arXiv subjects

Fangchen Song

Publications and source records attributed to Fangchen Song.

2 recordsLinked to original sources

arXiv2026

Remote Work: Driver or Deterrent of Digital Product Innovation

As firms adopt divergent policies regarding work-from-home (WFH), the implications of remote work for collaborative and interdependent outcomes such as digital product innovation remain uncertain. This study examines how remote work adoption affects continuous digital product innovation using a panel dataset of mobile applications. We identify firm-level remote work adoption from job postings data and estimate its effects on app innovation using a staggered difference-in-differences design. We find that remote work significantly increases both major releases and new feature introductions per app, indicating enhanced digital product innovation performance. To assess whether these gains come at the expense of originality, we distinguish between novel and imitative feature introductions and show that remote work does not reduce the originality of digital product innovation. Moreover, improvements in digital product innovation translate into greater market success, as reflected in increased app downloads. The positive effects of remote work are stronger for app development teams with prior modular collaboration experience through open-source participation, suggesting that teams with greater experience coordinating modular work can better leverage remote work arrangements. We also find that remote work enables teams to expand their workforce and increase their collective skill capacity, both of which are associated with improved digital product innovation outcomes. In contrast, reductions in commuting time and app maturity do not explain the observed digital product innovation gains. Overall, our findings suggest that remote work can enhance continuous digital product innovation at the team level without compromising innovation novelty.

econ.EM↗

arXiv2024

The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot

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.

cs.SE↗