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When do scientists and other innovators organize into collaborative teams, and why do they do so for some projects and not others? At the core of this important organizational choice is, we argue, a trade-off scientists make between the productive efficiency of collaboration and the credit allocation that arises after the completion of collaborative work. In this paper, we explore this trade-off by developing a model to structure our understanding of the factors shaping researcher collaborative choices, in particular the implicit allocation of credit among participants in scientific projects. We then use the annual research activity of 661 faculty scientists at the Massachusetts Institute of Technology over a 31-year period to explore the trade-off between collaboration and reward at the individual faculty level and to infer critical parameters in the collaborative organization of scientific work. Keywords: science; collaboration; academic science; productivity; scientific credit History: Received September 19, 2011; accepted June 25, 2014, by Lee Fleming, entrepreneurship and innovation. Published online in Articles in Advance February 27, 2015., 1. Introduction In 2008, the Journal of Instrumentation published a paper entitled 'The ATLAS Experiment at the CERN Large Hadron Collider,' documenting the installation and expected performance of the ATLAS [...]

In December 1971, a ten page constraint was instituted for most papers published in Management Science. The implications of this page constraint policy are examined, and a modified policy is proposed which retains all of the advantages of the current policy while eliminating some of its disadvantages. [ABSTRACT FROM AUTHOR]

The article presents the meeting schedule for The Institute of Management Science (TIMS). The 13th International meeting will take place in Philadelphia, Pennsylvania, from September 6-8, 1966. Warsaw, Poland, will host the European Section meeting from September 2-7, 1966. The American meeting is scheduled for April 5-7, 1967 in Boston, Massachusetts. Detailed meeting information is provided.

This paper addresses primarily two questions. First, when (and why) should a company's strategy be developed by its CEO versus by some outside analyst or other insider? Second, how does strategy interact with vision (in the sense of a strong belief about the right course of action)? The paper studies these questions using a functional definition of strategy as "the smallest set of choices to optimally guide other choices." Among other things, the paper shows that strategy formulation by the CEO leads to both better strategy and better execution, and that a strategist's vision may improve execution. In the process, the paper also identifies criteria that make a decision strategic and derives explanations why strategies often reflect the background of the strategist. This paper was accepted by Bruno Cassiman, business strategy. [ABSTRACT FROM AUTHOR]

Cryptocurrencies provide a unique opportunity to identify how derivatives impact spot markets. They are fully fungible and trade across multiple spot exchanges at different prices, and futures contracts were selectively introduced on Bitcoin (BTC) exchange rates against the U.S. dollar (USD) in December 2017. Following the futures introduction, we find a significantly greater increase in cross-exchange price synchronicity for BTC–USD relative to other exchange rate pairs as demonstrated by an increase in price correlations and a reduction in arbitrage opportunities and volatility. We also find support for an increase in price efficiency, market quality, and liquidity. The evidence suggests that futures contracts allowed investors to circumvent arbitrage frictions associated with short-sale constraints, arbitrage risk associated with block confirmation time, and market segmentation. Overall, our analysis supports the view that the introduction of BTC–USD futures was beneficial to the Bitcoin spot market by making the underlying prices more informative. This paper was accepted by Will Cong, Special Section of Management Science: Blockchains and Crypto Economics. Funding: The authors acknowledge financial support from the Global Risk Institute. P. Augustin acknowledges financial support from the Canadian Derivatives Institute and from the Canada Research Chair Program of the Social Sciences and Humanities Research Council Canada. The paper has benefited significantly from a fellow visit of P. Augustin at the Center for Advanced Studies Foundations of Law and Finance funded by the German Research Foundation, project FOR 2774, and from a visiting position of P. Augustin at the finance department of the University of Luxembourg facilitated through the Inter Mobility Programme of the Luxembourg National Research Fund. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4900. [ABSTRACT FROM AUTHOR]

