Knowledge that Transforms

Modeling and leveraging consumers’ dynamic search behaviors presents significant business opportunities. Although deep learning methods excel at processing vast consumer data for predictive tasks, their opaque nature limits interpretability and fails to explicitly model consumer decision making. In contrast, economic theory suggests that consumers follow a sequential search strategy, evaluating alternatives until they find the best match for their preferences. To bridge this gap, we propose the sequential search transformer (SST), a deep structural econometric model that integrates deep learning with sequential search theory to model search and purchase decisions. SST unifies these two approaches into an end-to-end trainable model, improving both predictive accuracy and policy evaluation capabilities. Unlike conventional deep learning models, SST explicitly models consumer decision making, and unlike existing sequential search models, it enables consumer behavior modeling across sessions and sequentially resolves utility uncertainty for searched items. We provide a theoretical analysis of the identification strategy for the SST model and show that all parameters can be identified under the proposed framework. Then, we apply SST to a data set with detailed clickstream data collected from a U.S. e-commerce website. Empirical evaluations show that SST outperforms state-of-the-art deep learning and structural models in predicting consumer searches and purchases. Moreover, policy experiments demonstrate SST’s effectiveness in optimizing product recommendations and new product promotion strategies, ultimately enhancing consumers experience and driving revenue growth.

This paper examines the effect of crossborder carbon taxes on shareholder wealth. Using stock price reactions to key announcements of the European Union (EU) carbon border adjustment mechanism (CBAM), we find that EU purchasers of CBAM-covered products experience significantly lower returns than non-European producers of such products. The effect is strongest for EU purchasers with non-EU supply chains. Further cross-sectional analyses show that these negative reactions are more pronounced when CBAM-related costs are higher and when firms have a lower ability to pass them on to their trading partners. Overall, the evidence suggests that equity markets expect crossborder carbon pricing on imports to be costly for EU firms. This paper was accepted by Caroline Flammer, sustainability. Funding: G. Ormazabal thanks the Cátedra de Dirección de Instituciones Financieras y Gobierno Corporativo del Grupo Santander, the R + D + I Project [Reference PID2022-143016NB-I00 funded by MCIN/AEI/10.13039/501100011033], and ERDF “A way of making Europe” [Grant TED2021-132531B-I00 funded by MCIN/AEI/10.13039/501100011033, the European Union NextGeneration EU/PRTR, IESE’s High Impact Projects Initiative–2023, and the Social Trends Institute]. R. Raney acknowledges financial support from the Spanish Ministry of Science and Innovation [Grant PID2019-111143GB-C31 funded by MICIU/AEI/10.13039/501100011033] and [Grant PID2023-150744NB-C41 funded by MICIU/AEI/ 10.13039/501100011033].

To examine what women bring to the boardroom table, we first show a significant gender gap in viewing the tradeoff between environmental and economic benefits, using the Gallup Poll. We next demonstrate that such a gender gap extends into boardrooms. Having female directors is associated with more environmentally friendly business operations, but also with higher investment in environmental protection at the same time. Results from an analysis using a California law change that imposed board gender quotas point to a potentially causal effect of female directors. Employing a rich set of director demographics and board characteristics, we show that none consistently supersedes the share of female directors in explaining corporate environmental performance, suggesting that female directors play a unique role in explaining firms’ investment in environmental protection. This paper was accepted by Caroline Flammer, sustainability. Funding: P.-H. Hsu acknowledges the Yushan Fellow Program by the Ministry of Education and the National Science and Technology Council, Taiwan [Grants MOE-108-YSFMS-0004-012-P1 and NSTC 113-2410-H-007-008-MY3], the Mack Institute for Innovation Management at the Wharton School, University of Pennsylvania, and the E.SUN Academic Award for financial and research support. K. Li received financial support from the Canada Research Chair in Corporate Governance, the Social Sciences and Humanities Research Council of Canada [Grant 435-2022-0285], and the Montalbano Centre for Responsible Leadership Development at UBC Sauder School of Business.

We propose an exact linear programming (LP)-based solution approach to the Guaranteed Service Model (GSM), one of the most widely applied models for optimizing safety stock placement in supply chain networks. Our approach handles any directed acyclic network and any cost function that depends on a stage’s incoming and outgoing service times. It scales polynomially in the number of nodes n in the network, pseudo-polynomially with respect to the bit size of the maximum replenishment time M, and (for fixed M) exponentially in its treewidth, which quantifies how “tree-like” a network is and can be much smaller than n. This contrasts with existing approaches, which scale exponentially in n. The proof of exactness relies crucially on showing that the join of transportation-like polytopes remains integral and is more broadly applicable to other Operations Management problems. In addition to an exact formulation, our LP-based approach enables a practical solution strategy for the GSM built on a hierarchy of LP relaxations. These relaxations provide valid lower bounds, certify optimality when integral, and can strengthen existing exact methods. In our computational study, the smallest relaxation already recovers an optimal GSM solution on every real-world benchmark instance, leading to substantial speed-ups over the state-of-the-art exact algorithm and commercial general-purpose solvers. The framework also supports sensitivity analysis and accommodates additional operational constraints. Overall, our approach builds a new bridge between Operations Management and Computer Science, providing new theoretical foundations and practical tools for managing safety stocks in complex modern supply chain networks.

