Search

Fast growing scientific topics have famously been key harbingers of the new frontiers of science, yet, large-scale analyses of their genesis and impact are rare. We investigated one possible factor connected with a topic’s extraordinary growth: scientific prizes. Our longitudinal analysis of nearly all recognized prizes worldwide and over 11,000 scientific topics from 19 disciplines indicates that topics associated with a scientific prize experience extraordinary growth in productivity, impact, and new entrants. Relative to matched non-prizewinning topics, prizewinning topics produce 40% more papers and 33% more citations, retain 55% more scientists, and gain 37 and 47% more new entrants and star scientists, respectively, in the first five-to-ten years after the prize. Funding do not account for a prizewinning topic’s growth. Rather, growth is positively related to the degree to which the prize is discipline-specific, conferred for recent research, or has prize money. These findings reveal new dynamics behind scientific innovation and investment., Scientific revolutions have famously inspired scientists and innovation but large-scale analyses of scientific revolutions in modern science are rare. Here, the authors investigate one possible factor connected with a topic’s extraordinary growth—scientific prizes.

The ability to objectively assess academic performance is critical to rewarding academic merit, charting academic policy, and promoting science. Quintessential to performing these functions is first the ability to collect valid and current data through increasingly automated online interfaces. Moreover, it is crucial to remove disciplinary and other biases from these data, presenting them in ways that support insightful analysis at various levels. Existing systems are lacking in some of these respects. Here we present Scholar Plot (SP), an interface that harvests bibliographic and research funding data from online sources. SP addresses systematic biases in the collected data through nominal and normalized metrics. Eventually, SP combines synergistically these metrics in a plot form for expert appraisal, and an iconic form for broader consumption. SP’s plot and iconic forms are scalable, representing equally well individual scholars and their academic units, thus contributing to consistent ranking practices across the university organizational structure. In order to appreciate the design principles underlying SP, in particular the informativeness of nominal versus normalized metrics, we also present the results of an evaluation survey taken by senior faculty (n=28) with significant promotion and tenure assessment experience.

Debate over the impact of team composition on the outcome of a contest has attracted sports enthusiasts and sports scientists for years. A commonly held belief regarding team success is the superstar effect; that is, including more talent improves the performance of a team(1). However, studies of team sports have suggested that previous relations and shared experiences among team members improve the mutual understanding of individual habits, techniques and abilities and therefore enhance team coordination and strategy(2–9). We explored the impact of within-team relationships on the outcome of competition between sports teams. Relations among teammates consist of two aspects: qualitative and quantitative. While quantitative aspects measure the number of times two teammates collaborated, qualitative aspects focus on ‘prior shared success’; that is, whether teamwork succeeded or failed. We examined the association between qualitative team interactions and the probability of winning using historical records from professional sports— basketball in the National Basketball Association, football in the English Premier League, cricket in the Indian Premier League and baseball in Major League Baseball—and the multiplayer online battle game Defense of the Ancients 2. Our results show that prior shared success between team members significantly improves the odds of the team winning in all sports beyond the talents of individuals.

As terror groups proliferate and grow in sophistication, a major international concern is the development of scientific methods that explain and predict insurgent violence. Approaches to estimating a group’s future lethality often require data on the group’s capabilities and resources, but by the nature of the phenomenon, these data are intentionally concealed by the organizations themselves via encryption, the dark web, back-channel financing, and misinformation. Here, we present a statistical model for estimating a terror group’s future lethality using latent-variable modeling techniques to infer a group’s intrinsic capabilities and resources for inflicting harm. The analysis introduces 2 explanatory variables that are strong predictors of lethality and raise the overall explained variance when added to existing models. The explanatory variables generate a unique early-warning signal of an individual group’s future lethality based on just a few of its first attacks. Relying on the first 10 to 20 attacks or the first 10 to 20% of a group’s lifetime behavior, our model explains about 60% of the variance in a group’s future lethality as would be explained by a group’s complete lifetime data. The model’s robustness is evaluated with out-of-sample testing and simulations. The findings’ theoretical and pragmatic implications for the science of human conflict are discussed.

This Research Letter examines differences in the size of National Institutes of Health (NIH) grants awarded to first-time male and female principal investigators at top research institutions, including the Big Ten and Ivy League universities.