In several transportation systems, vehicles can choose where to meet customers rather than stopping in fixed locations. This added flexibility, however, requires coordination between vehicles and customers that adds complexity to routing operations. This paper develops scalable algorithms to optimize these operations. First, we solve the one-stop subproblem in the ℓ 1 space and the ℓ 2 space by leveraging the geometric structure of operations. Second, to solve a multistop problem, we embed the single-stop optimization into a tailored coordinate descent scheme, which we prove converges to a global optimum. Third, we develop a new algorithm for dial-a-ride problems based on a subpath-based time–space network optimization combining set partitioning and time–space principles. Finally, we propose an online routing algorithm to support real-world ride-sharing operations with vehicle–customer coordination. Computational results show that our algorithm outperforms state-of-the-art benchmarks, yielding far superior solutions in shorter computational times and can support real-time operations in very large-scale systems. From a practical standpoint, most of the benefits of vehicle–customer coordination stem from comprehensively reoptimizing "upstream" operations as opposed to merely adjusting "downstream" stopping locations. Ultimately, vehicle–customer coordination provides win–win–win outcomes: higher profits, better customer service, and smaller environmental footprint. This paper was accepted by Chung Piaw Teo, optimization. Funding: This research was supported by the National Natural Science Foundation of China [Grants 72288101, 52221005 and 52220105001]. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2023.4739. [ABSTRACT FROM AUTHOR]

Folklore suggests that policy gradient can be more robust to misspecification than its relative, approximate policy iteration. This paper studies the case of state-aggregated representations, in which the state space is partitioned and either the policy or value function approximation is held constant over partitions. This paper shows a policy gradient method converges to a policy whose regret per period is bounded by ϵ, the largest difference between two elements of the state-action value function belonging to a common partition. With the same representation, both approximate policy iteration and approximate value iteration can produce policies whose per-period regret scales as ϵ / (1 − γ) , where γ is a discount factor. Faced with inherent approximation error, methods that locally optimize the true decision objective can be far more robust. This paper was accepted by Hamid Nazerzadeh, data science. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2023.4788. [ABSTRACT FROM AUTHOR]

Hospitals throughout the developed world are reimbursed based on diagnosis-related groups (DRGs). Under this scheme, patients are divided into clinically meaningful groups, and hospitals receive a fixed fee per patient episode tied to the patient DRG. The fee is based on the average cost of providing care to patients who belong to the same DRG across all hospitals. This scheme, sometimes referred to as "yardstick competition," provides incentives for cost reduction, as no hospital wants to operate at a higher cost than average, and can be implemented using accounting data alone. Nevertheless, if costs within a DRG are heterogeneous, this scheme may give rise to cherry-picking incentives, where providers "drop" patients who are more expensive to treat than average. To address this problem, regulators have tried to reduce within-DRG cost heterogeneity by expanding the number of DRG classes. In this paper, we show that even if cost heterogeneity is eliminated, such expansion will fail to completely eliminate patient cherry picking. In equilibrium, the market will bifurcate into two groups, one of which will continue to cherry-pick patients and underinvest in cost reduction, whereas the other group treats all patients. Furthermore, we show that DRG expansion is particularly problematic if hospitals are also able to "upcode" patients, that is, intentionally assign patients to a more resource-intensive DRG than needed to increase income. Upcoding increases within-DRG cost heterogeneity and amplifies cherry-picking incentives. We examine potential solutions involving yardstick competition based on input statistics. This paper was accepted by Carri Chan, healthcare management. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2023.4752. [ABSTRACT FROM AUTHOR]

