In many online platforms like ride-sharing or grocery delivery, some demand agents can wait for a short period of time for a service if they are assured of its eventual provision within an acceptable delay. We study these “partially patient” agents in the context of an online resource allocation problem, contrasting them with impatient agents who require immediate service. We utilize a relaxed notion of competitive ratio, known as the resource/feature augmented competitive ratio, allowing online algorithms to benefit from additional features not available to the optimal offline algorithm. We introduce a class of polytope-based resource allocation (POLYRA) algorithms that achieve optimal or near-optimal competitive ratios by consulting specific polytopes and maintaining feasible algorithm states within these polytopes. For two or three types of agents, these algorithms can achieve the optimal competitive ratio. For more than three types, we present a near-optimal nested POLYRA algorithm that secures at least 80% of the optimal ratio. Our theoretical findings are supported by numerical analysis, highlighting the efficiency of our algorithms beyond adversarial arrivals and the modified competitive ratio framework. This paper was accepted by Chung Piaw Teo, optimization and decision analytics. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.03335 .

Despite the great potential benefits of battery energy storage systems (BESSs) to electrical grids, most standalone uses of BESS are not economical due to batteries’ high upfront costs and limited lifespans. Energy stacking, a strategy of providing two or more services with a single BESS, has been of great interest to improve profitability. However, some key questions, for example, the underlying mechanism by which stacking works or why and how much it may improve profitability, remain unanswered in the literature. Using two popular battery services, we analytically show that there often exists cost-saving synergy—the cost of performing both services at the same time (simultaneous stacking) is smaller than the sum of individual costs if we had performed each service alone—which allows for bigger profits. Furthermore, we perform comparative statics on the optimal mix of the services to systemically characterize grid/market conditions that maximize/minimize this synergy. We also derive a theoretical upper bound on simultaneous stacking’s benefits, showing that it can approximately double the profit of the best standalone service. Several generalizations of the base model not only show that the main lessons continue to hold but also that stacking’s benefits may become even stronger. This paper was accepted by Jayashankar Swaminathan, operations management. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.03477 .

We examine effects on productivity, employment, and debt outcomes among college students whose parents reside in areas that experience financial shocks caused by natural disasters. After shocks, treated students exhibit poorer academic performance. To boost the impaired grade point average, these students sacrifice educational content by withdrawing from more courses and enrolling in fewer STEM (Science, Technology, Engineering, Mathematics) courses. Effects are stronger for middle class students likely relying on family income to pay for college. Students mostly mitigate financial shocks with additional part-time employment. Ultimately, disrupted students are 10% more likely to default on student loans. Overall, these results shed light on the effects of financial stress on the intensive margin of human capital formation. This paper was accepted by Camelia Kuhnen, finance. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.02977 .

Women remain underrepresented not only as startup founders but also as nonfounding employees (“joiners”). Yet there is limited understanding of how to address this disparity. Whereas traditional explanations have emphasized individual employment preferences, we shift the focus to employers’ decision making. We propose that weaker employment protection—which reduces termination costs—increases women’s likelihood relative to men of being hired by startups rather than incumbent firms, as it enhances employers’ flexibility to experiment with traditionally overlooked candidates. We further hypothesize that this effect is less pronounced when information cues counteract stereotypical beliefs about women hires, such as when (a) women have experience as founders, or (b) the focal startup operates in an industry with a higher representation of female employees or female startup employees. Using Portuguese registry data on new hires from 2009 to 2013 and a regression discontinuity in time (RDiT) design, we provide empirical support for these predictions. Our study highlights gender disparities in hiring between startups and incumbent firms and shows that weaker employment protection can help reduce this gender gap. This paper was accepted by Toby Stuart, entrepreneurship and innovation. Funding: R. Conti gratefully acknowledges financial support from the CY Initiative. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01491 .

We examine how investors’ investment judgments are jointly affected by two important linguistic features that vary considerably across firms’ risk factor disclosures: namely, causal focus—the extent to which managers’ disclosures focus on the causes versus consequences of risk events—and specificity, which refers to specific references to names of objects and quantitative values. Results of controlled experiments show that, when the disclosure focus is on risk causes, greater specificity increases investment willingness because of enhanced feelings of knowable uncertainty in a risk event. In contrast, when the focus is on risk consequences, greater specificity reduces investment willingness because of diminished feelings of knowable uncertainty. Contrary to the Securities and Exchange Commission’s positive view of specific risk factor disclosures, our results suggest that specificity can have an opposite effects on investors’ judgments depending on the causal focus of risk factor disclosures. This paper was accepted by Ranjani Krishnan, accounting. Funding: This work was supported by Korea University Business School and the United Overseas Bank Professorship Endowment Fund. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00445 .

