social counterfactuals, significantly reduces investors’ willingness to invest. This effect underscores the influence of anticipated regret, social comparison, and emotional responses in shaping investment behavior. By contrast, omission-related counterfactuals show no measurable impact, suggesting a prevailing not-investing norm and limited effect of potential fear of missing out among new-venture investors. Our results provide a deeper understanding of how investors navigate the tension between avoiding missed opportunities and minimizing losses, emphasizing the salience of situational triggers as opposed to stable traits or market-wide factors. These insights highlight the importance of counterfactual thinking as a theoretical lens for studying decision-making in the high-risk, high-reward context of new-venture investing.

The Benefits of Access: Evidence from Private Meetings with Portfolio Firms

2026-02-22T00:00:00+00:00

The Journal of Finance
We use large language models to analyze the content of 4,700 private meetings between a large active asset manager and its portfolio firms. The high‐level meetings convey mostly soft information about the firm, and little about industry or market. Fund manager meetings focus on business models and financial metrics, while governance specialist meetings focus on environmental, social, and governance risks; 0.4% of meetings discuss material nonpublic information. Trades by fund managers increase with meetings attended by senior management, rated as unusually good or bad, where the tone is significantly positive or negative, or assessed as creating consensus. Meeting‐informed portfolios can generate significant outperformance.

Why Does Large Vertical Pay Dispersion Increase Turnover Among Both Employees and Senior Managers?

2026-02-22T00:00:00+00:00

Human Resource Management
This study examines the complex relationship between vertical pay dispersion (i.e., pay disparities across different organizational levels) and employee turnover by integrating insights from tournament theory and equity theory. While vertical pay dispersion is designed to incentivize career advancement, we argue it can simultaneously elevate turnover at all levels by fostering competition and inequity. Based on data collected from 302 firms in the Great Bay Area (Guangdong Province in Mainland China, Hong Kong, and Macao), our results show that greater vertical pay dispersion increases turnover for senior managers as well as lower‐level managers and employees. Inconsistent with tournament logic, we find that the detrimental effect of vertical pay dispersion on turnover is not significantly weaker for senior managers who benefit from the dispersion than for lower‐level managers and employees. Drawing on equity theory, we further investigate the moderating role of high‐investment human resource systems (HIHRS), which reflect an organization's fair treatment of employees. Results support the hypothesis that the coexistence of high vertical pay dispersion and high HIHRS within a firm increases both employee and senior manager turnover, as these practices send conflicting signals about organizational priorities and equity. These findings contribute to compensation literature by unfolding the contingent and differential effects of pay structures on turnover across hierarchical levels and organizational contexts.

Selection in Surveys: Using Randomized Incentives to Detect and Account for Nonresponse Bias

2026-02-23T00:00:00+00:00

Review of Economic Studies
We show how to use randomized participation incentives to test and account for nonresponse bias in surveys. We first use data from a survey about labour market conditions, linked to full-population administrative data, to provide evidence of large differences in labour market outcomes between survey participants and nonparticipants, differences which would not be observable to an analyst who only has access to the survey data. These differences persist even after correcting for observable characteristics. We then use the randomized incentives in our survey to directly test for nonresponse bias and find evidence of substantial bias. Next, we apply a range of existing methods that account for nonresponse bias and find they produce bounds (or point estimates) that are either wide or far from the ground truth. We investigate the failure of these methods by taking a closer look at the determinants of participation, finding that the composition of participants changes in opposite directions in response to incentives and reminder emails. We develop a model of participation that allows for two dimensions of unobserved heterogeneity in the participation decision. Applying the model to our data produces bounds (or point estimates) that are narrower and closer to the ground truth than the other methods. Our results highlight the benefits of including randomized participation incentives in surveys. Both the testing procedure and the methods for bias adjustment may be attractive tools for researchers who are able to embed randomized incentives into their survey.

Monetary Policy and Sovereign Risk in Emerging Economies (NK-Default)

2026-02-23T00:00:00+00:00

The Quarterly Journal of Economics
This paper develops a New Keynesian model with sovereign default risk. Inflation is set by forward-looking firms, monetary policy is an interest rate rule, and the fiscal government borrows externally, long-term, with an option to default. In this framework, default risk creates inflation pressures through an expectations channel, and tight monetary policy disincentivizes fiscal overborrowing. The model sheds light on temporary inflation events in emerging-market data: short-lived spikes in inflation, spreads, and domestic policy rates. As spreads rise, firms increase their prices in expectation of higher future inflation and low consumption during default. Monetary policy tightens, which reduces inflation and helps bring spreads down by disciplining government borrowing. These monetary-fiscal interactions imply that delivering the flexible prices allocation may not be optimal for monetary policy.

EXPRESS: Getting to the Green: Should a Profit-Maximizing Firm Buy Carbon Offsets or Invite Consumers to Buy Them?

2026-02-23T00:00:00+00:00

Production and Operations Management
Many firms, such as American Airlines, Patagonia, Google, and Apple, have publicly stated their goal of becoming carbon neutral at some point in the future. These firms are pursuing multiple emissions reduction initiatives within their value chains, such as the use of renewable energy, zero-emissions vehicles, and low-carbon materials and supplies. But despite these efforts, some residual emissions cannot be further reduced, necessitating the purchase of carbon offsets, which increase firms’ costs. While environmentally conscious consumers may be willing to pay a higher price for a low-emissions product or service, a significant segment of climate change–disengaged consumers is not willing to do so, as demonstrated by multiple studies. In this paper, we identify when it is optimal for a profit-maximizing firm to offer its consumers carbon-reduction offsets for purchase with the product, in addition to potentially purchasing offsets at the firm level. Through a stylized analytical model, we show that it is not optimal for firms to both buy offsets at the firm level and offer them to consumers for purchase at the same time. Rather, when the offset cost is low enough, the firm buys enough offsets to compensate for all of its emissions at the firm level, resulting in a higher overall product price; when the offset cost is in the middle range, the firm offers offsets to consumers for purchase at an offset price lower than offset cost, ensuring that green consumers buy offsets; and when the offset cost is high, offsets cannot be optimally used in any way. The thresholds depend on the green-segment size and disutility from emissions (at the consumer-consumption and firm levels), and on the product’s own carbon footprint. Providing offsets for purchase to consumers allows consumers to self-select into their preferred product/price bundle, thus providing the firm with a market-segmentation and price-discrimination mechanism that increases profits and reduces the firm’s carbon footprint.

EXPRESS: Predictive Hotspot Mapping for Data-driven Crime Prediction

2026-02-23T00:00:00+00:00

Production and Operations Management
Predictive hotspot mapping is an important problem in crime prediction and control. An accurate hotspot mapping helps in appropriately targeting the available resources to manage crime in cities. With an aim to make data-driven decisions and automate policing and patrolling operations, police departments across the world are moving towards predictive approaches relying on historical data. In this paper, we create a non-parametric model using a spatio-temporal kernel density formulation for the purpose of crime prediction based on historical data. The proposed approach is also able to incorporate expert inputs coming from humans through alternate sources. The approach has been extensively evaluated in a real-world setting by collaborating with the Delhi police department to make crime predictions that would help in effective assignment of patrol vehicles to control street crime. The results obtained in the paper are promising and can be easily applied in other settings. We release the algorithm and the dataset (masked) used in our study to support future research that will be useful in achieving further improvements.

