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Organizations today have access to enormous data sets, and sophisticated business analytical tools are needed to harness the tremendous potential of these data to improve day-to-day decision making. To encourage further developments in this rapidly growing area of interdisciplinary research, Management Science will publish a special issue dedicated to business analytics. Applications of business analytics extend to nearly all managerial functions in an organization. Using microlevel data, mathematical models can become more realistic, be validated more easily, and match more closely true behavioral phenomena. Through business analytics, historical data can be used to discover relationships among important elements in an organization's environment, make more formal statistical inferences regarding the structure or strength of these relationships, and improve actions taken by the organization. Because these relationships are often not static, the entire cycle of identification, estimation, prediction, and decision making is repeated frequently and on an ongoing basis, leading to real-time and highly targeted data-driven decisions. We envision business analytics applied to many domains, including, but surely not limited to: digital market design and operation; network and social-graph analysis; pricing and revenue management; targeted marketing and customer relationship management; fraud and security; sports and entertainment; retailing to healthcare to financial services to many other industries. We seek novel modeling and empirical work which includes, among others, probability modeling, structural empirical models, and/or optimization methods. Submission: Please submit your manuscript online via http://mc.manuscriptcentral.com/ms You must select "Special Issue" as the Manuscript Type in Step 1 and select one of the issue's four coeditors in Step 5. Manuscripts will generally be assigned to one of the Associate Editors for this issue (see below; please recommend three in Step 4), but you may also recommend guest Associate Editors.

Recently, a growing number of Operations Management researchers have started to investigate the interface between the two fields leveraging the institutional richness of Operations Management research, and proposing ways to operationally hedge operational and competitive exposures not effectively handled through typical financial hedging contracts. It is now timely to compile state-of-theart research at the interface of Finance and Operations Management in a special issue of Management Science. Such research will capitalize on the richness of the two fields while exploiting gaps left in their interfaces, with clear objectives: • contributing to the theory of at least one of the fields, while rigorously representing knowledge of the other. • creating managerial insights, practical knowledge and/or tools that general managers and risk management practitioners can use in their decisions and applications. The goal of this call for papers is to include research that shows conditions under which a tighter integration of both functions within and across enterprises leads to higher value creation, while at the same time advances the knowledge and creates tools for enterprise risk management. We solicit contributions spanning a diverse set of topics and using rigorous methodological approaches as appropriate to the question under study (theoretical modeling, conceptual frameworks, empirical, survey research, experimental etc.). Topics we are interested in, but not limited to: • Joint financing and capacity-investment choices • Integrated financial and operational hedging • Risk issues in commodity procurement • Real-options valuation and hedging in operations management • Global risk-management in manufacturing • Supply-chain contracting • Cross-functional approaches to managing large project risks • Risk measures, performance evaluation and incentives • Managing disruption and catastrophic risks in global supply chains: Building the Resilient Enterprise • Insurance and supply chain risks • Managing global outsourcing risks • Cross-functional approaches to managing new product and technology development risks • Managing operational risks in the service sector (banking, entertainment, travel, rentals etc.) • Operationalizing supply-chain risk management • Risk aversion in supply-chain management • Revenue management: financial implications of capacity-planning and dynamic pricing strategies • Risk considerations in revenue management • Special topics in sectors such as financial services and energy and commodity trading Submitted papers should feature original research contributions, display the rigor emphasized in the editorial statement of Management Science, clearly articulate their managerial relevance, and obey the journal’s author guidelines. The review process will be handled by the guest editors and an ad-hoc set of associate editors representing the fields of Operations and Supply Chain Management, Operations Research, Financial Economics and Applied Finance, and Financial Engineering. Submissions are due by July 31, 2007. Publication of the special issue is expected in early 2009. Paper provisions and submission guidelines are available at http://mansci.pubs.informs.org/.

A simple linear model is constructed for production, use and recycling of various paper and related products. This model is used to estimate the maximum feasible recycling rate given the current state of pulp and paper technology. Parametric linear programming is used to investigate the effect of the efficiency of collecting, sorting and transporting waste paper on the fraction recycled.

Understanding the drivers of market concentration in the generic pharmaceutical industry is essential to guaranteeing the availability of low-cost generics. In this paper, we develop a structural model to capture the multiple determinants governing manufacturers’ entry decisions; in particular, we focus on how manufacturing complexity and the regulatory environment for generics approval affect concentration in drug markets. We estimate the model using data collated from six disparate sources. We find that manufacturing complexity, as reflected in the number of active ingredients, for example, significantly reduces the likelihood of generics entry. Moreover, the delay in the review process for the generics applications significantly affects the number of firms entering a market. Our policy simulations suggest that a shortened drug approval review time significantly increases the average number of entrants per market and reduces the fraction of markets with no generics entry; however, a notable portion of markets would still lack any generic competition. This paper was accepted by Jayashankar Swaminathan, operations management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4423 .

When multiple retailers hold inventory to satisfy random demand, retailer inventory-sharing strategies can potentially reduce the supply-demand mismatch and increase overall supply chain performance. In this paper, we experimentally investigate alternative inventory-sharing strategies in a two-tier supply chain with an upstream manufacturer and two downstream retailers. In one setting, retailers act as if they are centralized and use a single quantity to fulfill joint demand. In the other, retailers are decentralized and face separate demands, but they can transfer inventory after demands are realized. In this latter decentralized scenario, we also consider whether the manufacturer or retailers have decision authority over the inventory transfer price. One key result is that when the retailers are decentralized and the manufacturer sets the transfer price, both retailers and the manufacturer earn higher profits than in the centralized retailer strategy, which runs counter to theory. We also find that when retailers are decentralized and set their own transfer price, the most equitable distribution of profits is achieved. In an effort to account for these results, we find that a model of fairness captures decisions well. Overall, by investigating how different inventory-sharing strategies affect the distribution of profits in a two-tier supply chain, our results provide guidance to firms considering how, if at all, they should enter such arrangements. This paper was accepted by Jay Swaminathan, operations management. Funding: The authors acknowledge financial support from Cornell University and the University of Virginia. Supplemental Material: The data files and electronic companion are available at https://doi.org/10.1287/mnsc.2022.4323 .

