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Quantifying Managerial Ability: A New Measure and Validity Tests
We propose a measure of managerial ability, based on managers' efficiency in generating revenues, which is available for a large sample of firms and outperforms existing ability measures. We find that our measure is strongly associated with manager fixed effects and that the stock price reactions to chief executive officer (CEO) turnovers are positive (negative) when we assess the outgoing CEO as low (high) ability. We also find that replacing CEOs with more (less) able CEOs is associated with improvements (declines) in subsequent firm performance. We conclude with a demonstration of the potential of the measure. We find that the negative relation between equity financing and future abnormal returns documented in prior research is mitigated by managerial ability. Specifically, more able managers appear to utilize equity issuance proceeds more effectively, illustrating that our more precise measure of managerial ability will allow researchers to pursue studies that were previously difficult to conduct.
This paper was accepted by Mary E. Barth, accounting.
Journal Article
Managing science : developing your research, leadership and management skills
'This book aims to introduce the working research scientists to the art and techniques of management and the skills necessary to be a good and effective manager and leader of science and scientists. This includes understanding the organization and functioning of scientific research establishments (universities, laboratories, research councils, etc.) and how to deal with the associated committee work, recruiting and team building; how to deal with difficulties managing projects and handling risks.\"--back cover.
Book
The handbook of behavioral operations
A comprehensive review of behavioral operations management that puts the focus on new and trending research in the field
The Handbook of Behavioral Operations offers a comprehensive resource that fills the gap in the behavioral operations management literature. This vital text highlights best practices in behavioral operations research and identifies the most current research directions and their applications. A volume in the Wiley Series in Operations Research and Management Science, this book contains contributions from an international panel of scholars from a wide variety of backgrounds who are conducting behavioral research.
The handbook provides succinct tutorials on common methods used to conduct behavioral research, serves as a resource for current topics in behavioral operations research, and as a guide to the use of new research methods. The authors review the fundamental theories and offer frameworks from a psychological, systems dynamics, and behavioral economic standpoint. They provide a crucial grounding for behavioral operations as well as an entry point for new areas of behavioral research. The handbook also presents a variety of behavioral operations applications that focus on specific areas of study and includes a survey of current and future research needs. This important resource:
* Contains a summary of the methodological foundationsand in-depth treatment of research best practices in behavioral research.
* Provides a comprehensive review ofthe research conducted over the past two decades in behavioral operations, including such classic topics as inventory management, supply chain contracting, forecasting, andcompetitive sourcing.
* Covers a wide-range of currenttopics andapplications including supply chain risk, responsible and sustainable supplychain, health care operations, culture and trust.
* Connects existing bodies of behavioral operations literature with related fields, including psychology and economics.
* Providesa vision for futurebehavioral research in operations.
Written for academicians within the operations management community as well as for behavioral researchers, The Handbook of Behavioral Operations offers a comprehensive resource for the study of how individuals make decisions in an operational context with contributions from experts in the field.
eBook
From Predictive to Prescriptive Analytics
We combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisions in OR/MS problems. In a departure from other work on data-driven optimization, we consider data consisting, not only of observations of quantities with direct effect on costs/revenues, such as demand or returns, but also predominantly of observations of associated auxiliary quantities. The main problem of interest is a conditional stochastic optimization problem, given imperfect observations, where the joint probability distributions that specify the problem are unknown. We demonstrate how our proposed methods are generally applicable to a wide range of decision problems and prove that they are computationally tractable and asymptotically optimal under mild conditions, even when data are not independent and identically distributed and for censored observations. We extend these to the case in which some decision variables, such as price, may affect uncertainty and their causal effects are unknown. We develop the coefficient of prescriptiveness
P
to measure the prescriptive content of data and the efficacy of a policy from an operations perspective. We demonstrate our approach in an inventory management problem faced by the distribution arm of a large media company, shipping 1 billion units yearly. We leverage both internal data and public data harvested from IMDb, Rotten Tomatoes, and Google to prescribe operational decisions that outperform baseline measures. Specifically, the data we collect, leveraged by our methods, account for an 88% improvement as measured by our coefficient of prescriptiveness.
This paper was accepted by Noah Gans, optimization.
Journal Article
Trade Credit, Risk Sharing, and Inventory Financing Portfolios
As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain coordination and inventory management. Using a model that explicitly captures the interaction of firms’ operations decisions, financial constraints, and multiple financing channels (bank loans and trade credit), this paper attempts to better understand the risk-sharing role of trade credit—that is, how trade credit enhances supply chain efficiency by allowing the retailer to partially share the demand risk with the supplier. Within this role, in equilibrium, trade credit is an indispensable external source for inventory financing, even when the supplier is at a disadvantageous position in managing default relative to a bank. Specifically, the equilibrium trade credit contract is net terms when the retailer’s financial status is relatively strong. Accordingly, trade credit is the only external source that the retailer uses to finance inventory. By contrast, if the retailer’s cash level is low, the supplier offers two-part terms, inducing the retailer to finance inventory with a portfolio of trade credit and bank loans. Further, a deeper early-payment discount is offered when the supplier is relatively less efficient in recovering defaulted trade credit, or the retailer has stronger market power. Trade credit allows the supplier to take advantage of the retailer’s financial weakness, yet it may also benefit both parties when the retailer’s cash is reasonably high. Finally, using a sample of firm-level data on retailers, we empirically observe the inventory financing pattern that is consistent with what our model predicts.
This paper was accepted by Vishal Gaur, operations management.
Journal Article
Rational Herding in Microloan Markets
Microloan markets allow individual borrowers to raise funding from multiple individual lenders. We use a unique panel data set that tracks the funding dynamics of borrower listings on Prosper.com, the largest microloan market in the United States. We find evidence of rational herding among lenders. Well-funded borrower listings tend to attract more funding after we control for unobserved listing heterogeneity and payoff externalities. Moreover, instead of passively mimicking their peers (irrational herding), lenders engage in active observational learning (rational herding); they infer the creditworthiness of borrowers by observing peer lending decisions and use publicly observable borrower characteristics to moderate their inferences. Counterintuitively, obvious defects (e.g., poor credit grades) amplify a listing's herding momentum, as lenders infer superior creditworthiness to justify the herd. Similarly, favorable borrower characteristics (e.g., friend endorsements) weaken the herding effect, as lenders attribute herding to these observable merits. Follow-up analysis shows that rational herding beats irrational herding in predicting loan performance.
This paper was accepted by Pradeep Chintagunta, marketing.
Journal Article