A Note on Piecewise Affine Decision Rules for Robust, Stochastic, and Data-Driven Optimization

2026-02-23T00:00:00+00:00

Operations Research
Sharper Approximation for Multistage Decisions Under UncertaintyIn “A Note on Piecewise Affine Decision Rules for Robust, Stochastic, and Data-Driven Optimization,” Thomä, Schiffer, and Wiesemann revisit piecewise affine decision rules, a widely used approximation for multistage stochastic programs. Building on the framework of Georghiou et al. (2015), the authors propose an algorithmic refinement that yields strictly better policies in stochastic settings while retaining tractability. Beyond stochastic programming, the framework naturally extends to multistage robust optimization and to modern data-driven models based on Wasserstein ambiguity sets. The paper shows how the resulting policies can be computed efficiently and provides numerical evidence demonstrating consistent performance improvements.

Bargaining and Network Effects in Two-Sided Platforms: Evidence from Online Healthcare

2026-02-23T00:00:00+00:00

Marketing Science
A generalizable modeling framework integrating bargaining and participant-specific network effects to guide growth strategies for platforms built on negotiated partnerships.

Quality Disclosures and Disappointment: Evidence from the Academy Nominations

2026-02-23T00:00:00+00:00

Management Science
This study examines the unintended consequences of quality disclosures, focusing on how Academy Award nominations impact consumer satisfaction in the movie industry. Awards and certifications typically signal high quality and increase consumer expectations. Yet, if the experience falls short of the expectation, they may also lead to disappointment. Using a novel data set from MovieLens, we analyze user ratings for movies surrounding Academy Award nominations from 1995 to 2019. We first implement a difference-in-differences strategy comparing nominated and non-nominated films and then introduce a novel recommendation-based matching approach that leverages vector representations of user preferences trained prior to the nominations. Our analysis removes taste-based selection and isolates changes in user experience: users who rate a movie after its nomination assign significantly lower ratings than similar users who rated the same film earlier. This effect accounts for more than 7% of the prenomination rating gap between nominated and non-nominated films and is most pronounced among less experienced users. Our findings are validated with data from IMDb, where the effect is even more pronounced, likely reflecting differences in the composition of the user base across platforms. Additional textual analysis of user-generated content on both platforms provides further evidence that the postnomination decline in ratings is driven by disappointment, rather than disinterest, deteriorating viewing conditions, or snob effects associated with mainstream popularity.This paper was accepted by Duncan Simester, marketing.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.09016 .

Start-Up Firms and Corporate Culture: Evidence from Advertised Corporate Culture

2026-02-23T00:00:00+00:00

Management Science
I document the advertised corporate culture among start-up firms from an online job board. Two corporate culture types emerge: one that concerns the well-being of employees (worker-centered culture) and another that emphasizes other values, such as customers, firms, and markets (firm-centered culture). The worker-centered culture attracts 20% more applications than the other culture type. Firms advertising the worker-centered culture pay 5% lower salaries than measurably similar jobs. Outcomes do not significantly differ among firms that promote different types of corporate culture. To assess whether a worker-centered culture is adopted to reduce labor costs or enhance productivity, I develop an equilibrium model. The model implies that many firms adopt a worker-centered culture primarily for cost savings.This paper was accepted by Ashish Arora, entrepreneurship and innovation.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00254 .

Macroeconomic Annaouncements and the News That Matters Most to Investors

2026-02-23T00:00:00+00:00

Management Science
Studying a large set of macroeconomic announcements (MAs) and disentangling their news content, we show that a portfolio of stocks that pays off around MAs that negatively impact the aggregate stock market commands a positive risk premium. Adding this portfolio to a position in the aggregate market substantially increases Sharpe ratio while reducing MA risk exposure, which implies a rejection of the CAPM. Using state-of-the-art measures of cash flow and discount rate news and consistent with prominent intertemporal CAPM specifications, we argue that the portfolio’s risk premium compensates investors for large reinvestment risk. Thus, we conclude that the MA news that matters most to investors is discount rate news and not cash flows news.This paper was accepted by Lukas Schmid, finance.Funding: This paper is based upon work supported by the Dutch Research Council [NWO Vidi Grant 201005], Fundação para a Ciência e a Tecnologia [UID/ECO/00124/2020], and POR Lisboa and POR Norte [Social Sciences DataLab, Project 22209].Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2024.07650 .

Getting on the Map: The Impact of Online Listings on Business Performance

2026-02-23T00:00:00+00:00

Management Science
We evaluate the extent to which small businesses maintain an online presence and estimate the impact of digital representation on business performance. Looking at a review platform, we find that roughly 18% of bars and restaurants in our sample do not have a listing as of the end of 2017, even though creating one is free. We then estimate the effect of adding a new listing using tax records on revenues combined with temporal variation in listing activity as well as a natural experiment that added over a thousand businesses to the platform via a data acquisition. The results suggest that new listings increase revenues in the range of 5%–10%; however, these effects vary substantially across establishments. Establishments that struggle to develop a reputation (e.g., nonchains or those in tourist areas) and those with a higher quality of product (e.g., larger restaurants or those with good reviews) experience larger increases after being listed.This paper was accepted by Duncan Simester, marketing.Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.04716 .

Detachment and Attachment: A Dual-Pathway Model of Leader Succession Rituals

2026-02-23T00:00:00+00:00

Journal of Management
Leader succession, the transition of a leadership position from a former leader to a new one, is a common yet potentially disruptive organizational process. Leader succession inherently carries risks and uncertainties, while rituals are often employed to instill order and provide stability. Building on this theoretical link between leader succession and rituals, we explored different types of leader succession rituals and investigated their impacts on succession outcomes. In a qualitative study (Study 1), we identified six types of leader succession rituals: artifact adoption, endorsement act, welcome ceremony, artifact return, closure act, and farewell ceremony. A duality emerged in our data: The first three ritual types focus on building attachment to the new leader, while the other three emphasize detachment from the former leader. Based on the findings from Study 1, we conducted a quantitative field study (Study 2) in a technology firm undergoing large-scale leader succession following an acquisition, as well as an experiment (Study 3) with full-time working adults from diverse organizational contexts via an online platform. Across Studies 2 and 3, we tested the dual-pathway model. The results not only confirm that the effects are transmitted through new leader attachment and former leader detachment but also reveal differential effectiveness among ritual types, with endorsement act, welcome ceremony, and farewell ceremony proving more effective in influencing succession outcomes.