This paper examines the impact of Airbnb on the local rental housing market. Airbnb provides landlords an alternative opportunity to rent to short-term tourists, potentially leading some landlords to switch from long-term rentals and thereby, affecting rental housing supply and affordability. Despite recent government regulations to address this concern, it remains unclear how many and what types of properties are switching. Combining Airbnb and American Housing Survey data, we estimate a structural model of property owners’ decisions and conduct counterfactual analyses to evaluate various regulations. We find that Airbnb mildly cannibalizes the long-term rental supply. Cities where Airbnb is more popular experience a larger rental supply reduction, but they do not necessarily have a larger percentage of switchers. Affordable units are the major sources of both the negative and positive impacts of Airbnb. They cause a larger rental supply reduction, which harms local renters; they also create a larger market expansion effect, which benefits local hosts who own affordable units and may be less economically advantaged. Policy makers need to strike a balance between local renters’ affordable housing concerns and local hosts’ income source needs. We also find that imposing a linear tax is more desirable than limiting the number of days a property can be listed. We propose a new convex tax that imposes a higher tax on expensive units and show that it can outperform existing policies in terms of reducing cannibalization and alleviating social inequality. Finally, Airbnb and rent control can exacerbate each other’s negative impacts. 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.2021.4288 .

One advantage of online retail is that a large number of products can be displayed at low cost. However, online retailers must decide which of these products to carry in inventory (stock items) and which to order from suppliers when a customer places an order (nonstock items). In this paper, we empirically investigate how carrying a product in inventory affects its online sales. We use data from a European furniture and interior design retailer consisting of daily sales transactions and inventory data covering 18 months. We use a quasi-natural experiment—random transitions of products in and out of inventory at the retailer’s central warehouse—to estimate the causal effect of carrying inventory on sales. Our results show a strong and statistically significant increase in sales of, on average, 65% associated with having the product available in stock. More interestingly, this effect differs between products and is moderated by the price of the product: sales of more expensive products are less sensitive to the product being in stock. We use these results to draw insights on which types of items to carry in inventory. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2023.4777 .

Through repeated interactions, firms today refine their understanding of individual users’ preferences adaptively for personalization. In this paper, we use a continuous-time bandit model to analyze firms that recommend content to multihoming consumers, a representative setting for strategic learning of consumer preferences to maximize lifetime value. In both monopoly and duopoly settings, we compare a forward-looking recommendation algorithm that balances exploration and exploitation to a myopic algorithm that only maximizes the quality of the next recommendation. Our analysis shows that, compared with a monopoly, firms competing for users’ attention focus more on exploitation than exploration. When users are impatient, competition decreases the return from developing a forward-looking algorithm. In contrast, development of a forward-looking algorithm may hurt users under monopoly but always benefits users under competition. Competing firms’ decisions to invest in a forward-looking algorithm can create a prisoner’s dilemma. Our results have implications for artificial intelligence adoption and for policy makers on the effect of market power on innovation and consumer welfare. This paper was accepted by Dmitri Kuksov, marketing. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4722 .

Voluntary environmental certification programs have been a popular tool used by governments, industry groups, and nonprofit organizations alike. A central question in the design of such programs is who should pay for them. In a context where firms respond strategically to a certification, the answer to this question is a priori ambiguous and, ultimately, empirical. This paper provides important insights on this question using ENERGY STAR, a voluntary certification program for energy-efficient products, as a case study. I show that firms are highly strategic with respect to this certification and extract consumer surplus associated with certified products via three mechanisms. They offer products that bunch at the certification requirement, differentiate certified products in the energy and nonenergy dimensions, and charge a price premium on certified products. I use these findings to motivate a structural econometric model with firms’ strategic behaviors with respect to product line and pricing decisions and to investigate the incidence of a certification licensing fee to fund the certification program. This paper was accepted by Juanjuan Zhang, marketing.

Policy makers in many developing countries use maximum price or markup policies to control pharmaceutical costs, which represent 20%–60% of their overall healthcare expenditure. We study the price effect of price ceiling policies by exploiting a major policy shift in China: the elimination of longstanding ceilings on retail drug prices. We collect weekly price and characteristics data on more than 4,500 drug stock keeping units (SKUs) from a leading pharmacy chain. By comparing the rate of discontinuous price jumps across drugs with and without price ceilings during the years before and after the policy change, we find that while price ceilings are effective in containing the prices of some drugs, they can lead to higher prices for others, particularly if the ceilings are set at the national level irrespective of local economic conditions. About 5% of nationally controlled drugs (or more than 125 drugs) had inflated prices because of price ceilings, with an average price inflation of 10%. We attribute this perverse price effect to focal point pricing and asymmetric information about production costs. Further supporting this view, we find the perverse price effect most prominent in lower-income regions where the centrally set price ceilings are arbitrarily high considering their poorer economic conditions. Moreover, drugs with highly concentrated production and less elastic demand face heightened risks of inflated prices under price ceilings. Finally, based on a sample of drugs with available price ceiling data, we find that drugs with manufacturer-specific ceilings are 100% more likely to be priced at or near their ceilings and 70% more likely to experience price drops once the ceilings are removed compared with other drugs with regular ceilings. Overall, this paper documents the unintended perverse effect of price ceilings in pharmaceutical markets and sheds lights on the ongoing debate of drug price regulation. This paper was accepted by Stefan Scholtes, healthcare management.

Motivated by challenges facing IT procurement, this paper studies a hybrid procurement model in which a reverse auction of a fixed-price IT outsourcing contract may be followed by renegotiation to extend the contract’s scope. In this model, the buyer balances the needs to incentivize noncontractible vendor investment and to curb the winning vendor’s information rent by choosing the initial project scope and the buyer’s investment in the quality of the project. We find that a buyer may benefit from inducing ex post renegotiation to motivate vendor investment, especially when the winning vendor has high bargaining power and the quality uncertainty is low. Broadening the initial scope reduces information rent but leaves little room for ex post renegotiation and, hence, discourages vendor investment, whereas increasing the buyer’s investment has opposite effects. Interestingly, the two measures can be strategic substitutes or complements depending on the likelihood of the renegotiation and the two parties’ bargaining powers. The buyer may strategically set a low initial project scope and high investment to incentivize renegotiation and vendor investment, which may explain why many IT outsourcing projects start small and allow expansions. Our findings also generate several testable predictions for IT outsourcing. This paper was accepted by Kartik Hosanagar, information systems.