Social influence is typically studied after a product is released. Yet, audience expectations and discussions begin before a product’s release. This observation suggests a need to understand adoption processes over a product’s life cycle. To explore pre- and postrelease social influence processes, this article uses survey data from Americans exposed to word of mouth for 309 Hollywood movies released over two and a half years. The data suggest pre- and postrelease social influences operate differently. Prerelease social influence displays a critical transition point with relation to adoption: before a critical value, any level of social influence is negligibly related to adoption, but after the critical value, the relationship between social influence and adoption is large and substantive. In contrast, postrelease social influence exhibits a positive linear relationship with adoption. Prerelease social influence is argued to require more exposures than postrelease social influence because of differences in the diagnosticity and accessibility of the information. To complement the survey data, computational models are used to test alternative hypotheses. Evidence from the computational models supports the proposed model of social influence.

Climb atop shoulders and wait for funerals. That, suggested Newton and then Planck, is how science advances (more or less). We've come far since then, but many notions about how people and practices, policies, and resources influence the course of science are still more rooted in traditions and intuitions than in evidence. We can and must do better, lest we resign ourselves to “intuition-based policy” when making decisions and investments aimed at driving scientific progress. Science invited experts to highlight key aspects of the scientific enterprise that are steadily yielding to empirical investigation—and to explain how Newton and Planck got it right (and Einstein got it wrong). — Brad Wible

BACKGROUND The increasing availability of digital data on scholarly inputs and outputs—from research funding, productivity, and collaboration to paper citations and scientist mobility—offers unprecedented opportunities to explore the structure and evolution of science. The science of science (SciSci) offers a quantitative understanding of the interactions among scientific agents across diverse geographic and temporal scales: It provides insights into the conditions underlying creativity and the genesis of scientific discovery, with the ultimate goal of developing tools and policies that have the potential to accelerate science. In the past decade, SciSci has benefited from an influx of natural, computational, and social scientists who together have developed big data–based capabilities for empirical analysis and generative modeling that capture the unfolding of science, its institutions, and its workforce. The value proposition of SciSci is that with a deeper understanding of the factors that drive successful science, we can more effectively address environmental, societal, and technological problems. ADVANCES Science can be described as a complex, self-organizing, and evolving network of scholars, projects, papers, and ideas. This representation has unveiled patterns characterizing the emergence of new scientific fields through the study of collaboration networks and the path of impactful discoveries through the study of citation networks. Microscopic models have traced the dynamics of citation accumulation, allowing us to predict the future impact of individual papers. SciSci has revealed choices and trade-offs that scientists face as they advance both their own careers and the scientific horizon. For example, measurements indicate that scholars are risk-averse, preferring to study topics related to their current expertise, which constrains the potential of future discoveries. Those willing to break this pattern engage in riskier careers but become more likely to make major breakthroughs. Overall, the highest-impact science is grounded in conventional combinations of prior work but features unusual combinations. Last, as the locus of research is shifting into teams, SciSci is increasingly focused on the impact of team research, finding that small teams tend to disrupt science and technology with new ideas drawing on older and less prevalent ones. In contrast, large teams tend to develop recent, popular ideas, obtaining high, but often short-lived, impact. OUTLOOK SciSci offers a deep quantitative understanding of the relational structure between scientists, institutions, and ideas because it facilitates the identification of fundamental mechanisms responsible for scientific discovery. These interdisciplinary data-driven efforts complement contributions from related fields such as scientometrics and the economics and sociology of science. Although SciSci seeks long-standing universal laws and mechanisms that apply across various fields of science, a fundamental challenge going forward is accounting for undeniable differences in culture, habits, and preferences between different fields and countries. This variation makes some cross-domain insights difficult to appreciate and associated science policies difficult to implement. The differences among the questions, data, and skills specific to each discipline suggest that further insights can be gained from domain-specific SciSci studies, which model and identify opportunities adapted to the needs of individual research fields.

