Brian Uzzi

The Increasing Dominance of Teams in Production of Knowledge

We have used 19.9 million papers over 5 decades and 2.1 million patents to demonstrate that teams increasingly dominate solo authors in the production of knowledge. Research is increasingly done in teams across nearly all fields. Teams typically produce more frequently cited research than individuals do, and this advantage has been increasing over time. Teams now also produce the exceptionally high-impact research, even where that distinction was once the domain of solo authors. These results are detailed for sciences and engineering, social sciences, arts and humanities, and patents, suggesting that the process of knowledge creation has fundamentally changed.

Embeddedness in the making of financial capital: How social relations and networks benefit firms seeking financing

I investigate how social embeddedness affects an organization’s acquisition and cost of financial capital in middle-market banking—a lucrative but understudied financial sector. Using existing theory and original fieldwork, I develop a framework to explain how embeddedness can influence which firms get capital and at what cost. I then statistically examine my claims using national data on small-business lending. At the level of dyadic ties, I find that firms that embed their commercial transactions with their lender in social attachments receive lower interest rates on loans. At the network level, firms are more likely to get loans and to receive lower interest rates on loans if their network of bank ties has a mix of embedded ties and arm’s-length ties. These network effects arise because embedded ties motivate network partners to share private resources, while arm’s-length ties facilitate access to public information on market prices and loan opportunities so that the benefits of different types of ties are optimized within one network. I conclude with a discussion of how the value produced by a network is at a premium when it creates a bridge that links the public information of markets with the private resources of relationships.

Collaboration and Creativity: The Small World Problem 1

Small world networks have received disproportionate notice in diverse fields because of their suspected effect on system dynamics. The authors analyzed the small world network of the creative artists who made Broadway musicals from 1945 to 1989. Using original arguments, new statistical methods, and tests of construct validity, they found that the varying “small world” properties of the systemic‐level network of these artists affected their creativity in terms of the financial and artistic performance of the musicals they produced. The small world network effect was parabolic; performance increased up to a threshold, after which point the positive effects reversed.

Multi-University Research Teams: Shifting Impact, Geography, and Stratification in Science

This paper demonstrates that teamwork in science increasingly spans university boundaries, a dramatic shift in knowledge production that generalizes across virtually all fields of science, engineering, and social science. Moreover, elite universities play a dominant role in this shift. By examining 4.2 million papers published over three decades, we found that multi-university collaborations (i) are the fastest growing type of authorship structure, (ii) produce the highest-impact papers when they include a top-tier university, and (iii) are increasingly stratified by in-group university rank. Despite the rising frequency of research that crosses university boundaries, the intensification of social stratification in multi-university collaborations suggests a concentration of the production of scientific knowledge in fewer rather than more centers of high-impact science.

Atypical Combinations and Scientific Impact

Making an Impact How big a role do unconventional combinations of existing knowledge play in the impact of a scientific paper? To examine this question, Uzzi et al. (p. 468) studied 17.9 million research articles across five decades of the Web of Science, the largest repository of scientific research. Scientific work typically appeared to draw on highly conventional, familiar mixtures of knowledge. The highest-impact papers were not the ones that had the greatest novelty, but had a combination of novelty and otherwise conventional combinations of prior work. Highly cited work is simultaneously conventional and unconventional. Novelty is an essential feature of creative ideas, yet the building blocks of new ideas are often embodied in existing knowledge. From this perspective, balancing atypical knowledge with conventional knowledge may be critical to the link between innovativeness and impact. Our analysis of 17.9 million papers spanning all scientific fields suggests that science follows a nearly universal pattern: The highest-impact science is primarily grounded in exceptionally conventional combinations of prior work yet simultaneously features an intrusion of unusual combinations. Papers of this type were twice as likely to be highly cited works. Novel combinations of prior work are rare, yet teams are 37.7% more likely than solo authors to insert novel combinations into familiar knowledge domains.