This paper examines the information content of insider silence, periods of no insider trading. We hypothesize that, to avoid litigation risk, rational insiders do not sell own-company shares when they anticipate bad news; neither would they buy, given unfavorable prospects. Thus, they keep silent. By contrast, insiders sell shares when they do not anticipate significant bad news. Future stock returns are significantly lower following insider silence than following insider net selling, especially among firms with higher litigation risk. We examine two quasinatural experiments where new laws result in changes in shareholder litigation risks for insiders. In both cases, with higher shareholder litigation risks, stocks where insiders stay silent earn significantly lower returns than other stocks. This paper was accepted by Karl Diether, finance.

Driving is an integral component of many operational systems, and any small improvement in driving quality can have a significant effect on accidents, traffic, pollution, and the economy in general. However, making improvements is challenging given the complexity and multidimensionality of driving as a task. In this paper, we investigate the effectiveness of nudging to improve driving performance. In particular, we leverage a smartphone application launched by our industry partners to send three types of nudges through notifications to drivers, indicating how they performed on the current trip with respect to their personal best, personal average, and latest driving performance. We measure the resulting driving performance using telematics technology (i.e., real-time sensor data from an accelerometer, Global Positioning System (GPS), and gyroscope in a mobile device). Compared with the “no-nudge” control group, we find that personal best and personal average nudges improve driving performance by approximately 18% standard deviations of the performance scores calculated by the application. In addition, these nudges improve interaccident times (by nearly 1.8 years) and driving performance consistency, as measured by the standard deviation of the performance score. Noting that driving abilities and feedback seeking may vary across individuals, we adopt a generalized random forest approach, which shows that high-performing drivers who are not frequent feedback seekers benefit the most from personal best nudges, whereas low-performing drivers who are also frequent feedback seekers benefit the most from the personal average nudges. Finally, we investigate the potential mechanism behind the results by conducting an online experiment in a nondriving context. The experiment shows that the performance improvements are directly driven by the changes in participants’ effort in response to different nudges and that our key findings are robust in alternative (nondriving) settings. Our analysis further shows that nudges are effective when the variability in reference points is low, which explains why the personal best and personal average nudges are effective, whereas the last score nudge is not. This paper was accepted by Vishal Gaur, operations management.

The evaluation and selection of novel projects lies at the heart of scientific and technological innovation, and yet there are persistent concerns about bias, such as conservatism. This paper investigates the role that the format of evaluation, specifically information sharing among expert evaluators, plays in generating conservative decisions. We executed two field experiments in two separate grant-funding opportunities at a leading research university, mobilizing 369 evaluators from seven universities to evaluate 97 projects, resulting in 761 proposal-evaluation pairs and more than $250,000 in awards. We exogenously varied the relative valence (positive and negative) of others’ scores and measured how exposures to higher and lower scores affect the focal evaluator’s propensity to change their initial score. We found causal evidence of a negativity bias, where evaluators lower their scores by more points after seeing scores more critical than their own rather than raise them after seeing more favorable scores. Qualitative coding of the evaluators’ justifications for score changes reveals that exposures to lower scores were associated with greater attention to uncovering weaknesses, whereas exposures to neutral or higher scores were associated with increased emphasis on nonevaluation criteria, such as confidence in one’s judgment. The greater power of negative information suggests that information sharing among expert evaluators can lead to more conservative allocation decisions that favor protecting against failure rather than maximizing success. This paper was accepted by Alfonso Gambardella, business strategy.

This paper broadens the focus of empirical research on salesforce management to include multitasking settings with multidimensional incentives, where salespeople have private information about customers. This allows us to ask novel substantive questions around multidimensional incentive design and job design while managing the costs and benefits of private information. To this end, the paper introduces the first structural model of a multitasking salesforce in response to multidimensional incentives. The model also accommodates (i) dynamic intertemporal tradeoffs in effort choice across the tasks and (ii) salesperson’s private information about customers. We apply our model in a rich empirical setting in microfinance and illustrate how to address various identification and estimation challenges. We extend two-step estimation methods used for unidimensional compensation plans by embedding a flexible machine learning (random forest) model in the first-stage multitasking policy function estimation within an iterative procedure that accounts for salesperson heterogeneity and private information. Estimates reveal two latent segments of salespeople—a hunter segment that is more efficient in loan acquisition and a farmer segment that is more efficient in loan collection. Counterfactuals reveal heterogeneous effects: hunters’ private information hurts the firm as they engage in adverse selection; farmers’ private information helps the firm as they use it to better collect loans. The payoff complementarity induced by multiplicative incentive aggregation softens adverse specialization by hunters relative to additive aggregation but hurts performance among farmers. Overall, task specialization in job design for hunters (acquisition) and farmers (collection) hurts the firm as adverse selection harm overwhelms efficiency gain. This paper was accepted by Duncan Simester, marketing.

In this paper, we investigate the impact of ownership structure on corporate advertising expenditures. Using mutual fund mergers as an exogenous shock to ownership structure, we find that competing firms owned by the same institutional blockholders experience a significant reduction in advertising expenditure. The reduction in advertising expenditure is more likely to occur in the presence of higher coordination benefits or lower coordination costs. Specifically, this effect is more pronounced for firms in more competitive industries, in higher advertising-intensity industries, with greater common ownership, with more concentrated institutional ownership, and with headquarters located in the same state. Overall, our empirical evidence indicates that ownership by common institutional investors significantly affects corporate advertising strategy. This paper was accepted by Matthew Shum, marketing.

The paper by Huber (Huber, G. P. 1983. Cognitive style as a basis for designing MIS and DSS: much ado about nothing? Management Sci. 29 (5) 567–579.) stimulates further comment. While his conclusions that cognitive styles research has not served and will not serve as a satisfactory basis for operational DSS design guidelines appear justified, some additional points need to be made. This paper looks at several of Huber's arguments more closely and suggests a potentially valuable role for cognitive style awareness in the DSS development process.

