*In all papers, all authors contributed equally. Authors listed in alphabetical, reverse alphabetical or random order.

Demand-driven Innovation and Spillover Effects on Adjacent Technological Domains: Evidence from Electric Vehicle Technologies

Jino Lu

Revise & Resubmit at Organization Science

Best Conference Paper Finalist, Strategic Management Society Annual Conference, 2023

Best Conference PhD Paper, Strategic Management Society Annual Conference, 2023

Knowledge and Innovation Interest Group Best Paper Award, Strategic Management Society Annual Conference, 2023

Will Mitchell Dissertation Research Grant, Strategic Management Society, 2023

Greif Entrepreneurship PhD Research Award, USC Lloyd Greif Center for Entrepreneurial Studies, 2023

Dissertation Completion Grant, USC Marshall School of Business, 2023


Strategy and innovation scholars have long emphasized the crucial role of demand in driving innovation progress. However, relatively less is known about how changes in demand conditions in a domain impact the reallocation of innovation resources and technological progress in other domains. In this study, I propose that an increase in demand for innovation in a domain can have negative impacts on firms in adjacent domains, because it competes away knowledge workers, a critical innovation resource. Empirically, I exploit an unexpected environmental policy shock that led to an exogenous increase in demand for electric vehicle (EV) technologies. I find that, following an increase in innovation activities in the EV domain, some firms in adjacent technological domains not only experienced a decline in EV innovations, but also suffered a more significant decline (twice as much) in their core technological domains and their ability to explore new technological domains. This negative effect was stronger for firms that were farther in the product market from the EV domain but were in a related technological space, and for firms in other growing (rather than declining) technological domains (e.g., photovoltaic technologies). Further analyses suggest that this occurred because firms in adjacent technological domains were more likely to lose inventors to firms in the EV domain. These findings have implications for managers and policymakers by highlighting potential negative spillover effects of demand-based innovation policies and practices.

Intellectual Distance and Propensity to Engage with New Technological Development: Evidence from Electric Vehicle Technologies

Jino Lu

Revise & Resubmit at Organization Science

Best Conference PhD Paper, Strategic Management Society Annual Conference, 2022

The Bent Dalum Best PhD Paper Award, DRUID Academy Conference, 2023


Firms and policymakers are highly dependent on academic science for knowledge advancement to help overcome technological barriers and address pressing societal challenges, and they increasingly engage in shaping the research direction of high-quality academic scientists towards certain innovation domains. However, we know relatively little about how scientists respond to an increased demand for their expertise in an innovation domain. In this paper, I examine how scientists’ research productivity (which proxies their research ability) and intellectual distance from the demand shape their propensity to respond to the demand and the quality of their research outcomes in the domain. Empirically, I exploit an unanticipated environmental policy shock that led to increased demand for innovation in electric vehicle (EV) technologies. Empirical results suggest that more productive scientists (from both close and distant domains) are more likely to produce higher-quality EV research outcomes. Furthermore, EV research that includes productive coauthors from distant domains are more likely to be of higher quality and to be utilized in future technological innovations (i.e., patents). However, I also find that as scientists’ distance from the EV domain increases, more productive scientists progressively have reduced incentive to engage with EV research, relative to less productive scientists. Specifically, in domains intellectually closer to EV research, more productive close scientists are more likely to engage with EV research than less productive close scientists. By contrast, in domains intellectually more distant from EV research, less productive distant scientists are relatively more likely to engage with EV research than more productive distant scientists.

Mapping the Knowledge Space: Exploiting Unassisted Machine Learning Tools

Florenta Teodoridis, Jino Lu, and Jeffrey L. Furman

2nd round Revise & Resubmit at Strategic Management Journal


Understanding factors affecting the direction of innovation is a central aim of research in the economics of innovation. Progress on this topic has been inhibited by difficulties in measuring distance and movement in knowledge space. We describe a methodology that infers the mapping of the knowledge landscape based on text documents. The approach is based on an unassisted machine learning technique, Hierarchical Dirichlet Process (HDP), which flexibly identifies patterns in text corpora. The resulting mapping of the knowledge landscape enables calculations of distance and movement, measures that are valuable in several contexts for research in innovation. We benchmark and demonstrate the benefits of this approach in the context of 44 years of USPTO data.

Overcoming the Division of Labor in Scientific Research for Complementary Innovation: Evidence from Quantum Computing

Avi Goldfarb, Jino Lu, and Florenta Teodoridis

Reject & Resubmit at Management Science


Large corporate labs play an important role in innovation. Recently, there has been a trend toward universities producing scientific research and then corporate labs developing this research into practical applications. This division of scientific research labor can have negative consequences for the development of general purpose technologies and other enabling technologies. These technologies rely on a positive feedback loop of innovation, from seeding to complementary trajectories and back, in order to generate substantial productivity gains for companies and for the economy overall. A push against the increasing division of scientific research labor may catalyze the feedback loop. We explore this possibility in the context of the development of quantum computers. After a change in companies’ incentives to engage in scientific research, following a surprise announcement about the near-term commercial potential of quantum computing, we document a rise in company academic publications and patents in quantum computing hardware. Soon after, we document a rise in academic publications and patents in the complementary software trajectory. We also find suggestive evidence of a feedback loop between the hardware and the software trajectories. We interpret these results to suggest complementarities between company and university scientific research in the context of a newly emerging enabling technology.

The Partisanship of American Inventors

Daniel Fehder, Florenta Teodoridis, Joseph Raffiee, and Jino Lu

Revise & Resubmit at Research Policy


Using new panel data on 251,511 patent inventors matched with voter registration records containing partisan affiliation, we provide the first large-scale look into the partisanship of American inventors. We document that the modal inventor is Republican, with a slight decline over time. We also find that the proportion of Independent inventors declined while the proportion of Democrat inventors increased between 2014 and 2020. These patterns are not reflective of trends of partisan affiliation amongst the broader population. Last, we document that the partisan affiliation of inventors is associated with technological inventions related to guns and climate change, two issue areas associated with partisan divide.

Jino Lu