We study capacity rationing by servers facing differentiated customer classes using data from the Veterans Health Administration, which is the largest integrated healthcare system in the U.S. Using more than 11 million health encounters over two years in which the system was capacity constrained, our study provides a comprehensive analysis of the impacts of provider availability shocks on care channel diversion and delays. The outcomes studied include emergency room (ER) visits broken down by type, urgent care center visits, office and phone visits with one’s own versus another provider, post-ER follow-up visits, and ER readmissions. Availability shocks in our analysis are a residualized measure characterizing weeks in which the provider has fewer (or more) office appointments than expected based on typical patterns. The main finding is that moving from two standard deviations above to two standard deviations below in availability shocks increases ER visits by 2.4%, or about 20,000 yearly ER visits. Interestingly, the increase in ER visits is only present for the non-emergent category, indicating differentiated service to emergent and non-emergent care requests; capacity-constrained providers still tend to the patients in most need. Another finding is that provider availability shocks delay and divert post-ER follow-up care. Yet there is no effect on ER readmissions, a severe outcome of delayed or foregone follow-up, indicating that providers ration by priority these follow-up appointments.

One key factor contributing to emergency department (ED) overcrowding is prolonged waiting time for admission to inpatient wards, also known as ED boarding time. To gain insights into reducing this waiting time, we study operations in the inpatient wards and their interface with the ED. We focus on understanding the effect of inpatient discharge policies and other operational policies on the time-of-day waiting time performance, such as the fraction of patients waiting longer than six hours in the ED before being admitted. Based on an empirical study at a Singaporean hospital, we propose a novel stochastic processing network with the following characteristics to model inpatient operations: (1) A patient’s service time in the inpatient wards depends on that patient’s admission and discharge times and length of stay. The service times capture a two-time-scale phenomenon and are not independent and identically distributed. (2) Pre- and post-allocation delays model the extra amount of waiting caused by secondary bottlenecks other than bed unavailability, such as nurse shortage. (3) Patients waiting for a bed can overflow to a nonprimary ward when the waiting time reaches a threshold, where the threshold is time dependent. We show, via simulation studies, that our model is able to capture the inpatient flow dynamics at hourly resolution and can evaluate the impact of operational policies on both the daily and time-of-day waiting time performance. In particular, our model predicts that implementing a hypothetical policy can eliminate excessive waiting for those patients who request beds in mornings. This policy incorporates the following components: a discharge distribution with the first discharge peak between 8 a.m. and 9 a.m. and 26% of patients discharging before noon, and constant-mean allocation delays throughout the day. The insights gained from our model can help hospital managers to choose among different policies to implement depending on the choice of objective, such as to reduce the peak waiting in the morning or to reduce daily waiting time statistics.

Strong majority influence can potentially harm organizational decisions by causing decision makers to engage in groupthink. This study examines whether and how computer-mediated communication (CMC) can reduce majority influence and thereby enhance the quality of decisions in some situations. To measure the impact of CMC on majority influence, three settings (unsupported, face-to-face CMC, and dispersed CMC) were compared. Matching laboratory experiments were carried out in an individualistic (the US) and a collectivistic culture (Singapore) to determine how the impact of CMC might be moderated by national culture. An intellective and a preference task were used to see whether the impact of CMC might be moderated by task type. The results showed that the impact of CMC on majority influence was contingent upon national culture. In the individualistic culture, majority influence was stronger in the unsupported setting than the face-to-face CMC and dispersed CMC settings. In the collectivistic culture, there were no corresponding differences. The results also revealed that the impact of CMC on majority influence was not moderated by task type. Instead, task type had a direct impact on majority influence. Regardless of the setting involved, majority influence was stronger with the preference than the intellective task. Besides demonstrating how cultural factors may moderate the impact of CMC, this study raises the broader issue of cultural relativism in current knowledge on CMC.

Alternative ways to alleviate the shortage of anesthesiologists in obstetric services have been investigated in three study stages. These were (1) a survey and detailed analysis of the existing situation in one metropolitan region of the United States, (2) the construction and validation of a computer simulation model to study the consequences of alternative practice modes with speed, with economy and requiring no human experimentation, and (3) the establishment of a methodology for selecting an optimal anesthesia team configuration. This in turn should aid administrators in establishing future manpower needs and enable more efficient allocations of existing staff. The methodology, incorporating the simulation model, provides team design charts which enable decision makers to arrive at a basis for enlightened tradeoffs between personnel-associated costs and quality of service. Service quality is measured in terms of temporary unavailability of anesthesia personnel with the proper skills for performing certain tasks when needed. This article documents the overall project mission, the data base, the methodologies used, and the study results.

