Anderson School of Management

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.

People frequently encounter dynamic systems that involve inflows, outflows, and accumulated stocks-whether within their own households (e.g., financial accounts, stocks of food or supplies) or in larger institutional settings (e.g., manufacturing inventory, government benefit accounts). In this research, we introduce a novel stock-flow reasoning error, inflow neglect, and argue that this error can lead to important misperceptions regarding future outflows. To study this reasoning, we first focus on the United States' Social Security trust funds, whose impending depletion generates significant attention due to implications for American retirees. In Experiments 1-3, we show participants information about the trust funds over time that focus on the stock (i.e., balance) or flows (i.e., tax revenue and benefits payments), finding that those who see flows presentations are significantly less likely to expect benefits to cease completely after depletion (i.e., hold zero-outflow beliefs). In Experiments 4a and 4b, we show that prompting participants to reflect on the continuity of inflows (i.e., by reminding them that they expect payroll taxes to continue) significantly reduces inflow neglect and zero-outflow beliefs. Experiment 5 replicates these results in a separate domain, illustrating the generalizability of inflow neglect and underscoring the efficacy of presentations and targeted questions that emphasize the flows. This research contributes both theoretically and practically, advancing the literature on stock-flow reasoning and highlighting how communications about particular components of dynamic systems may contribute to-or be used to remedy-misconceptions that outflows will cease after depletion. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

This appendix provides a diagnostic framework for identifying and assessing the two forms of uncertainty that undermine sustainability metric credibility: effect uncertainty (whether an initiative will produce its intended outcome) and measurement uncertainty (whether outcomes can be accurately quantified). For each type, the framework distinguishes contextual sources—such as system complexity, data quality gaps, and lack of standardization—from behavioral sources, including cognitive biases, organizational resistance, and strategic disclosure bias. A two-stage diagnostic process guides practitioners from initial screening to deeper assessment, supported by a summary taxonomy table of key manifestations, sources, and warning signs.

Problem definition: How should (apparel) retailers manage product sizes? For example, if most customers wearing a given shoe size, such as 9.5, are willing to accept a half-size up or down, is it necessary for a retailer to carry that size at all? Additionally, although identical products in different sizes are treated as distinct stock-keeping units in inventory management, they are often aggregated for assortment and strategic planning. However, there is no theoretical justification for this approach. In this paper, we address the fundamental questions about size management that have remained largely unexplored in the operations literature. Methodology/results: We propose a choice model where each customer forms a consideration set based on the in-stock availability of products of her best-fit size and adjacent sizes. Using a real-world data set from a large footwear retailer, we show that nearly 25% of the unmet demand caused by stockouts spills over to adjacent sizes. We further solve the assortment and inventory optimization problems under the proposed choice model. Our findings demonstrate that the optimal assortment remains unchanged, regardless of the likelihood that customers might purchase adjacent sizes. We utilize this finding and further show that inventory policies that ignore size substitution can be (asymptotically) optimal when the demand rate is high or the selling horizon is long. We also propose a mixed-integer program to determine inventory levels that account for size substitution and achieve higher profits in low-demand settings. Managerial implications: We show that the prevalent size-aggregation approach employed in apparel retail operations is sensible in high-demand settings, such as e-commerce. In contrast, when the expected demand over the selling horizon is low, size substitution can be relevant and should be considered in stocking decisions. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0674 .

Climate impacts like heatwaves, droughts, and extreme storms increasingly force organizations to adapt. Many adaptation strategies are unsustainable: actions that protect firms today can deplete ecological resources, create new vulnerabilities, or shift risks onto other communities and future generations. We argue that adaptation becomes sustainable only when understood not as reactive adjustment but as organizational and institutional transformation within planetary limits. We identify a theoretical blind spot in management research, which has focused mainly on mitigation while overlooking the unique challenges adaptation poses to organizations. Drawing on examples from the wine industry, we illustrate how maladaptive practices emerge and why adaptation raises distinctive organizational challenges. We outline a research agenda to advance sustainable adaptation by avoiding maladaptation, balancing short- and long-term horizons, and navigating firm- and collective-level decision-making. This agenda centers on adaptation’s unique ecological embeddedness, deep uncertainty, and how its risks and responsibilities are temporally and spatially distributed.

