What is the impact of private equity (PE) ownership on healthcare value? Does PE ownership reduce healthcare value, and if so, can its effect be mitigated using health information technologies (IT)? Given the significant investments by PE firms in the healthcare sector in recent years, it is increasingly important for stakeholders, including policy makers, care providers, and patients, to understand their likely impact and whether PE ownership is aligned with their interests. Because PE firms seek to improve efficiency and generate short-term financial returns, the cost-cutting strategies used to achieve these goals may come at the expense of care quality, leading to competing hypotheses on the impact of PE ownership on healthcare value. We argue that IT-enabled health information sharing capabilities can help PE-owned hospitals to better manage this cost-quality tension and achieve higher healthcare value. Using a staggered difference-in-differences approach and data from US hospitals from 2008 to 2020, we estimate changes in healthcare value following PE acquisition and ownership. Overall, we find that PE ownership of hospitals reduces healthcare value. This decline is partially driven by reduction in clinical staffing following PE ownership. We further find that pre-existing, hospital health information sharing capabilities mitigate the negative impact of PE ownership on healthcare value. Specifically, such capabilities can strengthen the effect of PE ownership on cost reduction and, at the same time, mitigate PE-induced, care quality deterioration. Our results highlight the critical role of common data standards needed to promote IT-enabled information sharing between healthcare providers, which, in turn, can align the incentives of PE firms with the goals of value-based care.
This study examines how telehealth delivery models are associated with access to care and protocol adherence in underserved populations. While prior research reports mixed evidence on telehealth’s effectiveness, we argue that these inconsistencies arise because telehealth is not a single intervention but is implemented through distinct service configurations that address different barriers. Drawing on the theoretical framework of digital economics, we conceptualize telehealth as a technology-mediated service that can alter key economic frictions, including search and verification, transportation, tracking, and setup costs. We focus on two widely adopted models—specialist-facilitated telehealth and remote telehealth—and theorize that each addresses different subsets of these frictions, leading to distinct effects on care access and protocol adherence across settings. Using panel data from U.S. Community Health Centers on mental health services, we document three findings. First, adoption of the specialist-facilitated model is associated with increased access to mental health care, particularly in areas with greater clinical need. Second, remote telehealth is not associated with increased access on average but is associated with higher adherence to standardized depression screening and follow-up protocols. These effects are consistent with improvements in screening and documentation processes rather than increases in utilization. Third, the estimated effects of both models vary across local cost environments. The access effects of specialist-facilitated telehealth are larger in areas where search and verification frictions are greater. In contrast, the effects of remote telehealth are attenuated where patient-side setup costs are high but are larger where transportation barriers and documentation frictions are greater. These findings advance a model-specific perspective on telehealth’s effects, extend the digital economics framework to technology-mediated services, and provide evidence that the estimated effects of digital health interventions vary with how delivery models interact with underlying cost environments.
Cybersecurity threats are increasing the demand for managed security services, yet service providers face a fundamental challenge: how to design contracts that encourage client participation while reducing risks arising from security externalities. This study shows that offering a menu of security contracts can serve as a strategic tool for both market expansion and risk management. By providing both fixed and proportional compensation options simultaneously, MSSPs can leverage client self-selection to better manage heterogeneous risks. As security risks increase, high-valuation clients may migrate toward proportional compensation contracts, reducing aggregate client negligence and lowering MSSPs’ compensation burdens, which enables market expansion and higher profitability. Under certain market conditions, this flexible contract design creates a “win-win-win” outcome, benefiting providers, clients, and society. Moreover, our findings reveal that market incentives do not always align with social objectives. We identify the conditions under which targeted policy interventions—such as contractual guidelines, subsidies, or restrictions—may be needed to realign private incentives with social welfare. These insights highlight the importance of aligning cybersecurity service design and regulation with both economic incentives and collective security outcomes.
eHealth behavioral interventions have transformed how individuals manage their health and modify their lifestyles. Despite their growing popularity, many users gradually reduce or discontinue their participation over time. To understand this disengagement, we extend the Expectation-Confirmation Theory (ECT) by modeling user engagement as a dynamic learning process, where evolving perceptions of intervention effectiveness are shaped by ongoing experience. Leveraging a hierarchical Bayesian learning framework, we analyze users’ perception updates and how such dynamics influences engagement decisions. Our empirical results show that individuals’ learning performance appears to be lower for interventions with ambiguous instructions or those focused on short-term health outcomes. These interventions tend to generate noisier feedback that may hinder users’ ability to form accurate perceptions of intervention effectiveness, which may ultimately reduce sustained engagement. Given that eHealth behavioral interventions typically possess credence characteristics, where effectiveness may be difficult for users to evaluate directly, the need for clear, accessible information becomes especially critical. To help improve user learning and engagement, we propose several denoising strategies and evaluate them through counterfactual simulations. Our work extends ECT into healthcare settings and provides actionable insights for designing more supportive Health IT systems that foster informed decision-making and sustain user engagement.
Generative AI holds great promise for reshaping software development. We examine the impact of GitHub Copilot, a generative AI pair programmer, on collaborative open-source software (OSS) development, where developers voluntarily contribute and collaborate on projects. Using GitHub’s proprietary Copilot usage data combined with public OSS data from GitHub, we find that Copilot increases project-level code contributions by expanding developer participation and increasing code contributed by individual developers. However, these gains lead to increase in time to coordinate, review and integrate code. Overall, Copilot still increases the amount of code that is integrated in a timely manner, but the benefits are unevenly distributed. Core developers (highly active contributors) gain more from Copilot, whereas contributions from peripheral developers (occasional contributors) require relatively more coordination time. These findings suggest that as AI increasingly reduces the cost of producing code, coordination and integration may become more important constraints in software development. Organizations and OSS communities should therefore complement AI adoption with governance structures and coordination processes that facilitate code integration and support peripheral developers. More broadly, as AI increasingly automates routine coding activities, organizational processes and team structures may become critical determinants of the returns to generative AI adoption.
