Title | Is Success Contagious? A Case Study of U.S. Patenting Teams

Abstract | Collaboration is widely acknowledged as a cornerstone of innovation, allowing individuals to combine their expertise to achieve feats that would be untenable alone. While existing research extensively explores team-level predictors of innovation, this study posits that an additional factor, social influence, plays a role as well. Social influence has been rigorously examined in a variety of domains, ranging from healthcare to online social networks. However, its impact within the context of innovation teams remains underexplored. This study seeks to fill this gap by posing a fundamental question: Is success in patenting contagious? Specifically, I question whether individual inventors seeking intellectual property rights significantly enhance their chances of success by collaborating with co-inventors who already possess a track record of patenting success in the field. Using a combination of the PatentsView and World Intellectual Property Organization (WIPO) databases, I identified and extracted all AI-related team patent applications submitted to the U.S. Patent & Trademark Office (USPTO) between 2001 and 2021. From this, I created a network of over 35,000 nodes and 83,000 edges, where each node represents a patent application team and each edge denotes shared membership between teams. I then posited patterns of social influence on this network using the Bayesian inference scheme for fitting Autologistic Actor-Attribute Models (ALAAMs). The results reveal that successful patent application teams (i.e., those that are granted a patent) often share co-inventorship with other successful patent application teams suggesting that success is – to some extent – contagious. However, the study also finds that while connections with successful teams are beneficial, too much collaboration can diminish these benefits. Additionally, teams with members who have an extensive history of patenting may experience innovation fatigue. The main contributions of this research are twofold: it enhances our understanding of innovation dynamics by considering the influence of team members' tacit knowledge from previous collaborations, and it offers practical insights for innovation teams, highlighting the strategic advantage of collaborating with co-inventors who already possess a track record of patenting success in the field.

Bio | Neelam is a fifth-year Ph.D. Candidate in Industrial Engineering & Management Sciences (IEMS) at Northwestern University, with concentrations in Computational Social Science, Applied Statistics & Statistical Learning, and Optimization. Her research is driven by a singular question: How do unseen network connections and influences catalyze or inhibit critical decisions and outcomes in organizations? She explores this question through two complementary streams: one substantive and the other methodological. Substantively, her research discovers the ways in which the structural arrangement and types of relationships – both key components of social influence among individuals or teams in an organization – jointly shape decision-making and performance. Methodologically, she develops and leverages state-of-the-art analytics for modeling social influence phenomena, particularly in contexts where collecting fine-grained relational and behavioral data is impractical. Neelam’s work spans a variety of application areas including public health/family planning, consumer behavior, and the science of science. Her work has been funded by the National Institutes of Health, National Science Foundation, and the Bill & Melinda Gates Foundation. Prior to her Ph.D., Neelam worked as a Strategy & Operations Consultant at Deloitte Consulting for two years. She holds a B.S. in Biomedical & Health Sciences Engineering from NC State University and UNC-Chapel Hill and a B.S. in Economics from NC State University.