The internet advertising market is a multibillion dollar industry in which advertisers buy thousands of ad placements every day by repeatedly participating in auctions. An important and ubiquitous feature of these auctions is the presence of campaign budgets, which specify the maximum amount the advertisers are willing to pay over a specified time period. In this paper, we present a new model to study the equilibrium bidding strategies in standard auctions, a large class of auctions that includes first and second price auctions, for advertisers who satisfy budget constraints on average. Our model dispenses with the common yet unrealistic assumption that advertisers' values are independent and instead assumes a contextual model in which advertisers determine their values using a common feature vector. We show the existence of a natural value pacing–based Bayes–Nash equilibrium under very mild assumptions. Furthermore, we prove a revenue equivalence showing that all standard auctions yield the same revenue even in the presence of budget constraints. Leveraging this equivalence, we prove price of anarchy bounds for liquid welfare and structural properties of pacing-based equilibria that hold for all standard auctions. In recent years, the internet advertising market has adopted first price auctions as the preferred paradigm for selling advertising slots. Our work, thus, takes an important step toward understanding the implications of the shift to first price auctions in internet advertising markets by studying how the choice of the selling mechanism impacts revenues, welfare, and advertisers' bidding strategies. This paper was accepted by Itai Ashlagi, revenue management and market analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4719. [ABSTRACT FROM AUTHOR]

Battery swapping for electric vehicle refueling is reviving and thriving. Despite a captivating sustainable future where swapping batteries will be as convenient as refueling gas today, a tension is mounting in practice (beyond the traditional "range anxiety" issue): On one hand, it is desirable to maximize battery proximity and availability to customers. On the other hand, capacitated urban power grids may curb decentralized charging at a slow speed. To reconcile this tension, some cities are embracing an emerging infrastructure network: Decentralized swapping stations replenish charged batteries from centralized charging stations. It remains unclear how to design such a network or whether pooling charging demands will save costs or batteries. In this paper, we model this new urban infrastructure network. This task is complicated by non-Poisson swaps and by the intertwined stochastic operations of swapping, charging, stocking, and circulating batteries among swapping and charging stations. We tackle these complexities by deriving analytical models, which enrich the classical batched repairable-inventory theory. We next propose a joint location and repairable-inventory model for citywide deployment of hub charging stations, with a nonconvex nonconcave objective function. We solve this problem exactly by exploiting submodularity and combining constraint-generation and parameter-search techniques. Even for solving convexified problems, our algorithm brings a speedup of at least three orders of magnitude relative to the Gurobi solver. The major insight is twofold: The benefit of pooling charging demands alone is not enough to justify the adoption of the "swap-locally, charge-centrally" network; instead, the main justification is that faster charging accessible at centralized charging stations significantly reduces the system-wide battery stock level. In a broader sense, this work deepens our understanding of how mobility and energy are coupled toward enabling smart cities. This paper was accepted by Chung Piaw Teo, optimization. Funding: Y. Zhang acknowledges the support from the National Natural Science Foundation of China [Grants 71871023, 72271029, and 72061127001]. W. Qi acknowledges the support from the National Natural Science Foundation of China [Grants 72272014 and 72188101] and the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2019-04769]. N. Zhang acknowledges the support from the China Scholarship Council [202106030140]. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4731. [ABSTRACT FROM AUTHOR]

This paper examines how a firm's choice of the type of experiment impacts on its potential exploitation of new technological opportunities. It does so in the context of the failure of successful firms (or disruption) where the literature has informally suggested that firms undertake errors in experimental choice. It is shown that firms will generically choose experiments that minimize false positives (a high-bar) or minimize false negatives (a low-bar) rather than strike a balance between the two. This is done to better inform decisions regarding the exploitation of technological opportunities. It is shown that these choices can differ between incumbents and entrants based on their fundamentals and because of the anticipation of competition between them. This paper was accepted by David Simchi-Levi, business strategy. [ABSTRACT FROM AUTHOR]