Peer-to-peer lending is hypothesized to help equalize economic opportunities for the world's poor. We empirically investigate the "flat-world" hypothesis, the idea that globalization eventually leads to economic equality, using crowdfinancing data for over 660,000 loans in 220 nations and territories made between 2005 and 2013. Contrary to the flat-world hypothesis, we find that peer-to-peer lending networks are moving away from flatness. Furthermore, decreasing flatness is strongly associated with multiple variables: relatively stable patterns in the difference in the per capita GDP between borrowing and lending nations, ongoing migration flows from borrowing to lending nations worldwide, and the existence of a tie as a historic colonial. Our regression analysis also indicates a spatial preference in lending for geographically proximal borrowers. To estimate the robustness for these patterns for future changes, we construct a network of borrower and lending nations based on the observed data. Then, to perturb the network, we stochastically simulate policy and event shocks (e.g., erecting walls) or regulatory shocks (e.g., Brexit). The simulations project a drift towards rather than away from flatness. However, levels of flatness persist only for randomly distributed shocks. By contrast, loss of the top borrowing nations produces more flatness, not less, indicating how the welfare of the overall system is tied to a few distinctive and critical country-pair relationships.

How existing technologies and ideas are recombined into new innovations remains an important question, particularly as the store of prior technology, art, and work expands at an increasing rate. Yet, methodologies for identifying effective recombinations remain a nascent area of research. This paper extends our previous work, which developed a network methodology for assessing a scientific article's recombinations of prior work. The methodology uses information from the entire co-citation network of all papers recorded in the Web of Science to identify combinations of prior work that are conventional or atypical and then identifies the virtuous mix of conventional and atypical pairings associated with high impact work. Here, we summarize our prior method and findings, present new findings, and perform a case study application to the field of management science. First, the results show that despite an ever-increasing frontier of possible new combinations of prior work, atypical combinations of prior work are becoming increasingly rare with time, while the distribution of conventional pairings is increasing with time. Second, our analyses show that with time the atypical pairings found in hit papers have a relatively stable mean rate at which they become conventional pairing. Nevertheless, the variance around the mean is growing significantly, which indicates that there is a greater tendency over time for novel pairings either to be virtually never used again or to become conventional pairings.

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. The authors’ 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.

This symposium presents a mix of empirical and theoretical papers, which explore the way AI can be used for management of knowledge production, organizations and teams. Together these papers allow ...

Many leaders today do not rise through the ranks but are recruited directly out of graduate programs into leadership positions. We use a quasi-experiment and instrumental-variable regression to understand the link between students' graduate school social networks and placement into leadership positions of varying levels of authority. Our data measure students' personal characteristics and academic performance, as well as their social network information drawn from 4.5 million email correspondences among hundreds of students who were placed directly into leadership positions. After controlling for students' personal characteristics, work experience, and academic performance, we find that students' social networks strongly predict placement into leadership positions. For males, the higher a male student's centrality in the school-wide network, the higher his leadership-job placement will be. Men with network centrality in the top quartile have an expected job placement level that is 1.5 times greater than men in the bottom quartile of centrality. While centrality also predicts women's placement, high-placing women students have one thing more: an inner circle of predominantly female contacts who are connected to many nonoverlapping third-party contacts. Women with a network centrality in the top quartile and a female-dominated inner circle have an expected job placement level that is 2.5 times greater than women with low centrality and a male-dominated inner circle. Women who have networks that resemble those of high-placing men are low-placing, despite having leadership qualifications comparable to high-placing women.

In the version of this article initially published, errors occurred in the Acknowledgments.

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.

Science's knowledge base is expanding rapidly, but the breakthrough paper rate is narrowing and scientists take longer to make their first discoveries. Breakthroughs are related to how information is recombined, yet it remains unclear how scientists and inventors forage the knowledge base in search of tomorrow's highest impact ideas. Studying 28 million scientific papers and 5 million U.S. patents, we uncover 2 major findings. First, we identify “Darwin's Conjecture,” which reveals how conventional and novel ideas are balanced within breakthrough papers. Second, we find an “information hotspot.” The hotspot is that cluster of papers of a certain age distribution in the knowledge base that best predict tomorrow's hits. Together, works that combine knowledge according to Darwin's Conjecture or forage in the hotspot double their odds of being in the top 5% or better of citations. These patterns result in over 250 scientific and technology fields, are increasingly dominant, and outperform other predictors of impact, suggesting a universal link between the age of information and scientific discovery.

This paper reviews the literature on small-world networks in social science and management. This relatively new area of research represents an unusual level of cross-disciplinary research within social science and between social science and the physical sciences. We review the findings of this emerging area with an eye to describing the underlying theory of small worlds, the technical apparatus, promising facts, and unsettled issues for future research.