SUVA: A Probabilistic Framework for Auditing LLMs with an Application to Social Preferences

2026-02-23T00:00:00+00:00

Information Systems Research
Organizations are increasingly deploying large language models (LLMs) as customer service agents, decision aids, and semiautonomous agents. We develop State–Understanding–Value–Action (SUVA), a probabilistic auditing framework that turns an LLM’s response into structured evidence about how its decision was produced. SUVA treats the prompt as the state, codes the model’s reasoning to extract its understanding and stated values using a transparent codebook, and then estimates how these elements statistically predict the eventual action. We demonstrate SUVA on social preference games from behavioral economics and show how the same workflow can audit other delegated decisions by using domain-specific prompts and value codebooks. Across eight widely used LLMs, SUVA reveals systematic prosocial and reciprocity patterns and shows how posttraining alignment reshapes them. For practice and policy, SUVA supports a repeatable audit, align, and reaudit workflow for model selection, compliance, and ongoing monitoring of deployed LLM systems.

Managerial actions using historical values for tackling hyper‐competitive environments: The case of Toyota

2026-02-04T00:00:00+00:00

Strategic Management Journal
This study provides a causal explanation concerning how managerial actions using historical values contribute to dynamic capabilities, or sustaining competitive advantage in changing environments. Based on historical methods that consist mainly of hermeneutics, contextualization, and source criticism, it analyzes sources and data on how Toyota strategically responded to the competitive dynamics of the automotive industry centered around modularization. This resulted in the identification of three types of managerial action using historical values—filtering,integrating, andmodifying—which have a rhetorical influence on the processes of strategic diagnosis, choice, and action respectively in ways that strengthen the microfoundations of dynamic capabilities. This enables the firm to adapt to hyper‐competitive environments while maintaining the idiosyncrasies of its core capabilities.Managerial SummaryToyota has sustained its competitive advantage for decades by consistently relying on its core capabilities, especially the Toyota Production System and its longstanding supplier relations. How has Toyota been able to do so while facing rapidly changing environments? This paper seeks to explore this question by comparing Toyota's and Nissan's strategies in response to modularization in the automotive industry in the 1990s and 2000s. It uncovers how a firm's managers can utilize historical values in ways that influence the processes of strategy formation and implementation. This allows the firm to incorporate key elements of new strategies that its rivals introduce in the marketplace alongside the firm's strategies based on its core capabilities, thereby adapting to hyper‐competitive environments while sustaining its idiosyncrasies and competitive advantage.

EXPRESS: Multiproduct Dynamic Pricing with Popularity Bias

2026-02-04T00:00:00+00:00

Production and Operations Management
Popularity bias, reflects the positive impact of popularity information on consumer choices. There are two common display formats for popularity information on online retail platforms: the total-based cumulative sales format, where the total sales of a product are displayed since its launch, and the period-based cumulative sales format, where only sales within a specific recent period are shown. Both types of popularity information are continuously updated. This paper focuses on the multiproduct dynamic pricing problem with popularity bias. We employ the widely used multinomial logit model (MNL) to investigate the impact of popularity bias on consumer choices. In particular, we examine how popularity bias affects marginal revenue, pricing decisions, and market shares. Moreover, we highlight that ignoring popularity bias can lead to a suboptimal outcome. As the multiproduct dynamic pricing problem suffers from the curse of dimensionality, we propose a semi-myopic pricing policy, which is computationally tractable, and demonstrate its asymptotic optimality under both formats. Our numerical simulations further indicate that ignoring popularity bias can result in substantial revenue losses, while the semi-myopic pricing policy consistently outperforms other heuristics under both formats. Finally, empirical tests on real data provide a comprehensive procedure for identifying the most appropriate choice models, which offers practical insights for implementation.

EXPRESS: Revenue Management with Nonparametric Demand Learning and Product Returns

2026-02-04T00:00:00+00:00

Production and Operations Management
Product returns are prevalent in practice. Many retailers provide lenient free return policies but with specific return window within which customers are allowed to return products. Motivated by this phenomenon, we consider a single-product online learning and pricing problem with stochastic product returns. A salient feature is that the demand function, depending on price and return window decisions, is initially unknown and must be learned on the fly. The retailer thus faces the classic exploration-exploitation trade-off. Moreover, we consider an inventory constraint, introducing an additional trade-off between earning revenue and managing inventory. We propose a modeling framework to integrate pricing and return window decisions, and develop a deterministic fluid model that serves as the full-information benchmark. To tackle the learning problem, we design a novel nonparametric learning algorithm that seamlessly integrates inverse stochastic gradient descent (SGD) and Upper Confidence Bound (UCB) methods. Under mild assumptions on demand and revenue functions, we establish a regret upper bound for our learning algorithm asO(WTlogT), whereWdenotes the number of return window candidates andTdenotes the time horizon. This result aligns with lower bounds established in both online pricing and multi-armed bandit (MAB) literature. Numerical experiments are conducted to verify the effectiveness and robustness of our algorithm across various environments. From an operational standpoint, retailers can use our learning framework as a decision-support tool to identify the optimal price and return window.

EXPRESS: Offline Learning and Optimization for Multi-product Inventory Management with Stockout-based Substitution

2026-02-04T00:00:00+00:00

Production and Operations Management
We deal with a retailer’s multi-product inventory system where customers randomly seek acceptable substitutes if their initial requests are not satisfied. Unsuccessful quests for substitutes would result in lost sales. Motivated by a consulting project as well as other real practices, we also choose to deal with further challenges posed by initially unknown base demand distributions and substitution probabilities; moreover, we aim to develop an offline learning method that (i) takes advantage of given data derived from bygone decisions rather than of any learning-while-doing opportunity, (ii) makes inferences about base-demand distributions and pairwise substitution probabilities without knowing whether there have been demand arrivals after inventory depletions, and (iii) is unhindered by the lack of knowledge about the assortments faced by customers and their purchase-or-no-purchase decisions at their individual arrivals. To address these challenges, we propose an innovative approach based on the Kaplan-Meier estimator that circumvents unrealistic data requirements. Our substitution probability estimates employ carefully designed weighting schemes to facilitate rigorous theoretical analysis through tools such as the Cauchy-Schwartz inequality. Both one-time substitution scenarios and more complex Markov-chain substitution patterns would be accommodated. Using large deviation tools, we establish provably optimal convergence rates of our estimates on top of consistency. The precision in parameter estimates would translate into accuracy in replenishment decisions. For inventory management, we take advantage of a submodularity property to obtain an exact algorithm for the two-product case and a good heuristic for the general multi-product problem. Computational studies based on simulated and actual data confirm the merits of our approach.

Knowledge Behind Firewalls: How Rivalry Among Alliance Partners Constrains Innovation Inside Firms

2026-02-04T00:00:00+00:00

Organization Science
Organizations can gain substantial knowledge synergies from their alliance portfolios, and research suggests firms may benefit even more when their partners compete directly with each other. However, firms can only capture such synergies if their own inventors are allowed to freely exchange knowledge obtained from these partners to create novel combinations. We challenge this premise and argue that, because partners in such configurations are more concerned about the risks of knowledge leakage to their rivals, they will pressure the focal firm into imposing credible safeguards that restrict the access and handling of knowledge inside its organization. We predict that the constraints imposed by these safeguards will be reflected in a higher disconnectedness in the focal firm’s inventor collaboration network, with the extent of these constraints tied to the relative technological bargaining power the focal firm holds over its partners. Importantly, these safeguards are not costless, and we predict that the disconnectedness induced will affect the focal firm’s innovation performance by undermining its ability to create and appropriate inventive value. Using a longitudinal sample of pharmaceutical firms, we find consistent support for these arguments. Our study contributes to research on interfirm alliances, intraorganizational networks, and innovation while offering practical insights for managers.Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.17230 .