This paper argues that thresholds in financial contracts act as implicit nudges in consumers’ decisions. Exploiting a regulatory change to credit card minimum payments in Mexico, we find that a 1-percentage point change in minimum payments leads to a 0.87-percentage point change in actual payments, both expressed as a percentage of total balances. We decompose the effect of minimum payments into a constraining effect and a reference effect. The former captures the effect of minimum payments as a binding constraint and accounts for 59% of its total effect. The latter captures any remaining impact of changes in minimum payments beyond their constraining effect and represents 41% of the total. In turn, 67% of the reference effect is explained by the multiple heuristic: the tendency of consumers to pay whole-number multiples of the minimum payment. This paper was accepted by Kay Giesecke, finance.

In the era of big data, an important weapon in a machine learning researcher’s arsenal is a scalable support vector machine (SVM) algorithm. Traditional algorithms for learning SVMs scale superlinearly with the training set size, which becomes infeasible quickly for large data sets. In recent years, scalable algorithms have been designed which study the primal or dual formulations of the problem. These often suggest a way to decompose the problem and facilitate development of distributed algorithms. In this paper, we present a distributed algorithm for learning linear SVMs in the primal form for binary classification called the gossip-based subgradient (GADGET) SVM. The algorithm is designed such that it can be executed locally on sites of a distributed system. Each site processes its local homogeneously partitioned data and learns a primal SVM model; it then gossips with random neighbors about the classifier learnt and uses this information to update the model. To learn the model, the SVM optimization problem is solved using several techniques, including a gradient estimation procedure, stochastic gradient descent method, and several variants including minibatches of varying sizes. Our theoretical results indicate that the rate at which the GADGET SVM algorithm converges to the global optimum at each site is dominated by an [Formula: see text] term, where λ measures the degree of convexity of the function at the site. Empirical results suggest that this anytime algorithm—where the quality of results improve gradually as computation time increases—has performance comparable to its centralized, pseudodistributed, and other state-of-the-art gossip-based SVM solvers. It is at least 1.5 times (often several orders of magnitude) faster than other gossip-based SVM solvers known in literature and has a message complexity of O(d) per iteration, where d represents the number of features of the data set. Finally, a large-scale case study is presented wherein the consensus-based SVM algorithm is used to predict failures of advanced mechanical components in a chocolate manufacturing process using more than one million data points. This paper was accepted by J. George Shanthikumar, big data analytics.

Background. Influenza imposes heavy societal costs through healthcare expenditures, missed days of work, and numerous hospitalizations each year. Considering these costs, the healthcare and behavioral science literature offers suggestions on increasing demand for flu vaccinations. Yet, the adult flu vaccination rate fluctuated between 37% and 46% between 2010 and 2019. Aim. Although a demand-side approach represents one viable strategy, an operations management approach would also highlight the need to consider a supply-side approach. In this paper, we investigate how to improve clinic vaccination rates by altering provider behavior. Methodology. We implement and study a flu vaccine intervention among 145 clinics from nine different states. This intervention randomly assigned these clinics to a control group or one of two separate treatment arms that received either relative performance feedback or financial incentives. Results. We find clinics that received relative performance feedback outperformed all others: Our intervention led to a 12% increase in flu shots for this group of clinics. Moreover, we also find clinics in this group exhibit rank response behavior, specifically last-place aversion; in particular, clinics near last place outperform the corresponding control clinics by 23% points. Conclusion. Overall, we find that clinic-level performance feedback can effectively drive operational improvement. Even a small increase in the U.S. adult flu vaccination rate might confer hundreds of millions of dollars in societal benefits and prevents thousands of hospitalizations. We discuss the implications of our work for healthcare operations theory, healthcare providers, and healthcare administrators. This paper was accepted by Charles Corbett, operations management.

This paper examines the behavior of job seekers and recruiters in the labor market for software engineers. I obtained data from a recruiting platform where individuals can self-report their computer programming skills and recruiters can message individuals they wish to contact about job opportunities. I augment this data set with measures of each individual’s previous programming experience based on analysis of actual computer source code they wrote and shared within the open-source software community. This novel data set reveals that candidates’ self-reported technical skills are quantitatively important predictors of recruiter interest. Consistent with social psychology and behavioral economics studies, I also find female programmers with previous experience in a programming language are 11.07% less likely than their male counterparts to self-report knowledge of that programming language on their resume. Despite public pronouncements, however, recruiters do not appear more inclined toward recruiting female candidates who self-report knowing programming languages. Indeed, recruiters are predicted to be 6.47% less likely to express interest in a female candidate than a male candidate with comparable observable qualifications even if those qualifications are very strong. Ultimately, a gender gap in the self-reporting of skills on resumes exists; but recruiters do not appear to be adjusting their response to such signals in ways that could increase the representation of women among software engineering recruits. This paper was accepted by Yan Chen, behavioral economics and decision analysis.

When confronted with a new product, consumers often find it difficult to predict how it will perform, and such uncertainty reduces consumers’ willingness to adopt the product. In this paper, we consider a market whereby consumers decide when and which product to buy, given that they know the product quality of the incumbent but are uncertain about that of the entrant. We investigate how consumer uncertainty about product quality affects firms’ behavior-based pricing and customer acquisition and retention dynamics. Using a two-period vertical model, we find that, under high-end encroachment, an increase in consumer uncertainty reduces the entrant’s profit and hurts the incumbent’s profit when the quality differential between the products is relatively small, whereas, under low-end encroachment, increasing uncertainty not only benefits the incumbent but also can favor the entrant. An important implication for entrants is that the marketing activities, which aim to reduce consumer uncertainty about product functionalities, may fail to improve profitability. We also find that the entrant lowers the price for uninformed customers and raises the price for repeat buyers under high-end encroachment but lowers the price for all customers under low-end encroachment. We further examine the subsidy strategy and show that, when the entrant’s product has a significant quality advantage and consumer uncertainty is high but not very high, the optimal strategy for the entrant is to acquire all consumers who do not buy from the incumbent by providing subsidies and to drop the low-valuation customers by means of a high price after their uncertainty is resolved. This paper was accepted by Anandhi Bharadwaj, information systems.

