Despite a recent surge in corporate activism, with firm leaders communicating about social-political issues unrelated to their core businesses, we know little about its strategic implications. This paper examines the effect of an employer communicating a stance about a social-political issue on employee motivation, using a two-phase, preregistered field experiment in an online labor market platform. Results demonstrate an asymmetric treatment effect of taking a stance depending on whether the employee agrees or disagrees with that stance. Namely, I observe a demotivating effect of taking a stance on a social-political issue with which employees disagree and no statistically significant motivating effect of taking a stance on a social-political issue with which employees agree. This study has important implications for the nascent scholarship on corporate activism, as well as the scholarship on strategic human capital management. This paper was accepted by Greta Hsu, organizations.

In this paper, I provide some observations on how the academic field of operations management has changed over the past 40 years. For this purpose, I have identified and classified the operations management (OM) papers published in Management Science in 1976 and in 2016. From this review, I comment on what’s changed, what’s new, and what we might see in the future. In reflecting on these changes, I also document and discuss how the OM editorial structure and mission have evolved at Management Science over this time. This paper was accepted by David Simchi-Levi, Special Section of Management Science: 65th Anniversary.

Promotions are a critical decision for supermarket managers, who must decide the price promotions for a large number of items. Retailers often use promotions to boost the sales of the different items by leveraging the cross-item effects. We formulate the promotion optimization problem for multiple items as a nonlinear integer program. Our formulation includes several business rules as constraints. Our demand models can be estimated from data and capture the postpromotion dip effect and cross-item effects (substitution and complementarity). Because demand functions are typically nonlinear, the exact formulation is intractable. To address this issue, we propose a general class of integer programming approximations. For demand models with additive cross-item effects, we prove that it is sufficient to account for unilateral and pairwise contributions and derive parametric bounds on the performance of the approximation. We also show that the unconstrained problem can be solved efficiently via a linear program when items are substitutable and the price set has two values. For more general cases, we develop efficient rounding schemes to obtain an integer solution. We conclude by testing our method on realistic instances and convey the potential practical impact for retailers. This paper was accepted by Yinyu Ye, optimization.

This paper assembles a toolkit for the assessment of model risk when model uncertainty sets are defined in terms of an F-divergence ball around a reference model. We propose a new family of F-divergences that are easy to implement and flexible enough to imply convincing uncertainty sets for broad classes of reference models. We use our theoretical results to construct concrete examples of divergences that allow for significant amounts of uncertainty about lognormal or heavy-tailed Weibull reference models without implying that the worst case is necessarily infinitely bad. We implement our tools in an open-source software package and apply them to three risk management problems from operations management, insurance, and finance. This paper was accepted by Baris Ata, stochastic models and simulation.

Information systems generate copious trace data about what individuals do and when they do it. Trace data may affect the resolution of lawsuits by, for example, changing the time needed for legal discovery. Trace data might speed resolution by clarifying what events happened when, or they might slow resolution by generating volumes of new and potentially irrelevant data that must be analyzed. To investigate this, we analyze the effect of electronic medical records (EMRs) on malpractice claim resolution time. Use of EMRs within hospitals at the time of the alleged malpractice is associated with a four-month (12%) reduction in resolution time. Because unresolved malpractice claims impose substantial costs on the entire healthcare system, our finding that EMRs are associated with faster resolution has broad welfare implications. Furthermore, as we increasingly digitize society, the ramifications of trace data on legal outcomes matter beyond the medical context. This paper was accepted by Teck Ho, information systems.

How should a firm price a new product for which little is known about demand? We propose a simple and practical pricing rule for new products where demand information is limited. The rule is simple: Set price as though the demand curve were linear. Our pricing rule can be used if three conditions hold: the firm can estimate the maximum price it can charge and still expect to sell some units, the firm need not plan in advance the quantity it will sell, and marginal cost is known and constant. We show that if the true demand curve is one of many commonly used demand functions, or even a more complex (randomly generated) function, the firm can expect its profit to be close to what it would earn if it knew the true demand curve. We derive analytical performance bounds for a variety of demand functions, calculate expected profit performance for randomly generated demand curves, and evaluate the welfare implications of our pricing rule. We show that with limited demand information (maximum price and marginal cost), our simple pricing rule can be used for new products while often achieving a near-optimal performance. We also discuss the limitations of our method by identifying cases where our pricing rule does not perform well. This paper was accepted by Joshua Gans, business strategy.

We draw on a five-year longitudinal data set to investigate the influence of performance measurement in the processual dynamics of strategic change, particularly in enacting effective strategic change. Our model examines the role of performance measurement in driving strategy-consistent operational changes and in ensuring that the desired objectives of the strategic change process are achieved. We investigate these roles for performance measurement over time and empirically document lags between changes in strategic priorities, changes in operational processes, and subsequent changes in firm performance. We find that performance measurement supports the implementation of strategic change by influencing the extent to which changes to operational tasks and activities are made in response to new strategic priorities, as well as influencing the quality and impact of these operational changes, as reflected in improved contemporaneous and future firm performance. This paper was accepted by Suraj Srinivasan, accounting.

Disagreement about stock valuation, combined with short-sales constraints, can increase asset prices. We build a model showing that, so long as investor beliefs are not perfectly correlated, investors disagree less about the value of a conglomerate than about each of its individual divisions. This generates a conglomerate discount with disagreement and short-sales constraints being complementary in explaining its cross-sectional variation. We test these predictions empirically and find substantial support: conglomerates have lower differences of opinion and lower short-sales constraints than pure-play firms. Furthermore, greater differences of opinion and tighter short-sales constraints are significant predictors of valuation differences between conglomerates and pure plays. This paper was accepted by Karl Diether, finance.

Knowing how small a violation of stochastic dominance rules would be accepted by most individuals is a prerequisite to applying almost stochastic dominance criteria. Unlike previous laboratory-experimental studies, this paper estimates an acceptable violation of stochastic dominance rules with 939,690 real world data observations on a choice of deductibles in automobile theft insurance. We find that, for all policyholders in the sample who optimally chose a low deductible, the upper bound estimate of the acceptable violation ratio is 0.0014, which is close to zero. On the other hand, considering that most decision makers, such as 99% (95%) of the policyholders in the sample, optimally chose the low deductible, the upper bound estimate of the acceptable violation ratio is 0.0405 (0.0732). Our results provide reference values for the acceptable violation ratio for applying almost stochastic dominance rules. This paper was accepted by Manel Baucells, decision analysis.

Scholars generally assume that consumer ratings reflect consumer satisfaction, but ratings can be influenced by the design of the rating system. We examine two rating designs—single-dimensional rating systems, which elicit overall ratings only, and multidimensional (MD) rating systems, which elicit both dimensional and overall ratings—and how they impact overall ratings. Drawing on the accessibility–diagnosticity framework, we argue that dimensional ratings in MD systems influence overall ratings based on how the dimensions have been rated. We support this explanation with seven experiments. Our results suggest that across various experimental settings, rating objects, dimensions, and numbers of dimensions, overall ratings are systematically influenced by the design of the rating system. This paper was accepted by Chris Forman, information systems.