Algorithms for Loot Box Design

2026-02-04T00:00:00+00:00

Operations Research
From Loot Boxes to Better Design: Pricing Randomized ProductsRandomized rewards—often described as “mystery packs” or “blind-box” mechanics—are now a familiar feature in many video games. Yet choosing a loot box’s price and the odds of each possible drop is not just a design decision; it is a challenging optimization problem. In their accepted Operations Research paper, “Algorithms for loot box design” (Han, Ryan, and Tong, forthcoming), the authors develop an algorithmic framework for designing loot boxes by selecting a purchase price and item drop probabilities to maximize expected revenue under player choice behavior. They show that the general problem is computationally hard, but also identify economically motivated restrictions on player utilities that make the design problem tractable. When the number of items is fixed, the paper provides an exact polynomial-time algorithm under one class of utility structures and efficient approximation algorithms with provable guarantees under another. The analysis also links loot-box design to classic pricing ideas, offering guidance on how to translate item-level values and rarity into transparent, well-performing randomized reward systems.

Forced Ranking Systems and Gender Gaps in Job Tenure

2026-02-04T00:00:00+00:00

Management Science
Using career history data from employees’ online profiles, we provide empirical evidence regarding how human resources systems can affect the gender gap in job tenure. Our results indicate that female workers experience shorter average job tenure than male workers. Our results further show that abandoning forced ranking systems helps to narrow this gender gap. We present experimental evidence indicating that in forced ranking environments, women experience shorter tenure than men and are more likely to exit their roles. This is true even when women are on equal footing with men. Finally, we present survey evidence that under forced ranking systems, more women resign as compared with men. In sum, our empirical, experimental, and survey results indicate that forced ranking systems may contribute to a gender gap in job tenure. Accordingly, abandoning such systems may help promote workplace gender equality.This paper was accepted by Yan Chen, behavioral economics and decision analysis.Funding: This work was supported by the National Natural Science Foundation of China [Grants 72472068, 72422022, 72272055, 72321001, 72342009, 72325010, and 72172085], the Key Project of Philosophy and Social Science Research of the Chinese Ministry of Education [Grant 22JZD012], Shenzhen Natural Science Foundation [Grant JCYJ20250604144252069], and Macau University of Science and Technology (MUST) Faculty Research Grant [Project No. FRG-25-013-MSB].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03264 .

The Value of Flexibility from Opaque Selling

2026-02-04T00:00:00+00:00

Management Science
An opaque product refers to a subset of products that only differ in some secondary attribute (such as color or style). After a customer purchases an opaque product, one of the products in the subset is then allocated to the customer. Selling opaque products has become popular on e-commerce platforms, because it is a source of flexibility that reduces demand variability and hence inventory cost. In this paper, we seek to understand how the design of opaque product configurations and the fraction of customers choosing opaque products impact the value of flexibility arising from opaque selling. The key finding from our study is the following insight: A modest opaque selling strategy where (i) the opaque options contain small subsets and (ii) only a small fraction of customers buy opaque products achieves substantial cost savings (compared with nonopaque traditional selling). Moreover, the cost savings of this modest opaque strategy is on the same order as a fully flexible scenario where all customers buy an opaque product. Our analysis relies on exploring a novel connection of multiproduct inventory management to the balls-into-bins framework along with a simple balancing policy for opaque selling. As a byproduct of our analysis, we show that the simple balancing policy is asymptotically optimal. For extensions outside of our theoretical analysis, we conduct extensive numerical experiments to show that our core insights continue to hold.This paper was accepted by Jayashankar Swaminathan, operations management.Funding: This work was supported by the Division of Civil, Mechanical and Manufacturing Innovation [Grant 1944428].Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2019.02983 .

Joint Assortment and Inventory Planning Under the Markov Chain Choice Model

2026-02-04T00:00:00+00:00

Management Science
We address the joint assortment and inventory optimization problem for an online retailer facing a set of N substitutable products. The retailer must determine both the assortment and inventories of these products before the start of the selling season to maximize the expected profit. We consider a setting with dynamic SOBS, where consumers’ choices follow the Markov chain choice model. This is a challenging problem, and even computing the expected profit for a given assortment and inventory solution requires solving an intractable dynamic program. We present a sample average approximation-based algorithm for the problem that achieves a regret of [Formula: see text] with respect to an linear program (LP) upper bound. Our algorithm first selects an assortment by balancing the expected revenue (from a single consumer) and the inventory cost. We do this by identifying a subset of products that can pool demand from the universe of substitutable products without significantly cannibalizing the revenue in the presence of dynamic substitution behavior of consumers. We then use a sample average approximation-based LP to decide on the inventory level for each item in the selected assortment. We numerically show that our algorithm considerably improves the performance over standard approaches from the literature on a wide range of instances of the Markov chain choice model and demonstrate that it carefully handles the inventory of products in the long tail (i.e., products with small mean total demand).This paper was accepted by Chung Piaw Teo, optimization.Funding: This work was supported by Amazon [Research Award (V. Goyal and O. El Housni)] and the National Science Foundation [Grants CMMI 1636046 (V. Goyal) and 2226900 (O. El Housni)].Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2023.01322 .

Optimal Contests with Negative Prizes: Theory and Experiment

2026-02-04T00:00:00+00:00

Management Science
This paper examines the optimal design of contests in the presence of negative prizes and establishes the optimality of a modified all-pay auction with entry fee and reserve. The entry fee always equals the contestants’ liability, and the reserve is weakly higher than in contests without negative prizes. The modification involves awarding all contestants a strictly positive prize if none meet the reserve. This optimal contest better incentivizes high-ability contestants by offering them a higher prize augmented by entry fees, while still ensuring full participation from low-ability contestants. Theoretical analysis demonstrates that when contestants’ liability is sufficiently high, the same contest maximizes both the expected total effort and winner’s effort, with both measures increasing with liability. Numerical simulations show that even with low liability, predictions from the two optimal contests are closely aligned. To test these predictions, we conduct an experiment comparing optimal contests across different liability levels, confirming the “killing-two-birds-with-one-stone” prediction.This paper was accepted by Elena Katok, operations management.Funding: The authors gratefully acknowledge the financial support of the UTS Business School Research Grant, UTS Behavioural Lab Grant and National Natural Science Foundation of China [Grants 72192842, 72203099, 72422018].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03580 .