Operational risk has been among the three most significant types of risks in the financial services industry, and its management is mandated by Basel II regulations. To inform better labor decisions, this paper studies how workload affects banks’ operational risk event occurrence. To achieve this goal, we use a unique data set from a commercial bank in China that contains 1,441 operational risk events over 16 months. We find that workload has a U-shaped impact on operational risk error rate. More specifically, the error rate of operational risk events decreases first, as workload increases, and then increases. Furthermore, when workload is low, employees tend to make performance-seeking risks; however, when workload is high, employees tend to exhibit quality degradation due to cognitive multitasking. Based on the causal relationship between workload and operational risk events from the empirical analysis, we discuss staffing policies among branches aimed at reducing operational risk losses. We find that employing a flexible staffing rule can significantly reduce the number of operational risk events by 3.2%–10% under different scenarios. In addition, this significant performance improvement can be achieved by adding even a little bit of flexibility to the process by allowing employees to either switch their business lines in the same branch or switch branches within the same business lines on a quarterly basis. This paper was accepted by Vishal Gaur, operations management.

Involving suppliers deep in the supply chain is critical for the success of environmental and social responsibility (ESR) initiatives. Administering ESR programs throughout a complex supply network, however, is challenging. In this paper, we apply a multiunit bilateral bargaining framework to coordinate ESR investments in a general supply network and analyze to what extent an ESR initiator should directly engage the higher-tier suppliers as opposed to delegating that responsibility to the first-tier suppliers. Our bargaining framework not only generalizes the conventional Shapley value approach by allowing the flexibility of modeling imbalanced power distribution among the firms but also provides an explicit way of implementing the resulting gain sharing among the firms through negotiated contract terms. We show that the eventual structure of ESR negotiation relationships can be derived by finding a shortest path tree in the supply network with the arc cost defined as the logarithm of the negotiating parties’ relative bargaining power. These developments allow us to analyze ESR implementation in generally extended supply networks. We find that the ESR initiator tends to delegate ESR negotiations to a supplier that is strong in negotiations with higher-tier suppliers. When the supply network is complex (i.e., wide and deep), directly engaging all suppliers can lead to a larger gain by the initiator than fully delegating the negotiations with higher-tier suppliers to the first-tier ones. However, as the network gets increasingly complex, the ESR initiator tends to directly engage a reduced percentage of higher-tier suppliers. We further extend our analysis to situations where the ESR relationships are sequentially formed in a decentralized manner, where the benefit of ESR depends on the collective choice of the firms’ investment levels, where multiple ESR programs are implemented in the network, and where ESR investments depend on the negotiation relationships. This paper was accepted by David Simchi-Levi, operations management.

This paper estimates the heterogeneity in peer effects among research scientists in terms of network position. I propose a new measure, brokerage degree, that determines the extent to which a scientist depends on a coauthor to provide him unique access to other scientists further away. I apply this measure to the coauthorship network of medical scientists. I show that network position is crucial for productivity by facilitating access to nonredundant knowledge. Identification results from variation in brokerage degree among coauthors linked to a star scientist who dies. A one standard deviation increase in the brokerage degree of a deceased star is associated with a 10% decrease in annual publications of his coauthor. By applying brokerage degree to topics, I provide evidence that access to knowledge flows embodied in scientists further away can account for a large proportion of the identified heterogeneity effect. This paper was accepted by Toby Stuart, entrepreneurship and innovation.

Clinical pathways outline standardized processes in the delivery of care for a specific disease. Patient journeys through the healthcare system, however, can deviate substantially from these pathways. Given the positive benefits of clinical pathways, it is important to measure the concordance of patient pathways so that variations in health system performance or bottlenecks in the delivery of care can be detected, monitored, and acted upon. This paper proposes the first data-driven inverse optimization approach to measuring pathway concordance in any problem context. Our specific application considers clinical pathway concordance for stage III colon cancer. We develop a novel concordance metric and demonstrate using real patient data from Ontario, Canada that it has a statistically significant association with survival. Our methodological approach considers a patient’s journey as a walk in a directed graph, where the costs on the arcs are derived by solving an inverse shortest path problem. The inverse optimization model uses two sources of information to find the arc costs: reference pathways developed by a provincial cancer agency (primary) and data from real-world patient-related activity from patients with both positive and negative clinical outcomes (secondary). Thus, our inverse optimization framework extends existing models by including data points of both varying “primacy” and “alignment.” Data primacy is addressed through a two-stage approach to imputing the cost vector, whereas data alignment is addressed by a hybrid objective function that aims to minimize and maximize suboptimality error for different subsets of input data. This paper was accepted by Chung Piaw Teo, Management Science Special Section on Data-Driven Prescriptive Analytics.

This paper investigates the association of corporate reporting and executive network centrality, which measures an executive’s relative position in a massive network consisting of outside corporate leaders. I find that high-centrality chief executive officers (CEOs) and chief financial officers (CFOs) are generally more likely to engage in financial misreporting than low-centrality CEOs and CFOs. I also find that the influence of CFO network centrality is greater than that of CEOs in financial misreporting. Further analyses show that the monitoring effect of internal governance mechanisms on high-centrality executives is very limited and that the discipline of the managerial labor market is weaker for high-centrality CFOs as well. My results hold for a subsample subject to exogenous shocks to CFO connectedness and are robust to a series of alternative specifications including using CFO fixed effects. Taken together, my findings suggest that corporate reporting can be influenced by executives’ social network position, with high-centrality CFOs using their social power to make adverse corporate reporting decisions to gain personal benefits. This paper was accepted by Brian Bushee, accounting.

This paper analyzes the effects of bail-in and bailout policies on banks’ funding costs, incentives for loan monitoring, and financing capacity. In a model with moral hazard and two investment stages, a full bail-in turns out to be, ex post, the optimal policy to deal with a failing bank. Unlike a bailout, it allows the government to recapitalize the bank without resorting to distortionary taxes. As a consequence, however, investors expect bail-ins rather than bailouts. Ex ante, this raises banks’ cost of debt and depresses bankers’ incentives to monitor. When moral hazard is severe, this time inconsistency leads to a credit market collapse in which productive projects are not financed, unless the government precommits to an alternative resolution policy. The optimal policy is either a combination of bail-in and bailout—in which the government uses a minimal amount of public transfers to lower banks’ cost of debt—or liquidation, depending on the severity of moral hazard and the shadow cost of the partial bailout. This paper was accepted by Gustavo Manso, finance.