Embeddedness and Price Formation in the Corporate Law Market

The determination of prices is a key function of markets, yet sociologists are just beginning to study it. Most theorists view prices as a consequence of economic processes. By contrast, we consider how social structure shapes prices. Building on embeddedness arguments and original fieldwork at large law firms, we propose that a firms embedded relationships influence prices by prompting private-information flows and informal governance arrangements that add unique value to goods and services. We test our arguments with a separate longitudinal dataset on the pricing of legal services by law firms that represent corporate America. We find that embeddedness can significantly increase and decrease prices net of standard variables and in markets for both complex and routine legal services. Moreover, results show that three forms of embeddedness—embedded ties, board memberships, and status—affect prices in different directions and have different magnitudes of effects that depend on the complexity of the legal service.

A Multi-Level Systems Perspective for the Science of Team Science

Understanding how teams function is vital because they are increasingly dominating the production of high-impact science. This Commentary describes recent research progress and professional developments in the study of scientific teamwork, an area of inquiry termed the “science of team science” (SciTS, pronounced “sahyts”). It proposes a systems perspective that incorporates a mixed-methods approach to SciTS that is commensurate with the conceptual, methodological, and translational complexities addressed within the SciTS field. The theoretically grounded and practically useful framework is intended to integrate existing and future lines of SciTS research to facilitate the field’s evolution as it addresses key challenges spanning macro, meso, and micro levels of analysis.

Strong contributors to network persistence are the most vulnerable to extinction

The architecture of mutualistic networks facilitates coexistence of individual participants by minimizing competition relative to facilitation. However, it is not known whether this benefit is received by each participant node in proportion to its overall contribution to network persistence. This issue is critical to understanding the trade-offs faced by individual nodes in a network. We address this question by applying a suite of structural and dynamic methods to an ensemble of flowering plant/insect pollinator networks. Here we report two main results. First, nodes contribute heterogeneously to the overall nested architecture of the network. From simulations, we confirm that the removal of a strong contributor tends to decrease overall network persistence more than the removal of a weak contributor. Second, strong contributors to collective persistence do not gain individual survival benefits but are in fact the nodes most vulnerable to extinction. We explore the generality of these results to other cooperative networks by analysing a 15-year time series of the interactions between designer and contractor firms in the New York City garment industry. As with the ecological networks, a firms survival probability decreases as its individual nestedness contribution increases. Our results, therefore, introduce a new paradox into the study of the persistence of cooperative networks, and potentially address questions about the impact of invasive species in ecological systems and new competitors in economic systems.

Advancing the Science of Team Science

The First Annual International Science of Team Science (SciTS) Conference was held in Chicago, IL April 22–24, 2010. This article presents a summary of the Conference proceedings. Clin Trans Sci 2010; Volume 3: 263–266.

A simple model of bipartite cooperation for ecological and organizational networks

In theoretical ecology, simple stochastic models that satisfy two basic conditions about the distribution of niche values and feeding ranges have proved successful in reproducing the overall structural properties of real food webs, using species richness and connectance as the only input parameters. Recently, more detailed models have incorporated higher levels of constraint in order to reproduce the actual links observed in real food webs. Here, building on previous stochastic models of consumer–resource interactions between species, we propose a highly parsimonious model that can reproduce the overall bipartite structure of cooperative partner–partner interactions, as exemplified by plant–animal mutualistic networks. Our stochastic model of bipartite cooperation uses simple specialization and interaction rules, and only requires three empirical input parameters. We test the bipartite cooperation model on ten large pollination data sets that have been compiled in the literature, and find that it successfully replicates the degree distribution, nestedness and modularity of the empirical networks. These properties are regarded as key to understanding cooperation in mutualistic networks. We also apply our model to an extensive data set of two classes of company engaged in joint production in the garment industry. Using the same metrics, we find that the network of manufacturer–contractor interactions exhibits similar structural patterns to plant–animal pollination networks. This surprising correspondence between ecological and organizational networks suggests that the simple rules of cooperation that generate bipartite networks may be generic, and could prove relevant in many different domains, ranging from biological systems to human society.