By extracting information from economic news articles, this paper proposes a novel measure of narrative-based ambiguity that captures investors’ attitudes toward narrative uncertainty and exhibits strong in- and out-of-sample predictive power for the stock market returns. It reveals general ambiguity aversion except in high loss probability scenarios in which ambiguity tolerance emerges. Further tests confirm the significant pricing power and distinct information of the narrative ambiguity on top of existing ambiguity measures, such as survey- and return-based ambiguity. By aligning industry-specific narrative ambiguity, we construct a superior predictor for market returns with the predictability predominantly driven by the ambiguity from consumption-, energy-, and technology-related sectors. Our findings also carry broad implications as the predictability remains significant across international markets and other asset classes. This paper has been accepted by Will Cong for the Virtual Special Issue on Digital Finance.

We study a newsvendor problem with unknown demand distribution in a nonstationary demand environment over a multiperiod time horizon. The demand in each period consists of a time-varying demand level and an additive random shock. Neither the demand level nor the random shock is separately observable. The amount of change in the demand level over the time horizon is measured by a cumulative variation metric. The problem has widespread applications, such as perishable inventory planning, staffing, and medical resource capacity planning in the wake of COVID-19. We design a family of nonparametric dynamic ordering policies, termed two-stage estimation (2SE) policies, that track the shifts in the unknown demand level while accounting for the unobservable random demand shocks. To compute the order quantity in each period, these policies only need the past demand observations, without any access to the underlying demand distribution. For a finite variation “budget,” we prove that our ordering policies are first-order optimal in the sense that their regret grows at the smallest possible rate. We also extend our analysis to the case of asymptotically large variation budgets. Through case studies based on real-life data, we show that our policies can save more than 20% of overage and underage costs, relative to policies widely used for perishable inventory replenishment and nurse staffing. Moreover, our simulation experiments indicate that our policies consistently maintain superior performance across diverse patterns of nonstationary demand environments.

Supply uncertainty, characterized by stochastic lead times and random supply quantities, has attracted increasing attention from academia, industries, and governments, particularly in the aftermath of the COVID-19 pandemic. In this paper, we consider the problem of managing lost-sales inventory systems with general supply uncertainty: stochastic lead times and random supplies. Unlike the previous studies, we assume the decision maker has no prior information on the stochastic demand and supply. We propose the first provably effective learning algorithm for inventory management problems with censored demand and supply data under general supply uncertainty. Then, we establish a cumulative regret of [Formula: see text] for this learning algorithm compared with the best constant-order policy, where [Formula: see text] is the upper bound of the random part, and L is the deterministic part of the stochastic lead times. We also conduct numerical experiments to demonstrate the effectiveness of our algorithm. Our approach lies in developing a new framework for transformed convexity. Furthermore, we address the unique challenges of our problem through new techniques, for example, estimating the long-run cost by establishing coupling and concentration results utilizing the system structures. These techniques are also of independent interest. Beyond our problem, our framework provides broad implications for other operations management (OM) problems exhibiting transformed convexity.

Do job seekers consider the race or gender of an employer when applying for a job? Although we have extensive research on employer-side discrimination, we know less about employee-side biases and their consequences. In this study, we examine how the gender and race of the employer shape the willingness of prospective employees to apply for a job. To examine this, we conducted a field experiment where we randomized real jobseekers into three conditions according to employer demographics. We find that job candidates were less likely to apply to a job when they learn that the founders are Black, and, when they did apply, they requested 10% higher salary. In addition, the more qualified a candidate, the less likely they were to apply to Black founders, leaving Black founders with a pool of candidates that is smaller, worse, and more expensive than their White peers. We find no gender penalty for White female founders. Findings from two survey experiments suggest that the penalty is unique to White applicants evaluating Black founders and reflects a concern among White applicants that they will be less likely to fit within a firm and that the firm is less likely to be successful in the long run. We find no evidence of a widespread applicant homophily where all applicants favor founders of their own ethnic group, nor do we find evidence of widespread statistical discrimination whereby all applicants penalize Black founders for being atypical members of the entrepreneurial class.

This paper investigates how environmental regulatory uncertainty affects green innovation in polluting firms. The findings suggest that, instead of adopting a passive “wait-and-see” strategy, polluting firms proactively engage in green innovation and R&D activities. To address endogeneity, I employ an instrumental variable approach using political polarization in roll-call votes on environmental and climate issues in the U.S. House of Representatives. I also exploit the proposal of the Affordable Clean Energy rule in 2018 as an exogenous shock in a difference-in-differences framework. The results are consistent with the growth options view of green innovation in polluting firms. Additional evidence suggests that environmental regulatory uncertainty leads to reductions in toxic emissions.