This paper studies the contribution of analysts to the functioning and failure of the market for initial coin offerings (ICOs). The assessments of freelancing analysts exhibit biases because of reciprocal interactions of analysts with ICO team members. Even favorably rated ICOs tend to fail raising some capital when a greater portion of their ratings reciprocate prior ratings. Ninety days after listing on an exchange, the market capitalization relative to the initial funds raised is smaller for tokens with more reciprocal ratings. These findings suggest that conflicts of interest help explain the failure of ICOs. This paper was accepted by Bruno Biais, Special Section of Management Science: Blockchains and Crypto Economics. Funding: A. Barth acknowledges financial support from the Chaire Fintech at University Paris Dauphine - PSL. A. F. Wagner acknowledges financial support from the University of Zurich Research Priority Program "Financial market regulation." This paper was initiated when A. F. Wagner was visiting the Center for Advanced Studies on the Foundations of Law and Finance, funded by the German Research Foundation under the project FOR 2774 at Goethe University Frankfurt. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2021.02928. [ABSTRACT FROM AUTHOR]

In this paper, we study a family of sales-based rebate mechanisms as an effective tool to implement price discrimination across a population of buyers with correlated heterogeneous valuations on indivisible goods and services. In order to implement such sales-based rebate mechanisms, the seller charges each buyer a fixed price at the time of purchase contingent on a rebate that is a function of the ex post sales volume to be realized at the end of the sales period. The seller declares both a price and a menu of rebates as a function of sales. We show that, when there is a common component of uncertainty in consumers' valuations (to which we refer as the quality of the product), such rebates enable a seller to effectively induce different expected net prices at different valuations. Importantly, this effective price discrimination over valuations is achieved keeping both the base price and the rebate uniform across all buyers. This uniformity of price and rebate across buyers is a key advantage of our proposed rebate mechanism, thereby providing a new mechanism for price discrimination in crowd-based markets. We use tools and techniques from game theory and variational optimization to provide insight into the economics of such mechanisms. In particular, we identify two mechanisms that are monotone functions of the sales volume that are easy to implement in practice and perform well when compared with the much more complex optimal mechanism. We provide a rigorous analysis of the optimal mechanism and discuss practical limitations in implementing the globally optimal design, further demonstrating the efficacy of our proposed monotone mechanisms. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Funding: This research was supported by a Vannevar Bush Fellowship from the Office of Secretary of State and Army Research Office [MURI Project W911NF-19-1-0217]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4691. [ABSTRACT FROM AUTHOR]

