8월 6주차

Generative artificial intelligence (AI) facilitates content production and enhances ideation, with potentially important implications for developer productivity and participation in software development. To explore its impact on collaborative open-source software (OSS) development, we investigate the role of GitHub Copilot, a generative AI pair programmer, in OSS development where multiple distributed developers voluntarily collaborate. Using GitHub's proprietary Copilot usage data, combined with public OSS project data obtained from GitHub, we find that Copilot use increases project-level code contributions by 5.9%. This gain is accompanied by a 3.4% increase in developer coding participation and a 2.1% increase in individual code contributions. However, Copilot use is also associated with an 8% increase in coordination time and more code discussions. This reveals an important tradeoff: While AI expands who can contribute and how much they contribute, it slows coordination in collective development efforts. Despite this tension, the overall effect remains positive, resulting in a net increase in the timely merge of code contributions at the project level. Interestingly, we also find heterogeneous effects across developer roles. Peripheral developers exhibit relatively smaller increases in project-level code contributions and larger increases in coordination time than core developers. Together, our findings highlight the dual effects of AI pair programmers on code contributions and coordination in OSS development and provide implications for how generative AI may reshape the structure of OSS communities over time.

As data privacy regulations expand consumers’ control over personal data, e-commerce platforms and sellers increasingly face users whose data cannot be used for targeting or pricing. This paper studies how such information withholding reshapes competition in digital marketplaces. We develop a two-stage model of platform segmentation and pricing competition and introduce “fuzzy segmentation,” whereby the platform strategically pools privacy-preserving users with selectively grouped data-sharing consumers. Our analysis yields three main insights. First, fuzzy segmentation softens competition and increases seller profits, contrasting with the canonical intuition that horizontal segmentation tends to intensify price competition. Second, contrary to the intuition that privacy protects consumers from price discrimination, privacy-preserving consumers may face higher prices. Third, some data-sharing consumers may experience negative spillovers due to their inclusion in the mixed segment with privacy-preserving users. These results reveal unintended consequences of data privacy regulation: although privacy rules may enhance consumers’ control over personal information, they can also alter market segmentation in ways that raise prices for both privacy-preserving users and some data-sharing consumers. From a strategic perspective, the results show how privacy-driven incomplete information can be leveraged as a profit-enhancing force in e-commerce platforms.

A decentralized autonomous organization (DAO) is a novel form of blockchain-based organization designed for collective decision-making. As DAOs emphasize a decentralized, democratic decision-making approach, participation serves as the foundation for their sustainable operation and development. Unfortunately, many DAOs struggle with low participation rates, often falling short of the required quorum. To address this critical issue, an increasing number of DAOs have adopted delegated voting, which allows members to transfer their voting rights to others. However, the impact of delegated voting within the DAO context remains unknown. By leveraging variation in the adoption of delegated voting across DAOs, we find that delegated voting increases members’ participation in proposal voting and enhances decision quality. Our results further show that delegated voting stimulates greater participation in proposals with higher participation costs, including those that are more complex, urgent, or operational in nature. However, in the long term, delegated voting also leads to greater voting power concentration and reduces engagement from both new and active voters, potentially harming sustained participation and the growth of the DAO community. Overall, our findings highlight the need for DAOs to balance the short-term gains from higher participation with the potential long-term risks to decentralization.

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.