Papers or patents that cite past work of a particular age distribution double their chances of being a hit., Scientists and inventors can draw on an ever-expanding literature for the building blocks of tomorrow’s ideas, yet little is known about how combinations of past work are related to future discoveries. Our analysis parameterizes the age distribution of a work’s references and revealed three links between the age of prior knowledge and hit papers and patents. First, works that cite literature with a low mean age and high age variance are in a citation “hotspot”; these works double their likelihood of being in the top 5% or better of citations. Second, the hotspot is nearly universal in all branches of science and technology and is increasingly predictive of a work’s future citation impact. Third, a scientist or inventor is significantly more likely to write a paper in the hotspot when they are coauthoring than whey they are working alone. Our findings are based on all 28,426,345 scientific papers in the Web of Science, 1945–2013, and all 5,382,833 U.S. patents, 1950–2010, and reveal new antecedents of high-impact science and the link between prior literature and tomorrow’s breakthrough ideas.

Understanding social processes that lead to wise decision making and peak performance is critical for predicting, evaluating and building successful teams. Over the past 50+ years there have been many conceptual developments in understanding teams. A team is formally defined as an intact social system, complete with boundaries, interdependence for some shared purpose, and differentiated member roles. Teams are organized, either by design or by natural evolution, into structured relationships that are governed by interactions that involve power, influence, and varying degrees of cooperation, control, flexibility and adaptability. Team networks enable groups of people to build knowledge, reach consensus, achieve breakthroughs, and generally perform complex problem solving that would not be attainable through either individual efforts or a sequence of additive contributions. A critical question in army commands is how to improve the performance of teams and of multi-team systems (teams that work together to carry out missions).

Social network research has begun to take advantage of fine-grained communications regarding coordination, decision-making, and knowledge sharing. These studies, however, have not generally analyzed how external events are associated with a social network's structure and communicative properties. Here, we study how external events are associated with a network's change in structure and communications. Analyzing a complete dataset of millions of instant messages among the decision-makers in a large hedge fund and their network of outside contacts, we investigate the link between price shocks, network structure, and change in the affect and cognition of decision-makers embedded in the network. When price shocks occur the communication network tends not to display structural changes associated with adaptiveness. Rather, the network "turtles up". It displays a propensity for higher clustering, strong tie interaction, and an intensification of insider vs. outsider communication. Further, we find changes in network structure predict shifts in cognitive and affective processes, execution of new transactions, and local optimality of transactions better than prices, revealing the important predictive relationship between network structure and collective behavior within a social network., 12 pages, 8 figures, Proceedings of the 25th ACM International World Wide Web Conference (WWW) 2016

Users online tend to select information that support and adhere their beliefs, and to form polarized groups sharing the same view - e.g. echo chambers. Algorithms for content promotion may favour this phenomenon, by accounting for users preferences and thus limiting the exposure to unsolicited contents. To shade light on this question, we perform a comparative study on how same contents (videos) are consumed on different online social media - i.e. Facebook and YouTube - over a sample of 12M of users. Our findings show that content drives the emergence of echo chambers on both platforms. Moreover, we show that the users' commenting patterns are accurate predictors for the formation of echo-chambers.

We examine how forms of a firm's embedding in market relationships affect the size of its spreads – i.e., the difference between the selling price and production costs of its goods and services. Building on Harrison White's work on the relational underpinning of market behavior, we argue that the embeddedness of market transactions in social structures furnishes actors with private information and informal governance benefits that shape spreads by adding unique value to transactions and by revealing the price sensitivity of clients. We propose arguments about how a firm's embedded client relationships, interlock ties, and status influence the size of its spreads. Using longitudinal data on the economic and sociological characteristics of law firms that represent the Fortune 200 corporations and top 250 financial firms in America, we find that social structure has significant effects on spreads and that the effects change in scale and direction, depending on the form of embeddedness.

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.

Despite the large numbers of women trained and employed in creative fields, perceptions of creative achievement in the fields of art, music, and literature remain biased by gender. Most recent effo...

The authors argue that a law firm's client network affects employee mobility by easing the diffusion of information across firm boundaries, thereby enhancing organizational performance in attaining high-quality lawyers. The argument is tested by using a sample of the largest law firms in the U.S. The law firms are matched to their corporate clients to model the attainment of lateral partners as a function of social relations in which the law firms are embedded. Ways in which social relations of a hiring organization influence the job matching process and its success in the recruitment of high-quality workers are examined.