On-demand delivery through sharing platforms represents a rapidly expanding segment of the global workforce. The emergence of sharing platforms enables gig workers to choose when and where to work, allowing them to do so in a flexible manner. However, such flexibility brings notorious challenges to platforms in managing the gig workforce. Thus, understanding the incentive and behavioral issues of gig workers in this new business model is inherently meaningful. This paper investigates how the incentive mechanisms of sharing platforms—earnings, ratings, and penalties—affect the working decisions of gig workers and their nuanced relationships. To achieve this goal, we use data from one leading on-demand delivery platform with more than 50 million active consumers in China and implement a two-stage Heckman model with instrumental variables to estimate the impact of earnings, ratings, and penalties. We first show that better ratings motivate gig workers to work more. However, interestingly, when ratings are employed together with earnings, the two positive effects of ratings and earnings can be substitutes for each other. Second, we reveal that higher past penalties discourage workers from working more, whereas, interestingly, workers with higher past penalties tend to be more sensitive toward an increase in earnings. Finally, we conduct follow-up surveys to understand the underlying mechanisms of the observed moderating effects from both psychological and economic perspectives. The ultimate goal of this work is to provide managerial implications to help platform managers understand how earnings, ratings, and penalties work together to affect gig workers' working decisions and how to manage high- and low-quality workers. This paper was accepted by David Simchi-Levi, entrepreneurship and innovation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72192823, 72172169, 71821002, 91646125, 72071206, 72231011, 72025405, and 72088101] and Program for Innovation Research at the Central University of Finance and Economics. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4761. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has seen dramatic demand surges for hospital care that have placed a severe strain on health systems worldwide. As a result, policy makers are faced with the challenge of managing scarce hospital capacity to reduce the backlog of non-COVID patients while maintaining the ability to respond to any potential future increases in demand for COVID care. In this paper, we propose a nationwide prioritization scheme that models each individual patient as a dynamic program whose states encode the patient's health and treatment condition, whose actions describe the available treatment options, whose transition probabilities characterize the stochastic evolution of the patient's health, and whose rewards encode the contribution to the overall objectives of the health system. The individual patients' dynamic programs are coupled through constraints on the available resources, such as hospital beds, doctors, and nurses. We show that the overall problem can be modeled as a grouped weakly coupled dynamic program for which we determine near-optimal solutions through a fluid approximation. Our case study for the National Health Service in England shows how years of life can be gained by prioritizing specific disease types over COVID patients, such as injury and poisoning, diseases of the respiratory system, diseases of the circulatory system, diseases of the digestive system, and cancer. This paper was accepted by Chung-Piaw Teo, optimization. Funding: G. Forchini acknowledges funding from Jan Wallanders and Tom Hedelius Foundation and the Tore Browaldh Foundation, funding from MRC Centre for Global Infectious Disease Analysis [Reference MR/R015600/1], jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement, part of the EDCTP2 program supported by the European Union; and acknowledges funding by Community Jameel. D. Rizmie acknowledges partial funding from the MRC Centre for Global Infectious Disease Analysis [Reference MR/R015600/1]. J. C. D'Aeth acknowledges funding from the Wellcome Trust [Reference 102169/Z/13/Z]. S. Moret acknowledges partial support from the Swiss National Science Foundation (SNSF) under [Grant P2ELP2_188028]. S. Ghosal was funded by the Imperial College President's PhD Scholarship. F. Grimm was funded by the Health Foundation as part of core staff member activity. This research was funded in whole, or in part, by the Wellcome Trust [Grant 102169/Z/13/Z]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4679. [ABSTRACT FROM AUTHOR]

Policy learning using historical observational data are an important problem that has widespread applications. Examples include selecting offers, prices, or advertisements for consumers; choosing bids in contextual first-price auctions; and selecting medication based on patients' characteristics. However, existing literature rests on the crucial assumption that the future environment where the learned policy will be deployed is the same as the past environment that has generated the data: an assumption that is often false or too coarse an approximation. In this paper, we lift this assumption and aim to learn a distributionally robust policy with incomplete observational data. We first present a policy evaluation procedure that allows us to assess how well the policy does under worst-case environment shift. We then establish a central limit theorem type guarantee for this proposed policy evaluation scheme. Leveraging this evaluation scheme, we further propose a novel learning algorithm that is able to learn a policy that is robust to adversarial perturbations and unknown covariate shifts with a performance guarantee based on the theory of uniform convergence. Finally, we empirically test the effectiveness of our proposed algorithm in synthetic datasets and demonstrate that it provides the robustness that is missing using standard policy learning algorithms. We conclude the paper by providing a comprehensive application of our methods in the context of a real-world voting data set. This paper was accepted by Hamid Nazerzadeh, data science. Funding: This work was supported by the National Science Foundation [Grant CCF-2106508] and the Air Force Office of Scientific Research [Award FA9550-20-1-0397]. Z. Zhou also gratefully acknowledges the JP Morgan AI Research Grant and the New York University's Center for Global Economy and Business faculty research grant for support on this work. Additional support is gratefully acknowledged from the National Science Foundation [Grants 1915967 and 2118199]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4678. [ABSTRACT FROM AUTHOR]