Advertising is often criticized for presenting only partial or selective information about products. This criticism is particularly pronounced for health products, where large asymmetries in information may exist between consumers and firms. This paper explores how government restrictions designed to prevent selective advertising affect the types of information to which consumers are exposed. We exploit a natural experiment in the form of a U.S. Food and Drug Administration (FDA) crackdown that prevented pharmaceutical companies from using selectively chosen information in their Internet search ads. Because companies could not adequately document side effects within the advertising space allowed, they removed their ads. Our results suggest that, after the ads were removed, consumers were more likely to seek information from websites based on user-generated content or websites that focused on medical treatments not regulated by the FDA, such as Canadian pharmacies and sites promoting herbal remedies. This paper was accepted by Matthew Shum, marketing.

The most commonly cited issues with mobile apps are low user engagement and retention levels. In this paper, we use a randomized control trial to test the efficacy of crowdsourcing on enhancing user engagement and retention in the context of mobile gaming apps. We examine two specific crowdsourcing features: the ability to submit content and the ability to access crowdsourced content. We find that the content submission significantly increases engagement and retention by reducing users’ hazard of ending a session by approximately 11% relative to the baseline and reducing the hazard of abandoning the game app by 14%. In contrast, giving users the ability to access crowdsourced content has no significant effect on engagement but has a positive effect on retention by reducing the hazard of abandoning the game app by 13%. Surprisingly, we find that the interaction effect of these two crowdsourced features is negative on user engagement. Individually, the submission feature manifests itself via empowering users to control their product use experience, whereas the access feature’s positive effect on retention is mediated by diversity and novelty of content. However, the two effects are not complementary. It turns out when these two features are given together, the empowerment enabled by one’s own submission is crowded out by others’ submissions, and this dominates the diversity benefit. Crowdsourcing features have heterogeneous impact on different user segments, with heavy users and users of longer tenure being more affected by the crowdsourcing features. This paper was accepted by Anandhi Bharadwaj, information systems.

This paper shows that hedge fund activism is associated with a decrease in mergers and acquisitions (M&A) and offer premiums and an increase in stock and operating performance. Activist hedge funds improve target firms’ M&A performance by reducing poor M&A, diversifying M&A, and the M&A of firms with multiple business segments. Activist hedge funds improve target firms’ M&A decisions by influencing their governance practices. We show that our results are unlikely driven by selection bias. Overall, activist hedge funds play an important role in the market for corporate control by increasing the efficiency of target firms’ M&A activities through interventions. This paper was accepted by Gustavo Manso, finance.

This paper investigates empirically the effect of market power on dynamic pricing in the presence of inventories. Our setting is the auto retail industry; we analyze how automotive dealerships adjust prices to inventory levels under varying degrees of market power. We first establish that inventory fluctuations create scarcity rents for cars that are in short supply. We then show that dealers’ ability to adjust prices in response to inventory depends on their market power, that is, the quantity of substitute inventory in their selling area. Specifically, we show that the slope of the price–inventory relationship (higher inventory lowers prices) is significantly steeper when dealers find themselves in a situation of high rather than low market power. A dealership with high market power moving from a situation of inventory shortage to a median inventory level lowers transaction prices by about 0.57% ceteris paribus, corresponding to 32.5% of dealers’ average per-vehicle profit margin or $145.6 on the average car. Conversely, when competition is more intense, moving from inventory shortage to a median inventory level lowers transaction prices by about 0.35% ceteris paribus, corresponding to 20.2% of dealers’ average per-vehicle profit margin or $90.9. To our knowledge, we are the first to empirically show that market power affects firms’ ability to dynamically price. This paper was accepted by Juanjuan Zhang, marketing.

This paper studies the persistent effects of short-term peer exposure on long-run performance in a college setting. I exploit the random assignment of undergraduates to peer groups during a mandatory orientation week and track the students’ performance over four years (until graduation). Assignment to orientation week groups with high levels of peer ability is associated with lower performance during the first year at college and a higher probability of early dropout. These adverse effects are driven entirely by the exposure of low-ability students to high-ability peers. Beyond the first year, exposure to higher peer ability during the orientation week negatively affects selection into the college’s most popular major (business administration) and final grade point average. Taken together, the findings suggest that the composition of short-term peer groups matters for individual choices and long-run performance outcomes. This paper was accepted by Yan Chen, decision analysis.

Hospital and healthcare administrators name high prescription drug costs as one of their largest problems. A significant body of research demonstrates that meals and honoraria from pharmaceutical firms to physicians leads to higher prescribing of expensive, brand name drugs, despite little difference in efficacy. Some administrators and scholars have advocated for mandatory disclosure of these payments in order to reduce this conflict of interest, but many practitioners believe disclosure has little effect on prescribing, and the empirical evidence is mixed. This paper uses a quasi-experiment of a 2009 payment disclosure policy in Massachusetts to estimate the causal impact of public disclosure on prescribing. The comprehensive data set includes all retail prescriptions for 262 drugs in nine drug classes written by 5,730 physicians in five states over 48 months. We show a significant postdisclosure reduction in brand name drug prescriptions by Massachusetts physicians, relative to control physicians in other states. These effects are driven by heavy prescribers of brand name drugs in the prepolicy period, particularly for drugs with large prepolicy sales forces. Effects are also detected before the first data were released, implying that the effects are not because patients or administrators responded to the disclosed payments. Instead, some physicians may have changed their payments and prescriptions behavior to avoid appearing biased. Taken in tandem with the many studies showing that pharmaceutical industry payments influence prescribing, this study suggests a strong role for mandatory public disclosure in reducing conflicts of interest in medicine and costly prescribing of brand name drugs. This paper was accepted by Stefan Scholtes, healthcare management.