The Effect of Online Cart Composition on Cart Abandonment

2026-02-04T00:00:00+00:00

Journal of Consumer Research
Online shopping cart abandonment is widespread, causing major losses in potential revenues for e-commerce companies. We expand efforts to mitigate cart abandonment by investigating how the cart’s product composition affects abandonment and testing easy-to-implement interventions. We hypothesize that consumers are more likely to abandon carts containing a higher proportion of hedonic relative to utilitarian products. This cart composition effect arises because carts containing higher hedonic-to-utilitarian product ratios are perceived as more hedonic overall, increasing consumer guilt regarding cart purchase, and the likelihood of cart abandonment. Analyses of two large-scale field datasets and four controlled experiments provide converging evidence for the cart composition effect (Studies 1A-3) and the mediating role of perceived hedonism and consumer guilt (Studies 2, 4A, 4B). Substantively, we offer empirical support for a practical and easily implemented intervention: using e-commerce recommendation systems to reduce cart abandonment by suggesting utilitarian items (Studies 4A & 4B). Our findings suggest that recommendation systems may serve as an effective tool for reducing cart abandonment and underscore the importance of incorporating hedonic value considerations into recommendation algorithms. We conclude by discussing the practical implications of our findings for the development of more effective marketing strategies and improving online conversion rates.

Partisan Cities: How State‐Local Political Alignment Shapes Credit Risk and Information Processing in the Municipal Bond Market

2026-02-04T00:00:00+00:00

Journal of Accounting Research
This paper studies how partisan alignment between city leaders and state governors shapes information processing and bond pricing in the municipal bond market. Using a novel data set on 1,045 U.S. cities from 2005 to 2019, we show that cities with the same political affiliation as the state governor face 9 basis points lower borrowing costs than misaligned cities. The effect is stronger for riskier bonds, in states where governors hold greater authority, and for fiscally dependent cities. Aligned cities also receive more aid during fiscal distress. Partisan alignment shapes how investors interpret and respond to financial information: Nondisclosure and adverse audit findings raise borrowing costs primarily for misaligned cities, while penalties for aligned cities are markedly smaller.

Reactions by Actual Data Breach Victims over Time: Evidence from Facebook’s Cambridge Analytica Breach

2026-02-04T00:00:00+00:00

Information Systems Research
When a data breach happens, both victims, whose data were breached, and nonvictims react negatively but not equally so. In this study, we used Facebook’s Cambridge Analytica data breach to identify to what extent reactions of data breach victims go beyond those of nonvictims. Our findings reveal that victims initially lose more trust, feel more violated, feel more strongly that a psychological contract has been breached, feel less belonging on Facebook, and intend to continue using Facebook less. However, these effects are short-lived, disappearing within six months. Intuitively, one might assume that this is because people are locked in on Facebook and are forced to change their attitudes because they cannot change their behavior by leaving it. Surprisingly, a follow-up experiment shows that this is not the case as we found people who are less locked in to improve their attitudes more over time. Our study adds to the mounting evidence that consumers are unable to act when their data are breached and their privacy is violated and that regulation may be needed to better protect data. The short-lived nature of victims’ reactions we identified in this case also raises questions about the role of postbreach compensation in different contexts.

The power of expressed humility: Early stage investors' reaction to humble entrepreneurs

2026-02-05T00:00:00+00:00

Strategic Entrepreneurship Journal
We examine how entrepreneur‐expressed humility affects early stage investors' willingness to fund new ventures. In pitching contexts where investors rely on relational cues and implicit prototypes of entrepreneurs, we theorize three distinct pathways through which expressed humility shapes funding decisions. First, building on research regarding interpersonal signals in early stage valuation, we propose that humility fosters perceptions of interpersonal affect and trust and team‐building qualities, increasing investors' willingness to fund. Second, drawing on implicit leadership theories, we argue that humility may trigger negative perceptions regarding the entrepreneur's ability to make rapid and risky decisions. Across a videometric analysis of 140 real‐world pitches and a randomized experiment with French early stage investors, we show that expressed humility elicits both pathways, but investors prioritize positive attributions.Managerial SummaryAlthough humility is often regarded as a positive leadership trait, it contradicts implicit prototypes of successful entrepreneurs, who are typically seen as dominant and assertive. We examine how early stage investors perceive and respond to displays of humility during pitches. We propose that entrepreneur‐expressed humility produces ambiguous effects: It enhances perceptions of interpersonal affect and trust and team‐building qualities, but raises doubts about the entrepreneur's ability to make rapid and risky decisions. Using a videometric analysis of 140 pitches from the French version ofShark Tankand a randomized experiment with venture capital investors, we find evidence for these competing pathways. Overall, investors prioritize the positive attributions of interpersonal skills, suggesting that entrepreneurs benefit from expressing humility when pitching.

Pre-hedging

2026-02-05T00:00:00+00:00

Operations Research
The paper “Pre-hedging” studies a dealer that pre-hedges an anticipated potential trade and analyses how this affects the client’s overall execution outcome. It shows that pre-hedging can benefit both parties: Improved risk management over an extended horizon then enables the dealer to charge reduced spreads that more than offset any adverse impact the pre-hedging activity has on the execution price. However, when a dealer pre-hedges too aggressively, this can be detrimental to the client. Timing uncertainty of the potential trade is an effective control held by the client to mitigate any counterproductive pre-hedging. Our results are robust to a setting where competing dealers simultaneously pre-hedge.

Deep Learning for High-Dimensional Continuous-Time Stochastic Optimal Control Without Explicit Solution

2026-02-05T00:00:00+00:00

Operations Research
Multiasset Optimal Execution via Deep Learning for High-Dimensional Continuous-Time Stochastic ControlIn “Deep Learning for High-Dimensional Continuous-Time Stochastic Optimal Control Without Explicit Solution,” Dupret and Hainaut introduce the generalized policy iteration physics-informed neural network, a novel deep learning algorithm for solving high-dimensional continuous-time stochastic optimal control problems even when the optimal control does not admit explicit solution. The method combines physics-informed neural networks with an actor-critic structure based on generalized policy iteration and uses separate networks to approximate both the value function and the multidimensional optimal control. This approach provides a global approximation of the solution across time and space, enabling fast online evaluation. Theoretical guarantees on convergence and optimality are provided, whereas its accuracy and efficacy are empirically validated through two important numerical examples from operations research. Thereby, the authors generalize the Almgren–Chriss framework arising from optimal execution in finance by allowing both temporary and permanent price impacts to be fully nonlinear and by considering a multidimensional setting with multiple cointegrated assets.