An important problem in single-player video game design is how to sequence game elements within a level (or "chunk") of the game. Each element has two critical features: a reward (e.g., earning an item or being able to watch a cinematic) and a degree of difficulty (e.g., how much energy or focus is needed to interact with the game element). The latter property is a distinctive feature in video games. Unlike passive services (like a trip to the spa) or passive entertainment (like watching sports or movies), video games often require concerted effort to consume. We study how to sequence game elements to maximize overall experienced utility subject to the dynamics of adaptation to rewards and difficulty and memory decay. We find that the optimal design depends on the relationship between rewards and difficulty, leading to qualitatively different designs. For example, when the proportion of reward-to-difficulty is high, the optimal design mimics that of more passive experiences. By contrast, the optimal design of games with low reward-to-difficulty ratios resembles work-out routines with "warm-ups" and "cool-downs." Intermediate cases may follow the classical "mini-boss, end-boss" design where difficulty has two peaks. Numerical results reveal optimal designs with "waves" of reward and difficulty with multiple peaks. Level designs with multiple peaks of difficulty are ubiquitous in video games. In summary, this paper provides practical guidance to game designers on how to match the design of single-player games to the relationship between reward and difficulty inherent in their game's mechanics. Our model also has implications for other interactive services that share similarities with games, such as summer camps for children. This paper was accepted by Jeannette Song, operations management. Funding: This work was supported by the National Natural Science Foundation of China [Project 72201210] and Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2020-06488]. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2022.4665. [ABSTRACT FROM AUTHOR]

This paper analyzes a dynamic relational contract for employees with reciprocal preferences. A model of a long-term employment relationship is developed that implies that generous upfront wages activate the norm of reciprocity and then are more important when an employee is close to retirement. In earlier stages, direct incentives promising a bonus in exchange for effort are more effective. Hence, direct and reciprocity-based incentives reinforce each other and should be used in combination. Moreover, a more competitive labor market may increase the utilization of reciprocity-based incentives. This paper was accepted by Axel Ockenfels, behavioral economics and decision analysis. Funding: This work was supported by Hardegg'sche Stiftung, the Deutsche Forschungsgemeinschaft [CRC TRR 190], and the Austrian Science Fund [Grant P 33307-G]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.4657. [ABSTRACT FROM AUTHOR]

In environments with impending regime shifts, previous research studies probability judgments of decision makers (DMs) regarding a regime shift and finds evidence for the system neglect hypothesis: In unstable environments with precise signals, DMs exhibit relatively more underreaction in their probability judgments, and in stable environments with noisy signals, they exhibit relatively more overreaction. In the present paper we focus on pricing decisions and study how risk attitudes, as well as probability judgments regarding a regime shift, affect these decisions. We develop a model that integrates prospect theory with the system neglect hypothesis and compare changes in empirical pricing decisions to those of a Bayesian risk-neutral agent. We find a pattern of under- and overreaction in pricing decisions that mimics the system neglect hypothesis in probability judgments only when the DM's prior probability of a regime shift is low; the system neglect pattern vanishes when the DM's prior probability of a regime shift is high. We argue that in the case of low priors, changes in risk attitude in reaction to signals indicative of a regime shift amplify system neglect in probability judgments. In the case of high priors, on the other hand, changes in risk attitude dampen the effect of system neglect. We test our model predictions in experimental insurance markets and offer various extensions to check for the robustness of our findings. Finally, we discuss implications for managerial practice in organizational contexts that are characterized by continuous change. This paper was accepted by Yuval Rottenstreich, behavioral economics and decision analysis. Funding: This work was funded by Fundação para a Ciência e a Tecnologia (UIDB/00124/2020, UIDP/00124/2020 and Social Sciences DataLab - PINFRA/22209/2016), POR Lisboa and POR Norte (Social Sciences DataLab, PINFRA/22209/2016). Furthermore, it was supported by the Ministerio de Ciencia e Innovación España [10.13039/501100011033, Grants ECO2014-52925-P, PID2019-111512RB-I00-HMDM, and HDL-HS-280218]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4661. [ABSTRACT FROM AUTHOR]