Prior research suggests portfolio entrepreneurs—businesspeople who run more than one firm simultaneously—launch more successful ventures than their single-business counterparts. However, their ventures are less likely to survive. In an attempt to reconcile this paradox, this paper presents a framework in which portfolio entrepreneurs’ main advantage is not a superior ability to select the best business opportunities ex ante, but rather the ability to redeploy human and capital resources across businesses ex post, which reduces the sunkenness of their investments in new projects. This redeployment option facilitates their exit from new businesses that fail initial market tests. Thus, portfolio entrepreneurs’ heterogeneous termination decisions explain a greater portion of new firm performance differential than ex ante opportunity selection. We test these ideas using a longitudinal data set of more than 5,700 entrepreneurs and find consistent evidence. Portfolio companies do not show systematically higher performance at the time of entry; a performance difference emerges only over time, as the selection effect and resource redeployment occur. This paper was accepted by Ashish Arora, entrepreneurship and innovation.

This paper studies how firms’ internal organization shapes the impact of international trade. Using establishment-level data from the U.S. Census and a difference-in-difference specification, I find that, relative to standalone firms, conglomerates are more likely to restructure after trade liberalization episodes, focusing on their core competency and improving firm productivity and product market performance. Adjustments through the extensive margin account for the majority of the productivity growth differential between conglomerates and standalones experiencing trade shocks. Aggregate industry productivity remains relatively unchanged in industries dominated by conglomerates’ core business but decreases significantly in others. My findings suggest that firms’ internal organization has important consequences on the effects of trade policies. This paper was accepted by Gustavo Manso, finance.

Renegotiations and last-minute contracting are prevalent in many vertical relationships. Information transmission between firms and buyers can be imperfect as well. In this paper we present a theory to explicate how early/delayed contracting over wholesale prices, and partial unraveling of private information, can sustain each other endogenously in a channel setting with bilateral monopoly. Should the wholesale price be predetermined, the downstream manufacturer would be compelled to fully disclose all private information. By contrast, the to-be-negotiated wholesale price can be potentially affected by manufacturer disclosure or concealment. This can represent a countervailing force for equilibrium revelation to be imperfect, even when disclosure is costless. Thus partial unraveling may emerge if and only if no enforceable contract has been signed (i.e., contracting is delayed). Conversely, partial unraveling can endogenously influence ex ante preferences for contract timing. Therefore, contracting may be deliberately delayed even without learning/cost considerations. Moreover, equilibrium contract timing can be socially too early to alleviate channel distortions, and bilateral bargaining can align private and social preferences for delayed contracting. This paper was accepted by Dmitri Kuksov, marketing.

Social media platforms for healthcare services are changing how patients choose physicians. The digitization of healthcare reviews has been providing additional information to patients when choosing their physicians. On the other hand, the growing online information introduces more uncertainty among providers regarding the expected future demand and how different service features can affect patient decisions. In this paper, we derive various service-quality proxies from online reviews and show that leveraging textual information can derive useful operational measures to better understand patient choices. To do so, we study a unique data set from one of the leading appointment-booking websites in the United States. We derive from the text reviews the seven most frequently mentioned topics among patients, namely, bedside manner, diagnosis accuracy, waiting time, service time, insurance process, physician knowledge, and office environment, and then incorporate these service features into a random-coefficient choice model to quantify the economic values of these service-quality proxies. By introducing quality proxies from text reviews, we find the predictive power of patient choice increases significantly, for example, a 6%–12% improvement measured by mean squared error for both in-sample and out-of-sample tests. In addition, our estimation results indicate that contextual description may better characterize users’ perceived quality than numerical ratings on the same service feature. Broadly speaking, this paper shows how to incorporate textual information into an econometric model to understand patient choice in healthcare delivery. Our interdisciplinary approach provides a framework that combines machine learning and structural modeling techniques to advance the literature in empirical operations management, information systems, and marketing. This paper was accepted by David Simchi-Levi, operations management.

The digital content industry is rapidly growing, and many platforms host a vast amount of content that is produced by independent producers. A major source of revenue for these platforms is advertising. However, advertising revenue depends on the number of active customers who visit the platform and enjoy content created by content producers. Therefore, major platforms provide incentives for content producers to encourage high-quality content creation. A commonly used incentive is ad revenue sharing. In this paper, we study how such sharing incentives are affected by the nature of competition among various producers, the size of customer base, and the type of customers. We also examine the implications for profits and welfare and extend the analysis to consider the case when the platform allows consumers to directly donate to the producers. Our results show that increased producer competition can lead to higher compensation for the producers, higher content quality, and higher producers’ profits. We also show that allowing consumers to donate to producers can lead to a higher commission rate and better-quality content. However, increased consumer generosity may lead to lower profits for content producers. In addition, we find that asymmetry among producers can sometimes benefit the platform. This paper was accepted by Matthew Shum, marketing.

Recent years have witnessed the rise of queue scalping in congestion-prone service systems. A queue scalper has no material interest in the primary service but proactively enters the queue in hopes of selling his spot later. This paper develops a queueing-game-theoretic model of queue scalping and generates the following insights. First, we find that queues with either a very small or very large demand volume may be immune to scalping, whereas queues with a nonextreme demand volume may attract the most scalpers. Second, in the short run, when capacity is fixed, the presence of queue scalping often increases social welfare and can increase or reduce system throughput, but it tends to reduce consumer surplus. Third, in the long run, the presence of queue scalping motivates a welfare-maximizing service provider to adjust capacity using a “pull-to-center” rule, increasing (respectively, reducing) capacity if the original capacity level is low (respectively, high). When the service provider responds by expanding capacity, the presence of queue scalping can increase social welfare, system throughput, and even consumer surplus in the long run, reversing its short-run detrimental effect on customers. Despite these potential benefits, such capacity expansion does little to mitigate scalping and may only generate more scalpers in the queue. Finally, we compare and contrast queue scalping with other common mechanisms in practice—namely, (centralized) pay-for-priority, line sitting, and callbacks. This paper was accepted by Victor Martínez de Albéniz, operations management.