Trustworthiness in Computational Theory Construction: Dimensionalization and Category Surfacing1

2026-02-05T00:00:00+00:00

MIS Quarterly
In this methods article, we unpack how researchers can foster trustworthiness in dimensionalization and category surfacing (DCS), a key method family within the genre of computational theory construction (CTC). Information systems (IS), management, and organizational scholars are increasingly leveraging DCS tools such as topic modeling, word embeddings, and clustering to surface latent categories and dimensions from textual data for theory construction. Yet they struggle because evaluations of such research often default to transparency, operationalized as replicability and accountability, which obscures the analytical choices that actually make DCS research rigorous. In this study, we recast transparency as a means toward trustworthiness. We treat researchers’ analytical moves as the primary unit of methodological reasoning in how they design, conduct, and disclose their choices across research phases. We develop a framework that authors, reviewers, and editors can use to construct and evaluate DCS research. The framework specifies how trustworthiness arises from the interplay of two research design choices: primacy to theoretical versus practice lexicons, and whether the content of texts or the structure of the corpus carries the theoretical load. We articulate expectations for conduct and disclosure across these design choices, clarifying how proportionate reasoning anchors trustworthiness. We conclude with implications for advancing trustworthiness within the broader CTC community and across other computational approaches to research.

Network‐Enabled Responses to Deglobalization: Examining How Firms Strategize During Eras of Global Disruption

2026-02-05T00:00:00+00:00

Journal of Management Studies
Recent research has shown how cycles of globalization and deglobalization can disrupt firms and networks. Understanding this process requires a historical perspective on how firms and networks coordinate, strategize, and respond to the effects of deglobalization. This paper examines firm‐level, network‐enabled strategic responses to deglobalization by analysing a selection of key firms operating within the Liverpool (UK) and New York (USA) networks at the turn of the nineteenth century. The case study analyses firms within a broader network structure through network visualization, supplemented by various archival data sources. We explore the strategic responses of multiple firms and demonstrate how the network supported them, enabling them to address the challenges posed by deglobalization. We identify five categories of network‐enabled strategic responses to deglobalization: reinforcement, adaptation, shared risk, lobbying, and exit.

Content Exclusivity on Advertising Revenue–Sharing Platforms

2026-02-05T00:00:00+00:00

Information Systems Research
Digital content platforms such as YouTube, TikTok, and Twitch rely on third-party providers and share advertising revenue to incentivize content production. Yet, as providers increasingly distribute content across multiple platforms, competition intensifies and platforms consider exclusive contracts to restrict providers’ multihoming behavior. Our research develops an analytical model to examine when exclusive contracts benefit or harm platforms, providers, consumers, and overall welfare. We find that exclusivity has two key forces: it limits the number of providers on each platform, reducing same-side crowding, but it also weakens the effectiveness of revenue sharing as a competitive tool for attracting providers. As a result, exclusivity benefits platforms only when consumers place low marginal value on content, leading platforms to offer lower sharing rates and earn higher profits. When consumer valuation is high, exclusivity can backfire, reducing platform profits. Importantly, exclusivity can increase provider surplus and even generate win–win outcomes for both platforms and providers at intermediate valuation levels. Although exclusivity reduces consumer surplus because of fewer providers, total social welfare may still increase because of reduced competition costs among providers. These findings suggest policymakers should take a nuanced stance toward regulating content exclusivity.

An Integer Programming Approach for Quick-Commerce Assortment Planning

2026-02-06T00:00:00+00:00

Management Science
In this paper, we explore the challenge of assortment planning in the context of quick commerce, a rapidly growing business model that aims to deliver time-sensitive products. In order to achieve quick delivery to satisfy the immediate demands of online customers in close proximity, personalized online assortments need to be included in brick-and-mortar store offerings. With the presence of this physical linkage requirement and distinct multinomial logit choice models for online consumer segments, the firm seeks to maximize overall revenue by selecting an optimal assortment of products for local stores and by tailoring a personalized assortment for each online consumer segment. We employ an integer programming approach to solve this NP-hard problem to global optimality. In particular, we derive convex hull results to represent the consumer choice of each online segment under a general class of operational constraints, and to characterize the relation between assortment decisions and choice probabilities of products. Our convex hull results, coupled with a modified choice probability–ordered separation algorithm, yield formulations that provide a significant computational advantage over existing methods. Finally, we illustrate how our convex hull results can be used to address other assortment optimization problems.This paper was accepted by Chung Piaw Teo, optimization.Funding: Y. Rong’s work was supported by the National Natural Science Foundation of China [Grants 72025201 and 72221001]. T. He’s work was supported by the National Natural Science Foundation of China [Grants 72101146 and 72231003], and Y. Wang’s work was supported by the National Natural Science Foundation of China [Grants 72331006].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02996 .

An Economic Analysis of Online Ad Fraud Deterrence

2026-02-06T00:00:00+00:00

Management Science
Ad fraud is increasingly becoming a major concern in online advertising, with publishers being one of the key sources of fraudulent ad traffic. Although ad fraud deterrence is a critical problem from both technical and economic perspectives, past research has not considered their interplay. Our paper fills this critical gap by building a game-theoretic model wherein an ad network (an intermediary between publishers and advertisers) strategically leverages its technological tool (the configuration of a given fraud detection technology) and economic tool (the payment to publishers) to deter ad fraud and maximize profits effectively. Our analysis generates several interesting findings. For example, as the fraud detection technology and fraud generation techniques improve, we show that although the ad network needs to respond by making the technology configuration stricter to dampen fraud motives and admit less ad traffic, it may sometimes need to increase the payment. Furthermore, although many stakeholders advocate instituting strict legislation and policies to reduce malicious publishers, we show that this may sometimes fail to reduce fraud traffic and even hurt an ad network’s profit. In addition, our results provide other useful implications that present a new theoretical perspective on the incentive problems in ad fraud generation and detection. Our study also draws valuable insights for ad networks into implementing effective ad fraud deterrence policies and for advertisers to audit and monitor their ad campaign performance.This paper was accepted by Hemant Bhargava, information systems.Funding: S. Kumar thanks the Temple Center for International Business Education and Research for partially supporting this research.Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.02201 .

Matchmaking Strategies for Maximizing Player Engagement in Video Games

2026-02-06T00:00:00+00:00

Management Science
Managing player engagement is vital to the online gaming industry, given that many games generate revenue through subscription models and microtransactions. We scrutinize engagement management in the prevalent category of competitive video games, where players are frequently matched against one another, and matchmaking systems substantially impact engagement. We propose a dynamic model to analyze player dynamics and optimize matchmaking policies for maximum engagement. Our model takes into account two essential factors in competitive games: heterogeneous skill levels and players’ aversion to losing. Additionally, the model enables us to consider pay-to-win strategies and AI-powered bots, which are contentious methods of influencing player engagement and endogenously affect the optimal matchmaking policy. To provide sharp insights, we analyze a specific case where there are two skill levels, and players churn only after experiencing a losing streak. The optimal matchmaking policy considers both short-term rewards by matching players myopically and long-term rewards by adjusting skill distribution. The pay-to-win system can positively impact player engagement when the majority of players are low-skilled, because adopting pay-to-win also affects skill distribution. This result challenges the conventional wisdom that typically regards pay-to-win as trading player experience for revenue. When incorporating AI-powered bots, we demonstrate that optimizing the matchmaking policy can significantly reduce the number of required bots. We then extend our model and conduct a case study with real data from an online chess platform. The optimal policy can improve engagement by 4%–6% or reduce the percentage of bots by 3% in comparison with skill-based matchmaking.This paper was accepted by Jeannette Song, operations management.Funding: A. N. Elmachtoub is partially supported by NSF [Grant CMMI-1944428]. X. Lei is partially supported by the Hong Kong Research Grants Council [Early Career Scheme 27503123].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02957 .

