Essays on Collaborative Innovation

Abstract

This dissertation examines how technological and policy shifts transform collaboration ininnovation across three different contexts: healthcare, commercial space, and entrepreneurship. Using large-scale empirical data and advanced econometric methods, I demonstrate how individuals and organizations can leverage technology and strategic partnerships to accelerate innovation while navigating complex market dynamics. My first study analyzes the impact of adopting digitization technology on scientific collaboration among 313,257 researchers at 148 U.S. teaching hospitals. Exploiting the staggered implementation of Electronic Health Records (EHR) systems as a quasi-experimental shock, I find that digitization increases research collaboration by 6.1% and intellectual diversity of teams by 1.1%. The effect is 24% stronger than other traditional health information technology (IT) systems such as data management software, driven by improvements in data interpretability, and doubles when collaborators use the same EHR system because of increased data shareability. This study reveals how standardized IT infrastructure can unlock collaborative potential, with policy implications for the 3.8 trillion dollars U.S. healthcare industry and the innovation of medical Artificial Intelligence (AI) and machine learning (ML). The second study assesses the outcomes of NASA’s commercial space initiative by analyzing the scientific and economic impacts of experiments carried out onboard the International Space Station (ISS). Using NASA’s launch records, I constructed a novel dataset of space experiments conducted between 2001 and 2021. Analyzing the publications and patents resulting from these experiments, I find that ISS-based research generates 63% more paper and 82% more patent citations than their digital twins—comparable experiments conducted in Earth labs—controlling for confounding factors such as the reputation of the principal investigator and the publication outlet. However, this impact diverges sharply between public and private research, with commercial experiments showing limited knowledge spillovers. These findings contribute to our understanding of how innovative organizations such as NASA, academic institutions, and high-tech companies responds to sudden shifts in technological and policy environments. They also inform the design of public-private partnerships in emerging industries, particularly as the space economy is projected to approach $500 billion by 2030. My third study examines how high-tech entrepreneurs strategically position themselves for different exit outcomes. Two specific outcomes are considered—acquisition and Initial Public Offering (IPO), given that more than 90% of startup entrepreneurs exit through one of these channels. Applying ML methods on the U.S. federal SBIR program data, I developed an “orientation score” that isolates a firm’s positioning toward one outcome over the other while holding its underlying quality constant. My analyses reveal important variation in orientation associated with both the technological specialty of firms and the economic environment surrounding them. IT firms orient 40% more toward collaborative exits via acquisition, while biotech firms favor the more competitive path through IPO. Regional innovation ecosystems strongly predict these strategies, with local patenting activity increasing acquisition orientation by more than 30%. Collectively, these three studies advance our understanding of how organizations adapt to technological disruption and policy change to create value through collaboration. The findings have immediate applications for healthcare systems implementing digital transformation, government agencies designing innovation programs, and investors evaluating startup strategies. My work demonstrates that successful innovation increasingly depends not on isolated brilliance but on the strategic orchestration of collaborative networks, a capability that determines competitive advantage across scientific domains and industries. This dissertation contributes methodologically through the application of advanced econometric models, such as the staggered difference-in-differences estimators, to address treatment effect heterogeneity, the use of ML to resolve endogenous selection, and the construction of unique datasets linking innovation inputs to measurable outcomes. These approaches enable rigorous causal inference and prediction, allowing me to address important questions facing the production of scientific knowledge and the strategic dynamics within innovation ecosystems