We argue substitution can exist among corporate social responsibility (CSR) investments, and exogenously increasing one CSR investment could lead to a decrease in another CSR investment. We provide evidence using the U.S. states’ staggered adoptions of E-Verify mandates, which curtail a labor-related social bad by reducing the hiring of unauthorized workers and related workplace abuses. We find the mandate leads to an increase in plant-level pollution, an environmental social bad, and the effect is stronger when the mandate applies to more employers, for plants in states with more unauthorized workers in the labor force, and for plants with jobs that are inherently more hazardous. This paper was accepted by Suraj Srinivasan, accounting. Funding: This work was supported by the Hong Kong Research Grants Council [Project 24502422]. Supplemental Material: The internet appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02310 .

This study examines how winning a significant industry award affects the behavior of finance professionals. Focusing on sell-side equity analysts and utilizing a novel data set from Institutional Investor on analyst rankings, we employ a regression discontinuity design that compares the postaward research outputs and behavior of third-place, all-star analysts with those of first runner-up analysts who barely miss the distinction. Our results show that third-place all-star winners are more optimistic in their forecasts and recommendations compared with first runner-up analysts after winning the award, and market reactions to their forecast revisions are stronger. The third-place winners also receive higher priority during earnings conference calls and experience better career outcomes. Our evidence is consistent with award-winning analysts leveraging their increased reputation and market influence to generate more trading commissions and career benefits. The broader inference of our findings is that finance professionals who win a significant award are likely to become more, rather than less, strategic. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.04249 .

How do venture capital investors react to social movements, including those that relate to historical underrepresentation in funding? We use image and name algorithms combined with clerical review to classify race for 150,000 founders and 30,000 investors. These data allow us to assess the impact of George Floyd’s murder on VC funding of Black entrepreneurs and identify which VCs were most responsive. Although VCs responded swiftly, investment in Black-founded startups reverted to prior levels within two years. This temporary reaction was concentrated among those who had never previously invested in any Black entrepreneur. Moreover, the investors who responded were less likely to invest in more than one Black-founded startup and were less inclined to engage deeply by taking a board seat. Finally, it appears that the best Black entrepreneurs may have anticipated this “token” response because they did not match with investors who had no experience funding Black startups. This paper was accepted by Toby Stuart, entrepreneurship and innovation. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.06410 .

We examine how access to debt markets affects firms’ incentives to provide trade credit. Using hand-collected trade credit data between customer-supplier pairs and two exogenous shocks to firms’ debt capacity, we show that better access to debt reduces firms’ provision of trade credit per dollar of sales. The decline in trade credit is concentrated on ex ante powerful customers, but absent for weak ones, suggesting that better access to debt improves firms’ bargaining position relative to powerful customers. The decline in trade credit leads customers to cut investment, increase leverage, and scale back trade credit provision to firms further downstream. This paper was accepted by Camelia Kuhnen, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.04090 .

When organizations adopt artificial intelligence (AI) to recognize individuals’ negative emotions and accordingly allocate limited resources, strategic users are incentivized to game the system by misrepresenting their emotions. The value of AI in automating such emotion-driven allocation may be undermined by gaming behavior, algorithmic noise in emotion detection, and the spillover effect of negative emotions. We develop a game-theoretical model to understand emotion AI adoption, particularly in customer care, and analyze the design of the associated allocation policies. We find that adopting emotion AI is valuable if the spillover effect of negative emotions is negligible compared with resource misallocation loss, regardless of algorithmic noise and gaming behavior. We also quantify the welfare impacts of emotion AI on the users, organization, and society. Notably, a stronger AI is not always socially desirable and regulation on emotion-driven allocation is needed. Finally, we characterize conditions under which leveraging the AI system is preferred to hiring human employees in emotion-driven allocation. We also explore the alternative application of using emotion AI to monitor strategic employees and compare it with hiring a human manager for monitoring. Intriguingly, algorithmic noise may increase the profit of AI monitoring. Our work provides implications for designing, adopting, and regulating emotion AI. This paper was accepted by D. J. Wu, Special Issue on the Human-Algorithm Connection. Funding: The work of L. Jia was supported by the National Natural Science Foundation of China [Grant 72172013]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02860 .