Multidimensional Signaling with Supplier Credit Guarantees

2026-02-06T00:00:00+00:00

Management Science
This paper investigates credit guarantees in a supply chain setting involving a supplier, a capital-constrained buyer, and a bank. We find that under complete information, credit guarantees increase order quantities but paradoxically raise wholesale prices, overall rendering the credit guarantee not useful for improving the operational efficiency. However, under information asymmetry, either a partial or full guarantee emerges in equilibrium, with the wholesale price maintained at its optimal full-information level. This underscores the pivotal role of credit guarantees in effective signaling and delineates the distinct functions of the wholesale price and the guarantee—the former on operational efficiency and the latter on signaling efficacy. Further comparative analysis of the supplier and buyer performances under the two contract types reveals that credit guarantees do not always benefit both parties.This paper was accepted by Jayashankar Swaminathan, operations management.Funding: H. Liu was supported by the National Natural Science Foundation of China [Grants 72272149 and 72394374]. H. Song was supported by the National Natural Science Foundation of China [Grants 72501259 and W2411062].Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2024.07052 .

Competition Enforcement and Accounting for Intangible Capital

2026-02-06T00:00:00+00:00

The Journal of Finance
Antitrust laws mandate review of mergers and acquisitions (M&As) that exceed an asset size threshold based on accounting standards that exclude most intangible capital. We show that this exclusion leads to thousands of intangible‐intensive M&As being nonreportable. Acquirers in nonreportable deals achieve higher equity values and price markups, especially when consolidating product markets. Furthermore, nonreportable pharmaceutical deals are three times more likely to involve overlapping drug projects, which are subsequently 40% more likely to be terminated. Our results suggest that the growth of intangible assets may exacerbate market power through nonreportable consolidation of the sectors most concerning for consumers.

A User Purchase Motivation-Aware Product Recommender System

2026-02-06T00:00:00+00:00

Information Systems Research
Retailers struggle to match recommendations to why customers buy. We introduce a practical framework that distinguishes two core, actionable purchase motivations, stable preference and exploratory intent, and present STB, a data-efficient measure that infers which motivation drives each item purchase using only transaction sequences and item attributes. Building on STB, we develop UPSTAR, a motivation-aware recommender that separates users’ behavior into stable-preference and exploratory subsequences and fuses their signals for next-item prediction. Across three real-world e-commerce data sets, UPSTAR substantially improves accuracy and, importantly, advances the system’s ability to surface genuinely exploratory items that drive discovery and cross-category sales. For practitioners, our method enables more targeted marketing: promote reliable items to preference-driven buyers while exposing exploratory buyers to curated novelty, improving conversion and long-term engagement. For platform policy and operations, motivation-aware recommendations support inventory planning, personalized promotions, and responsible diversification of exposure without requiring surveys or extensive auxiliary data. Implementation requires only existing transaction logs and item metadata, making it immediately deployable for large-scale retail systems.

Love of the Job: What It Is, How to Measure It, and Why It Matters for Work Outcomes

2026-02-06T00:00:00+00:00

Human Resource Management
Employee retention, motivation, performance, and well‐being remain enduring priorities in human resource management, yet existing constructs such as engagement, commitment, and satisfaction do not fully capture the depth of emotional attachment that some employees feel towards their jobs. We introduce Love of the Job (LOJ) as a higher‐order form of affective attachment to one's job that integrates passion for one's work, non‐romantic intimacy with coworkers, and commitment to organizational membership, grounded in Sternberg's triangular theory of love. Across a four‐stage, eight‐sample program of research (totalN= 1,801), we develop and validate a concise and reliable LOJ scale using diverse working‐adult samples and longitudinal designs. The measure demonstrates strong psychometric properties and discriminant validity from related constructs, establishing LOJ as a distinct form of work attachment, and shows incremental predictive validity for key outcomes, including discretionary performance, innovation, and job crafting, as well as retention and well‐being indicators such as turnover intentions, partial absenteeism, and work neglect. Together, these findings position LOJ as a novel, theoretically grounded construct with strategic relevance for HRM and provide scholars and practitioners with a robust tool to assess and better understand employees' deep emotional attachment to their work.

Platform competition and strategic trade‐offs for complementors: Heterogeneous reactions to the entry of a new platform

2026-02-07T00:00:00+00:00

Strategic Management Journal
We study how the entry of a rival platform affects the strategies of the incumbent's complementors. The latter face a trade‐off: While the entry threatens their benefits from indirect network effects, it also allows them to escape intense within‐platform competition. Studying Epic Games' entry into the PC video game market—until then dominated by Steam—we show that this trade‐off does not resolve uniformly, driving heterogeneity in strategic reactions. Complementors with weaker strategic resources (independent developers) were more likely to multihome and became less responsive to the incumbent's attempts to orchestrate collective action through platform‐wide sales promotions. In contrast, complementors more reliant on indirect network effects (multiplayer developers) were less likely to multihome and became more responsive to orchestration attempts.Managerial SummaryAs competition between digital platforms intensifies, complementors—firms that provide complementary products or services—must adjust how they engage with platform owners. The entry of a rival platform creates both opportunities and risks: it offers an alternative with less intense competition but also fragments the user base that underpins network benefits. We find that these opposing effects shape complementors' behavior differently. Those with fewer strategic resources are more likely to join the new platform and become less willing to follow the incumbent's coordinated initiatives. In contrast, complementors whose products rely strongly on network effects tend to remain with the incumbent and cooperate more closely with its orchestration efforts. For managers, this highlights that platform competition not only shifts market dynamics but also reshapes the motivations and strategies of heterogeneous complementors.

When Overconfident CEOs Deliver Higher Returns: Evidence From Acquisition Waves

2026-02-08T00:00:00+00:00

Journal of Management
Overconfident CEOs are frequently criticized for making value-destroying corporate acquisitions in which they acquire excessively and overpay for their acquisitions. By contrast, we argue that overconfident CEOs can deliver higher returns in acquisition waves because the motivation and the requirement for action speed that occur in acquisition waves are different from other acquisition contexts. Specifically, we hypothesize and find that overconfident CEOs are more likely to capture preemption opportunities by acting earlier in acquisition waves, and such rapid moves enable overconfident CEOs to achieve higher acquisition returns. In addition, drawing upon organizational learning research, we hypothesize and find that in acquisition waves, pre-wave experience with large and related acquisitions facilitates overconfident CEOs to pursue acquisitions even more quickly during acquisition waves, which further enhances acquisition returns. Contributions to the acquisitions and CEO overconfidence literatures are discussed.