Wasteful behavior poses major environmental, economic, and social challenges, yet the behavioral science literature on waste reduction remains fragmented. This systematic review synthesizes 99 experimental and quasi-experimental studies published between 2017 and 2021 that test behavioral interventions to reduce waste. This period captures a critical phase when global waste management systems faced unprecedented disruptions, including the 2017 launch of China's National Sword policy, which dramatically reshaped global recycling markets and exposed critical weaknesses in international waste systems. We adopt a broad definition of waste-including both discarded materials (e.g., food, trash, recyclables) and inefficient resource use (e.g., electricity, water, fuel)to better capture the full range of behaviors where interventions can reduce environmental impact and allow cross-domain comparisons. Our goal is to examine the behavioral interventions used, how interventions are structured, how behavior is measured, and whether they target individuals, households, communities, or broader systems. We identify six common types of behavioral interventions: education/informational feedback, social norms, economic incentives, cognitive biases and choice architecture, goal setting, and emotional appeals. Interventions targeting electricity and water use were most common, while food and solid waste remain under studied, largely due to measurement challenges. Although most studies used real-world field designs with direct behavioral outcomes, they focused heavily on individual and household behavior. This individual focus risks overlooking the structural and systemic changes needed to achieve broader, sustained reductions in waste. To advance the field, we call for greater use of community-level and system-wide interventions, investment in scalable measurement tools, and stronger collaboration between researchers, governments, and practitioners. Building on this foundation can help create more effective, scalable strategies to reduce waste across behavioral contexts.

Family-based genome-wide association studies (FGWASs) use random, within-family genetic variation to remove confounding from estimates of direct genetic effects (DGEs). Here we introduce a ‘unified estimator’ that includes individuals without genotyped relatives, unifying standard and FGWAS while increasing power for DGE estimation. We also introduce a ‘robust estimator’ that is not biased in structured and/or admixed populations. In an analysis of 19 phenotypes in the UK Biobank, the unified estimator in the White British subsample and the robust estimator (applied without ancestry restrictions) increased the effective sample size for DGEs by 46.9% to 106.5% and 10.3% to 21.0%, respectively, compared to using genetic differences between siblings. Polygenic predictors derived from the unified estimator demonstrated superior out-of-sample prediction ability compared to other family-based methods. We implemented the methods in the software package snipar in an efficient linear mixed model that accounts for sample relatedness and sibling shared environment.

While digital tools, such as the Internet, smartphones, and social media, are an important part of modern society, little is known about the specific role they play in the healthcare management of individuals and caregivers affected by rare disease. Collectively, rare diseases directly affect up to 10% of the global population, suggesting that a significant number of individuals might benefit from the use of digital tools. The purpose of this qualitative interview-based study was to explore: (a) the ways in which digital tools help the rare disease community; (b) the healthcare gaps not addressed by current digital tools; and (c) recommended digital tool features. Individuals and caregivers affected by rare disease who were comfortable using a smartphone and at least 18 years old were eligible to participate. We recruited from rare disease organizations using purposive sampling in order to achieve a diverse and information rich sample. Interviews took place over Zoom and reflexive thematic analysis was utilized to conceptualize themes. Eight semistructured interviews took place with four individuals and four caregivers. Three themes were conceptualized which elucidated key aspects of how digital tools were utilized in disease management: (1) digital tools should lessen the burden of managing a rare disease condition; (2) digital tools should foster community building and promote trust; and (3) digital tools should provide trusted and personalized information to understand the condition and what the future may hold. These results suggest that digital tools play a central role in the lives of individuals with rare disease and their caregivers. Digital tools that centralize trustworthy information, and that bring the relevant community together to interact and promote trust are needed. Genetic counselors can consider these ideal attributes of digital tools when providing resources to individuals and caretakers of rare disease.