Embeddedness in social networks is increasingly seen as a root cause of human achievement, social stratification, and actor behavior. In this article, we review sociological research that examines the processes through which dyadic ties form, persist, and dissolve. Three sociological mechanisms are overviewed: assortative mechanisms that draw attention to the role of actors' attributes, relational mechanisms that emphasize the influence of existing relationships and network positions, and proximity mechanisms that focus on the social organization of interaction.

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 parameters1, 2, 3, 4. Recently, more detailed models have incorporated higher levels of constraint in order to reproduce the actual links observed in real food webs5, 6. Here, building on previous stochastic models of consumer–resource interactions between species1, 2, 3, we propose a highly parsimonious model that can reproduce the overall bipartite structure of cooperative partner–partner interactions, as exemplified by plant–animal mutualistic networks7. 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 networks8, 9, 10. 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 society11, 12, 13, 14.

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.

Mechanisms that enable declining networks to avert structural collapse and performance degradation are not well understood. This knowledge gap reflects a shortage of data on declining networks and an emphasis on models of network growth. Analyzing >700,000 transactions between firms in the New York garment industry over 19 years, we tracked this network's decline and measured how its topology and global performance evolved. We find that favoring asymmetric (disassortative) links is key to preserving the topology and functionality of the declining network. Based on our findings, we tested a model of network decline that combines an asymmetric disassembly process for contraction with a preferential attachment process for regrowth. Our simulation results indicate that the model can explain robustness under decline even if the total population of nodes contracts by more than an order of magnitude, in line with our observations for the empirical network. These findings suggest that disassembly mechanisms are not simply assembly mechanisms in reverse and that our model is relevant to understanding the process of decline and collapse in a broad range of biological, technological, and financial networks.

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.

L'A. s'efforce de comprendre comment les structures sociales influent sur la performance economique. Il s'interesse plus particulierement a la notion d'« enracinement ». Il montre que celle-ci permet de saisir comment les relations sociales modelent l'activite economique. Il porte de meme son attention sur les reseaux sociaux qui jouent un role important en cette affaire. Il examine en premier lieu le concept d'enracinement structurel. Il compare ensuite les performances economiques d'entreprises americaines qui fonctionnent en reseaux avec les resultats d'activite d'entreprises qui n'operent que par le biais du marche. Il presente, a ce propos, un certain nombre de donnees collectees aux Etats-Unis, a New York entre 1990 et 1991

The current research used the contexts of U.S. presidential debates and negotiations to examine whether matching the linguistic style of an opponent in a two-party exchange affects the reactions of third-party observers. Building off communication accommodation theory (CAT), interaction alignment theory (IAT), and processing fluency, we propose that language style matching (LSM) will improve subsequent third-party evaluations because matching an opponent's linguistic style reflects greater perspective taking and will make one's arguments easier to process. In contrast, research on status inferences predicts that LSM will negatively impact third-party evaluations because LSM implies followership. We conduct two studies to test these competing hypotheses. Study 1 analyzed transcripts of U.S. presidential debates between 1976 and 2012 and found that candidates who matched their opponent's linguistic style increased their standing in the polls. Study 2 demonstrated a causal relationship between LSM and third-party observer evaluations using negotiation transcripts., Comment: in the Personality and Social Psychology Bulletin (2015)

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.

Agents in creative enterprises are embedded in networks that inspire, support, and evaluate their work. Here, we investigate how the mechanisms by which creative teams self-assemble determine the structure of these collaboration networks. We propose a model for the self-assembly of creative teams that has its basis in three parameters: team size, the fraction of newcomers in new productions, and the tendency of incumbents to repeat previous collaborations. The model suggests that the emergence of a large connected community of practitioners can be described as a phase transition. We find that team assembly mechanisms determine both the structure of the collaboration network and team performance for teams derived from both artistic and scientific fields.

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 firm's 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.

This article discusses how social exchange relations between producers and consumers affect the prices producers charge their clients in the large corporate law market. The embeddedness framework o...

Building on social embeddedness theory, we examine how the competencies and resources of one corporate actor in a network are transferred to another actor that uses them to enhance transactions wit...