Financing frictions may create a misallocation of assets in a market, thus depressing output, productivity, and asset values. This paper empirically explores how liquidity shocks generate a reallocation that diminishes this misallocation. Using a unique data set of agricultural outcomes, I explore how farmers respond to a relaxation of financial constraints through a liquidity shock unrelated to farming fundamentals, namely exogenous cash inflows caused by an expansion of hydraulic fracturing (fracking) leases. Farmers experiencing positive cash flow shocks increase their land purchases, which results in a reallocation effect. Examining purchases across areas, I find that farmers in high-productivity areas who receive cash flow shocks buy farmland in low-productivity areas, but farmers in low-productivity areas receiving positive cash flow shocks do not. Moreover, farmers increase their purchases of vacant (undeveloped) land. Average output, productivity, equipment investment, and profits all increase significantly following these positive cash flow shocks. Farmland prices also rise significantly, consistent with a cash-in-the-market pricing effect. These effects are consistent with an efficient reallocation of land toward more productive users. This paper was accepted by Tyler Shumway, finance. Funding: This work was supported by the Kritzman and Gorman Research Fund Grant. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4602. [ABSTRACT FROM AUTHOR]

This paper studies the effect of trust on the perceived credibility of earnings news. Using earnings response coefficients, we find that firms located in low-trust regions of the United States experience significantly lower stock price reactions to earnings news. Additional tests indicate that managers can counterbalance investors' dependence on trust by employing reputable auditors or signaling the quality of their earnings using dividends to improve the perceived credibility of their financial reports. Overall, our findings suggest that trust affects the pricing of earnings news in capital markets. This paper was accepted by Brian Bushee, accounting. Funding: This work was supported by Ministry of Education (Singapore) [Grant RG161/14]. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2022.4569. [ABSTRACT FROM AUTHOR]

Over the last two decades, researchers in operations management have increasingly leveraged laboratory experiments to identify key behavioral insights. These experiments inform behavioral theories of operations management, impacting domains including inventory, supply chain management, queuing, forecasting, and sourcing. Yet, until now, the replicability of most behavioral insights from these laboratory experiments has been untested. We remedy this with the first large-scale replication study in operations management. With the input of the wider operations management community, we identify 10 prominent experimental operations management papers published in Management Science, which span a variety of domains, to be the focus of our replication effort. For each paper, we conduct a high-powered replication study of the main results across multiple locations using original materials (when available and suitable). In addition, our study tests replicability in multiple modalities (in-person and online) due to laboratory closures during the COVID-19 pandemic. Our replication study contributes new knowledge about the robustness of several key behavioral theories in operations management and contributes more broadly to efforts in the operations management field to improve research transparency and reliability. This paper was accepted by David Simchi-Levi, operations management. Funding: The authors gratefully acknowledge financial support from Cornell University, the University of South Carolina, the University of Texas at Dallas, the University of Michigan, and the University of Wisconsin-Madison. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4866. [ABSTRACT FROM AUTHOR]

Decision making on the job is becoming increasingly important in the labor market, in which there is an unprecedented rise in demand for workers with problem-solving and critical-thinking skills. This paper investigates how indoor air quality affects the quality of strategic decision making based on data from official chess tournaments. Our main analysis relies on a unique data set linking the readings of air-quality monitors inside the tournament room to the quality of 30,000 moves, each of them objectively evaluated by a powerful artificial intelligence–based chess engine. The results show that poor indoor air quality hampers players' decision making. We find that an increase in the indoor concentration of fine particulate matter (PM 2.5 ) by 10 μ g / m 3 (corresponding to 75% of a standard deviation in our sample) increases a player's probability of making an erroneous move by 26.3%. The decomposition of the effects by different stages of the game shows that time pressure amplifies the damage of poor air quality to the players' decisions. We implement a number of robustness checks and conduct a replication exercise with analogous move-quality data from games in the top national league showing the strength of our results. The results highlight the costs of poor air quality for highly skilled professionals faced with strategic decisions under time pressure. This paper was accepted by Prof. Yan Chen, behavioral economics and decision analysis. Funding: The authors gratefully acknowledge the financial support from the Graduate School of Business and Economics at Maastricht University as well as the Institute of Labor Economics Bonn. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4643. [ABSTRACT FROM AUTHOR]