Carbon Pricing versus Green Finance

2026-02-08T00:00:00+00:00

The Journal of Finance
Green finance—including environmental, social, and governance investing and sustainable finance regulations—is widespread, but can it substitute for carbon pricing in fighting climate change? In a unified model, I show that (i) when carbon prices reflect the social cost of carbon, green finance should not be used; (ii) when carbon prices are too low, green finance can implement the social optimum if each firm's cost of capital can be set to itssustainable discount rate, which increases with the ratio of carbon emissions to firm value. I provide calibrations, analyze stranded assets, and present implementations through subsidies or preferential financing for green firms.

Social media discussion of sell-side analyst research: evidence from Twitter

2026-02-09T00:00:00+00:00

Review of Accounting Studies
We examine Twitter discussion of sell-side analysts’ stock recommendation revisions. While many investors lack direct access to analyst research, we observe revision-related Twitter discussion associated with approximately 90 percent of the revisions in our sample, usually within three hours of their announcement. Revision-related Twitter discussion is greater for upgrades and for analysts from larger brokerages. Examining within-revision intraday price discovery, we observe increased price discovery during intraday windows with more revision-related tweets, especially for tweets that have more user engagement, are posted by more influential authors, or involve stocks with more intense retail trading volume. We find that revision-related retail trading is more intense and better predicts future returns for revisions with more revision-related Twitter discussion. We observe no such evidence for institutional investors who have direct access to sell-side research. Our results suggest that Twitter is an important channel in facilitating price discovery following analyst revisions, particularly among retail investors.

EXPRESS: A Study of the Attributes of POMS Members and Conference Registrants

2026-02-09T00:00:00+00:00

Production and Operations Management
This study examines the composition of attributes among members of the Production and Operations Management Society (POMS). We analyze POMS membership data from 2017 to 2023 and conference registration data from 2011 to 2024. Specifically, the study seeks to: (1) describe the current composition of POMS members and conference registrants in terms of gender, academic rank, and country of affiliation, and (2) provide recommendations for improving representation within professional societies.

Online Tensor Inference

2026-02-09T00:00:00+00:00

Operations Research
From Big Data to Real-Time Decisions: Online Tensor InferenceModern digital platforms, from e-commerce and online advertising to mobile health, generate massive streams of high-dimensional data that must be analyzed in real time. In their paper “Online Tensor Inference,” Xin Wen, Will Wei Sun, and Yichen Zhang develop a new statistical framework that enables both efficient learning and rigorous inference for streaming tensor data. The authors propose an online low-rank tensor estimation method based on stochastic gradient descent that processes observations sequentially without storing historical data, overcoming the memory and scalability limitations of traditional offline approaches. Beyond estimation, the paper introduces a novel online debiasing technique that delivers valid confidence intervals and hypothesis tests on the fly without data splitting. Theoretical results establish near-minimax-optimal convergence rates and asymptotic normality for general linear functionals of tensors. Together, these advances provide a principled foundation for real-time, statistically grounded decision making in fast-changing, data-rich environments.

Forecasting Earnings from Home

2026-02-09T00:00:00+00:00

Management Science
We examine the impact of working from home (WFH) practices on the financial services industry, focusing on sell-side equity analysts. We find that analysts who previously benefited from access to in-person interactions with other informed parties experience a greater decline in earnings forecast accuracy following the COVID lockdown and the shift to WFH. Notably, the informational advantage associated with the in-person access disappears during the lockdown and returns once restrictions are lifted. Our results are stronger for all-star analysts and analysts with shorter coverage periods, suggesting that all-star analysts rely relatively more on access to in-person interactions prelockdown and that accumulated firm-specific knowledge mitigates the loss of in-person interactions. Our results remain robust across alternative analyst performance measures. We conclude that, despite recent advances in communications technology, AI, and machine learning, in-person interactions remain a unique and difficult to substitute information channel for sell-side research providers and that WFH impedes information flows between market participants in capital markets.This paper was accepted by Suraj Srinivasan, accounting.Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2024.04637 .

Calling It Quits: A Behavioral Perspective on Exploration and Exploitation in R&D Project Termination

2026-02-09T00:00:00+00:00

Journal of Operations Management
Not every R&D project will succeed, necessitating a careful selection of which R&D projects to pursue and which to terminate. Timely termination decisions free up scarce resources for more promising projects. Yet, prior research has yielded inconclusive results on how firms terminate exploratory versus exploitative R&D projects. Given that early‐stage R&D project termination is a decision made under high uncertainty, we adopt a behavioral perspective to reconcile the inconsistent findings. We propose that a firm's preference for terminating exploratory versus exploitative projects depends on three sources of contextual feedback: parallel projects, prior collaboration experience, and firm performance. We test our hypotheses using drug development projects from pharmaceutical firms over 11 years, finding that exploratory projects are less likely to be terminated relative to exploitative ones when the number of parallel projects is limited, when they are conducted with an existing partner, or when firm performance falls below aspiration.

Human-Algorithm Collaboration in Gig Work: The Role of Experience, Skill Level, and Task Complexity

2026-02-09T00:00:00+00:00

Information Systems Research
In this paper, we contribute to recent studies on human-algorithm collaboration by examining how experience, skill level, workload, and task complexity shape the impact of an algorithm-enabled decision-support tool for gig workers. We leverage a large-scale randomized field experiment on the Instacart platform from June 2022 to September 2022. The algorithm-enabled technology aims to revolutionize item picking by helping shoppers locate and collect items more efficiently, reducing picking time while maintaining service quality, as reflected by refund rates. We find that the technology complements experience: rather than diminishing the value of experience, it yields larger improvements for more experienced shoppers. We also find that it substitutes for skill levels by helping lower-skilled workers bridge the performance gap with higher-skilled peers, but lower-skilled workers need experience to fully benefit from the tool. Finally, treatment effects vary with workload and task complexity, clarifying when algorithmic guidance is most valuable. For policymakers, our findings suggest a simple rule: give workers some baseline experience before introducing AI tools, using a staggered rollout with basic training. We also show that these tools can make service more consistent by closing the gap between high performers and lower performers, reducing performance dispersion, and helping standardize quality.

Building High Involvement Work Systems in the Digital Era: Employee Experience‐Oriented Digital HRM and Employee Involvement

2026-02-09T00:00:00+00:00

Human Resource Management
Despite the increasing application of digital technology in management practices, its implications for employee involvement and high involvement work systems (HIWSs) remain largely unexplored. Based on an in‐depth qualitative case study of Tencent—one of China's largest information technology companies—this article explores whether and in what ways employee experience (EX)‐oriented digital HRM contributes to the development of HIWSs. Drawing on socio‐technical systems theory, we examine the goals, tasks, structures, actors, and technical elements of the digital HRM system in the case company, as well as the interactions among these elements. Our findings suggest that the EX‐oriented digital HRM system enhances two key dimensions of HIWSs: technological empowerment and organizational involvement. First, EX‐oriented digital HRM provides employees with digital tools that enable them to exercise some autonomy in various facets of their work life. This includes self‐management, team collaboration, and personalized learning and development opportunities. Second, EX‐oriented digital HRM arguably promotes employee involvement in both product development and some aspects of organizational management by leveraging user experience design. This study contributes to the theory of socio‐technical systems and the literature on HIWSs in the digital era.