We find that equity loan fees, which have been largely ignored by the anomalies literature, are the best predictor of cross-sectional returns. When compared with 102 other anomalies and other short-selling measures, the loan fee anomaly has the highest monthly long-short return (4.01%), the highest monthly Sharpe Ratio (0.66), and, unlike other anomalies, exhibits strong persistence throughout the sample. Although prior work has shown that existing anomalies reside in high loan fee stocks, we find that 42% of loan fee outperformance is due to unique information not contained in other anomalies. Future papers that examine cross-sectional predictors of returns should include the single most effective predictor: loan fees.

The applied research resource allocation decision process in a complex, hierarchical federal organization is explored in this paper. This decision process includes the identification of research objectives and the funding of projects selected to achieve the objectives. The hierarchical, geographical, and temporal diffuseness of participation in the decision process is described. Several a priori conjectures are presented concerning how the decision to fund research projects might be made in such a hierarchically and spatially diffused organization. The conjectures are empirically tested using data for 181 projects from the 1970 budget cycle, and their implications are discussed.

We examine the impact of family-based human capital stemming from a chairperson’s having siblings vis-à-vis not having siblings on corporate innovation in Chinese family firms. Using hand-collected data, we document that when a firm has a sibling-chairperson, it holds more patents, receives more total citations to its patents, and has greater innovation efficiency and innovation quality than an otherwise equivalent firm with a chairperson having no siblings. The results are economically significant and robust to a battery of robustness checks. Specifically, the findings remain intact after using China’s one-child policy as an exogenous shock to apply a regression discontinuity research design to mitigate endogeneity. Additional analyses suggest that the mechanisms behind the impact of siblings on innovation are consistent with family-based human capital embedded in the sibling relationships such as competition, knowledge spillover, and family firm succession effect among siblings. Furthermore, we show that sibling comanagement and sibling gender diversity matter in corporate innovation. In addition, sibling effect enhances corporate investment efficiency, stock returns, and merger and acquisition performance. Overall, family-based human capital from siblings positively contributes to corporate innovation.

Precise, compared with round, asking prices lead to counteroffers and final agreements that are closer to the asking price. Consequently, popular advice for sellers is to set precise asking prices. We propose that the advice is useful, but only in a buyer’s market, in which buyers counter below the asking price. In a seller’s market, in which buyers counter above the asking price, sellers who wish to receive high counteroffers and sell for high prices should set round asking prices. A preregistered study (n = 1,809) shows that, compared with round asking prices, precise prices lead to higher counteroffers in a buyer’s market but to lower counteroffers in a seller’s market. The effect is driven by buyers’ use of a finer-grained pricing scale when countering precise asking prices. An analysis of transactions (n = 8,278) from Amsterdam’s 2017 real estate market, in which 70% of the properties were sold above the asking price, corroborates the experimental findings. Results show that increasing the roundness of the asking price by one decimal, for instance, from precise to the thousands to precise to the tens of thousands, was associated with an increase of 0.6% in the selling price, equivalent to €2,099 on average.

In addition to setting price discounts, retailers need to decide how to schedule promotion vehicles, such as flyers and TV commercials. Unlike the promotion pricing problem that received great attention from both academics and practitioners, the promotion vehicle scheduling problem was largely overlooked, and our goal is to study this problem both theoretically and in practice. We model the problem of scheduling promotion vehicles to maximize profits as a nonlinear bipartite matching-type problem, where promotion vehicles should be assigned to time periods, subject to capacity constraints. Our modeling approach is motivated and calibrated using actual data in collaboration with Oracle Retail, leading us to introduce and study a class of models for which the boost effects of promotion vehicles on demand are multiplicative. From a technical perspective, we prove that the general setting considered is computationally intractable. Nevertheless, we develop approximation algorithms and propose a compact integer programming formulation. In particular, we show how to obtain a (1 − ε)-approximation using an integer program of polynomial size, and investigate the performance of a greedy procedure, both analytically and computationally. We also discuss an extension that includes cross-term effects to capture the cannibalization aspect of using several vehicles simultaneously. From a practical perspective, we test our methods on actual data through a case study, and quantify the impact of our models. Under our model assumptions and for a particular item considered in our case study, we show that a rigorous optimization approach to the promotion vehicle scheduling problem allows the retailer to increase its profit by 2% to 9%. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2926 .

This paper is an update of a paper that five of us published in 1992. The areas of multiple criteria decision making (MCDM) and multiattribute utility theory (MAUT) continue to be active areas of management science research and application. This paper extends the history of these areas and discusses topics we believe to be important for the future of these fields.