Host-generated property images as a visual channel reveal substantial information about properties. Selecting proper images to display can lead to higher demand and increased rental revenue. In this paper, we define, estimate, and optimize the impacts of Airbnb photos on customers' renting decisions. We apply ResNet-50, a convolutional neural network model, to build two separate, supervised learning models to evaluate the image quality and room types posted by Airbnb hosts. Then, we characterize the overall impacts of photo layout by the room type featured in the photo, photo quality, and order of display on the listings' web pages. To address two estimation challenges in the Airbnb setting, namely, censored demand and changing consideration sets, we propose a novel pairwise comparison model that utilizes customers' booking sequence data to consistently estimate the impact of photo layout on customers' renting decisions. Our estimation results suggest that the cover image has a significantly larger impact than noncover photos and a high-quality bedroom cover image leads to the largest increase in demand. Furthermore, we build a nonlinear integer programming optimization problem and develop an algorithm to determine the optimal photo layout. Our counterfactual analysis suggests that a listing's unilateral adoption of optimal photo layout leads to 11.0% more bookings on average. Moreover, depending on the neighborhood and market size, when listings simultaneously switch to the optimal photo layout, they get booked for two to five additional days in a year on average, which boosts revenue by $500 to $1,100. This paper was accepted by Swaminathan, Jayashankar, operations management. Funding: This research was partially sponsored by the MIT Data Science Lab and also benefited from generous support provided by Zalando. Supplemental Material: The online companion and data are available at https://doi.org/10.1287/mnsc.2022.4616. [ABSTRACT FROM AUTHOR]

Brand valuation methods traditionally focus on the value a brand generates via its ability to enhance demand and, accordingly, profitability. However, this paper explores how a brand can generate value for a firm through the ability to deter entry of new competitors. In this respect, we distinguish between a brand's direct effect on demand and its strategic effect on the behavior of rival firms. We investigate this within the context of the U.S. stacked chips category using a dynamic model that endogenizes brand building and entry decisions. We find that up to 63% of a brand's value can be derived from its ability to deter entry. Furthermore, we find that a brand is most valuable when the cost of entry that potential entrants face is moderate: neither too high nor too low. This paper was accepted by Matt Shum, marketing. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4608. [ABSTRACT FROM AUTHOR]

This paper studies how liquidity management affects asset markets and aggregate economic performance. We explore the connection between liquidity allocation and capital allocation. The policy that changes the liquidity premia on assets has broad implications for private-sector behavior and market efficiency. Liquidity tightening, that is, a reduction in the aggregate quantity of liquid debt, is associated with credit loosening, that is, an increase in funds raised through the issuance of private liquid debt. A positive liquidity premium on debt is required for capital-allocation efficiency, while a zero-liquidity premium is required for liquidity-allocation efficiency. The optimal policy balances these two considerations. Government purchase of private debt is needed to support low interest rates. The analysis is extended to incorporate the liquidity differential between public and private debt and partially liquid capital. This paper was accepted by Lukas Schmid, finance. Funding: This work was supported by the National Natural Science Foundation of China [Grant 71973058]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4559. [ABSTRACT FROM AUTHOR]

This paper reconsiders the wide agreement that females are more risk averse than males. We survey the existing experimental literature, finding that significance and magnitude of gender differences are task specific. We gather data from 54 replications of the Holt and Laury risk elicitation method, involving about 7,000 subjects. Gender differences appear in less than 10% of the studies and are significant but negligible in magnitude once all the data are pooled. Results are confirmed by structural estimations, which also support a constant relative risk aversion representation of preferences. Gender differences correlate with the presence of a safe option and fixed probabilities in the elicitation method. This paper was accepted by John List, behavioral economics. [ABSTRACT FROM AUTHOR]