Electricity end-users have been increasingly generating their own electricity via rooftop solar panels. Our paper studies the implications of such “distributed renewable energy” for utility profits and social welfare under net metering that has sparked heated debates in practice. The common belief is that such type of generation significantly decreases utility profits because (i) distributed generation reduces utility’s market size, and (ii) under net metering, utilities must buy back the excess generation of their customers at a rate typically larger than their procurement cost. Based on this understanding, there have been various lobbying campaigns against the net-metered distributed solar energy. A distinctive feature of our paper is that it considers supply-side effects of distributed renewable energy on utilities by explicitly modeling the wholesale market dynamics, while also accounting for (i) and (ii). Our analysis shows that in contrast to the common belief, the presence of net-metered distributed renewable energy can strictly increase the expected profit for utilities (compared to the case with no such energy) when wholesale market dynamics are factored in. Specifically, we prove that the net-metered distributed renewable energy strictly improves the utility’s expected profit if and only if the equilibrium wholesale electricity price is sufficiently sensitive to the changes in the wholesale demand and the utility’s expected market-reliance level is above a threshold. Our numerical study uses data on distributed solar energy in California and the CAISO’s wholesale electricity market, and demonstrates that our results continue to be valid under realistic parameters.

This paper studies whether euphemisms obfuscate the content of earnings conference calls and cause investors to underreact. I argue that managers’ use of euphemisms can alleviate the impact of bad news and delay the market reaction to adverse information. Using a dictionary of corporate euphemisms, I find that their use by managers—but not by analysts—is negatively associated with both immediate and future abnormal returns, and their frequency moderates the negative market reaction to bad earnings news. Finally, stock underreaction is more pronounced on busy earnings announcement dates, when investor attention is distracted. This paper was accepted by Brian Bushee, accounting.

This paper explores how governments may efficiently inform the public about an epidemic to induce compliance with their confinement measures. Using an information design framework, we find the government has an incentive to either downplay or exaggerate the severity of the epidemic if it heavily prioritizes the economy over population health or vice versa. Importantly, we find that the level of economic inequality in the population has an effect on these distortions. The more unequal the disease’s economic impact on the population, the less the government exaggerates and the more it downplays the severity of the epidemic. When the government weighs the economy and population health sufficiently equally, however, the government should always be fully transparent about the severity of the epidemic. This paper was accepted by Stefan Scholtes, healthcare management.

This paper proposes an original approach for backtesting systemic risk measures. This backtesting approach makes it possible to assess the systemic risk measure forecasts used to identify the financial institutions that contribute the most to the overall risk in the financial system. Our procedure is based on simple tests similar to those generally used to backtest the standard market risk measures such as value-at-risk or expected shortfall. We introduce a concept of violation associated with the marginal expected shortfall (MES), and we define unconditional coverage and independence tests for these violations. We can generalize these tests to any MES-based systemic risk measures such as the systemic expected shortfall (SES), the systemic risk measure (SRISK), or the delta conditional value-at-risk ([Formula: see text]CoVaR). We study their asymptotic properties in the presence of estimation risk and investigate their finite sample performance via Monte Carlo simulations. An empirical application to a panel of U.S. financial institutions is conducted to assess the validity of MES, SRISK, and [Formula: see text]CoVaR forecasts issued from a bivariate GARCH model with a dynamic conditional correlation structure. Our results show that this model provides valid forecasts for MES and SRISK when considering a medium-term horizon. Finally, we propose an early warning system indicator for future systemic crises deduced from these backtests. Our indicator quantifies how much is the measurement error issued by a systemic risk forecast at a given point in time which can serve for the early detection of global market reversals. This paper was accepted by Kay Giesecke, finance.

Theories of organizations are sympathetic to long-standing ideas from network science that organizational networks should be regarded as multiscale and capable of displaying emergent properties. However, the historical difficulty of collecting individual-level network data for many (N ≫ 1) organizations, each of which comprises many (n ≫ 1) individuals, has hobbled efforts to develop specific, theoretically motivated hypotheses connecting micro- (i.e., individual-level) network structure with macro-organizational properties. In this paper we seek to stimulate such efforts with an exploratory analysis of a unique data set of aggregated, anonymized email data from an enterprise email system that includes 1.8 billion messages sent by 1.4 million users from 65 publicly traded U.S. firms spanning a wide range of sizes and 7 industrial sectors. We uncover wide heterogeneity among firms with respect to all measured network characteristics, and we find robust network and organizational variation as a result of size. Interestingly, we find no clear associations between organizational network structure and firm age, industry, or performance; however, we do find that centralization increases with geographical dispersion—a result that is not explained by network size. Although preliminary, these results raise new questions for organizational theory as well as new issues for collecting, processing, and interpreting digital network data. This paper was accepted by David Simchi-Levi, Special Section of Management Science: 65th Anniversary.

I argue that academic research in accounting has strayed from producing work that is useful to either practitioners or policy makers. I use three criteria to arrive at that assessment: (i) How many products and processes have accounting research produced in the last 50 years? (ii) How much overlap do we observe between issues that Chief Financial Officers (CFOs) and Chief Executive Officers (CEOs) worry about and our published research? (iii) Is the science or the knowhow in academe in a particular area ahead of that in practice? I conjecture that tuition-funded research drives this problem. I review several initiatives that have been tried at Columbia and elsewhere (i) to better integrate academic research and practice and (ii) to disseminate our findings to practitioners. I suggest that Management Science set up a forum to encourage submissions of papers that use rigorous methods to address pressing applied problems. This paper was accepted by David Simchi-Levi, Special Section of Management Science: 65th Anniversary.

Revenue management has evolved over the years from its origins in the airline industry into a much broader discipline that analyzes algorithmic methods for demand and marketplace management, but many outside of the discipline are not aware of this transformation. The field’s transition tracks the widespread adoption of algorithmic decision-making techniques by businesses in a wide variety of industries over the last decade. We study this evolution in the field’s breadth of research, with a particular focus on revenue-management papers that study online marketplaces such as e-commerce retailing, digital advertisement, and ride-hailing markets for urban transportation. This paper was accepted by David Simchi-Levi, Special Section of Management Science: 65th Anniversary.

Understanding the process of innovation has been a central concern of management researchers, but despite this progress, there remains much that we do not understand. Deepening our knowledge is critically important given the enormous environmental and social challenges we face as a society. Pursing incremental innovation will continue to be hugely important, but this paper argues that building a richer understanding of architectural or systemic innovation will also be crucial. This paper suggests that the study of organizational purpose may provide a particularly fruitful avenue for future research. This paper was accepted by David Simchi-Levi, Special Section of Management Science: 65th Anniversary.