Charles J. Corbett

Third-party sustainability measures, such as ESG scores and rankings, are central to research linking corporate sustainability and financial performance. However, these measures lack transparency and vary significantly across providers, raising reliability concerns. This systematic review of 82 business journal articles (1995–2024) assesses how scholars engage with and critically assess these measures. We distinguish two sources of uncertainty that limit confidence in these measures: the quality of underlying data (accuracy, reliability, and timeliness) and how data are combined (fungibility assumptions and weighting schemes). Our analysis reveals that discussions of measurement quality are rare, while methodological rigor is bimodal—researchers either scrutinize multiple dimensions or none at all. We observe systematic associations between attention to measurement elements, data-provider choices, and reported financial performance. We argue that choices about measure quality and aggregation are not neutral but directly shape empirical findings and their interpretation. We outline practical recommendations to advance rigor and transparency in sustainability-performance research.

How we manage operations—the domain of Operations Management (OM)—has important implications for the practice of diversity, equity, and inclusion (DEI) in organizations. Conversely, DEI goals have important implications for organizations’ OM practices. We outline the two-way links between DEI and OM to offer future research opportunities. In particular, we examine interactions between OM and DEI across four broad themes: (1) Workforce, (2) Supply Chains, (3) Health and Society, and (4) Technology, Platforms, and Innovation. We conclude with a discussion of DEI in OM as it relates to research and teaching. This article is a collaborative effort with the Senior Editors involved in the special issue of Production and Operations Management on “DEI in Operations and Supply Chain Management.”

Workers in knowledge-intensive industries often complain of having too many meetings, but organizations still give little thought to deciding when or how often to meet. We investigate the efficiency and robustness of various coordination scheduling rules. We consider workers who are engaged in a common activity (e.g., software programming) that can be divided into largely independent, parallel production tasks, but that necessitates periodic coordination. Coordination enables workers to address issues they have encountered in their independent work but takes time away from production. Using a stylized game-theoretic model, we show that small teams allow a more fluid, that is, worker-driven, approach to scheduling coordination, such as preemptive coordination (or production), under which any worker can impose coordination (or production). In larger teams this becomes inefficient. Several approaches can mitigate this effect. One option is to allocate the decision rights to produce or coordinate to the most productive worker. A more general version is to implement a voting-based scheme, where a minimum number of workers from a predetermined subset choose to coordinate. A third approach is to modify the preemptive coordination and production rules by adding time-based controls, to reserve some minimal amount of productive time or to enforce coordination after some point. Finally, a fixed-interval meeting schedule works well for very large teams. Our research helps formalize the tension between meeting (coordinating) and producing and indicates how to adapt team coordination scheduling rules to the degree of worker heterogeneity and team size. This paper was accepted by Jay Swaminathan, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03227 .

Problem definition: Two disciplines increasingly applied in operations management (OM) are machine learning (ML) and behavioral science (BSci). Rather than treating these as mutually exclusive fields, we discuss how they can work as complements to solve important OM problems. Methodology/results: We illustrate how ML and BSci enhance one another in non-OM domains before detailing how each step of their respective research processes can benefit the other in OM settings. We then conclude by proposing a framework to help identify how ML and BSci can jointly contribute to OM problems. Managerial implications: Overall, we aim to explore how the integration of ML and BSci can enable researchers to solve a wide range of problems within OM, allowing future research to generate valuable insights for managers, companies, and society.

Problem definition: The meaning of life must surely be more about well-being than wealth, but what does that have to do with operations? Well-being encompasses a lot: Are we happy as individuals? Are groups treated fairly? Is society sustainable? Operations management has many impacts on well-being at each of these levels, some more obvious than others. Methodology/results: This MSOM Fellow forum article offers a wide-ranging exploration of linkages between operations and well-being. It organizes “operations” into five broad areas: pace and productivity, predictability and probability, process and prevention, performance and payment, and pollution and protection. For each of those, it explores what makes individuals (un)happy, what is fair, and what is sustainable. Managerial implications: It concludes with 7 recurrent themes for research in Operations Management.

It is well known that manufacturing operations can affect the environment, but hardly any research explores whether the natural environment shapes manufacturing operations. Specifically, we investigate whether water scarcity, which results from environmental conditions, influences manufacturing firms to lower their toxic releases to the environment. We created a data set that spans 2000–2016 and includes details on the toxic emissions of 3,092 manufacturing facilities in Texas. Additionally, our data set includes measures of the water scarcity experienced by these facilities. Our econometric analysis shows that manufacturing facilities reduce their toxic releases into the environment when they have experienced drought conditions in the previous year. We examine facilities that release toxics to water as well as facilities with no toxic releases to water. We find that the reduction in total releases (to all media) is driven mainly by those facilities that release toxic chemicals to water. Further investigation at a more granular level indicates that water scarcity compels manufacturing facilities to lower their toxic releases into media other than water (i.e., land or air). The impact of water scarcity on toxic releases to water is more nuanced. A full-sample analysis fails to link water scarcity to lower toxic releases to water, but a further breakdown shows that manufacturing facilities in counties with a higher incidence of drought do lower their toxic releases to water. We also find that facilities that release toxics to water undertake more technical and input modifications to their manufacturing processes when they face water scarcity.This paper was accepted by David Simchi-Levi, operations management.