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We leverage the knowledge network representation of the Medical Subject Heading (MeSH) ontology to infer conceptual distances between roughly 30,000 distinct MeSH keywords — each being prescribed to particular knowledge domains — in order to quantify the origins of cross-domain biomedical convergence. Analysis of MeSH co-occurrence networks based upon 21.6 million research articles indexed by PubMed identifies three robust knowledge clusters: micro-level biological entities and structures; meso-level representations of systems, and diseases and diagnostics; and emergent macro-level biological and social phenomena. Analysis of cross-cluster dynamics shows how these domains integrated from the 1990s onward via technological and informatic capabilities — captured by MeSH belonging to the “Technology, Industry, and Agriculture” (J) and “Information Science” (L) branches — representing highly controllable, scalable and permutable research processes and invaluable imaging techniques for illuminating fundamental yet transformative structure–function–behavior questions. Our results indicate that 8.2% of biomedical research from 2000 to 2018 include MeSH terms from both the J and L MeSH branches, representing a 291% increase from 1980s levels. Article-level MeSH analysis further identifies the increasing prominence of cross-domain integration, and confirms a positive relationship between team size and topical diversity. Journal-level analysis reveals variable trends in topical diversity, suggesting that demand and appreciation for convergence science vary by scholarly community. Altogether, we develop a knowledge network framework that identifies the critical role of techno-informatic inputs as convergence bridges — or catalyzers of integration across distinct knowledge domains — as highlighted by the 1990s genomics revolution, and onward in contemporary brain, behavior and health science initiatives.

Some ideologies, such as liberalism, conservatism, and socialism, are complex symbolic structures. Mastering them requires specialization, and because we are all amateurs in almost all symbolically rich domains, most people are not ideologically sophisticated. Instead, they reason about politics in a maturationally natural way, via friend-foe representations and inferences based on those representations (for example, friends of foes are foes). However, even complex ideologies are much simpler than the political, economic, and social systems that they are supposed to represent. Hence, all ideologies are inaccurate to varying degrees. More subtly, all are incomplete in various ways; in particular, they fail to anticipate some crucial events (for example, global warming), which leads to unanticipated value trade-offs and strategic conundrums. Ideologists sometimes adapt to incompleteness via recombinant innovation, producing hybrid ideologies (for example, ecosocialism). In turn, this tends to produce inconsistencies between new and old parts of an ideology. Thus, inaccuracy, incompleteness, and inconsistency are not pathologies of political belief systems. They are the inevitable result of ideologists grappling with a reality that is far more complex than their symbolic constructions can be. Therefore, evaluations of ideologies that identify errors, incompleteness, or inconsistency at a single point in time are often unenlightening. Following Imre Lakatos, evaluations should focus on how a sequence of ideologies—an ideological tradition—evolves. [ABSTRACT FROM AUTHOR]

To what degree has the vast space of higher-order knowledge combinations been explored and how has it evolved over time? To address these questions, we first develop a systematic approach to measuring combinatorial innovation in the biomedical sciences based upon the comprehensive ontology of Medical Subject Headings (MeSH) developed and maintained by the US National Library of Medicine. As such, this approach leverages an expert-defined knowledge ontology that features both breadth (27,875 MeSH analyzed across 25 million articles indexed by PubMed that were published from 1902 onwards) and depth (we differentiate between Major and Minor MeSH terms to identify differences in the knowledge network representation constructed from primary research topics only). With this level of uniform resolution, we differentiate between three different modes of innovation contributing to the combinatorial knowledge network: (i) conceptual innovation associated with the emergence of new concepts and entities (measured as the entry of new MeSH) and (ii) recombinant innovation, associated with the emergence of new combinations, which itself consists of two types: peripheral (i.e. combinations involving new knowledge) and core (combinations comprised of pre-existing knowledge only). Another relevant question we seek to address is whether examining triplet and quartet combinations, in addition to the more traditional dyadic or pairwise combinations, provides evidence of any new phenomena associated with higher-order combinations. Analysis of the size, growth, and coverage of combinatorial innovation yield results that are largely independent of the combination order, with some subtle caveats identified at higher order (i.e. beyond the common dyadic or pairwise representation of combinations). Our main results are two-fold: (a) despite the persistent addition of new MeSH terms, the network is densifying over time meaning that scholars are increasingly exploringand realizing the vast space of all knowledge combinations and (b) conceptual innovation is increasingly concentrated within single research articles, a harbinger of the recent paradigm shift towards convergence science.

To what degree has the vast space of higher-order knowledge combinations been explored and how has it evolved over time? To address these questions, we first develop a systematic approach to measuring combinatorial innovation in the biomedical sciences based upon the comprehensive ontology of Medical Subject Headings (MeSH) developed and maintained by the US National Library of Medicine. As such, this approach leverages an expert-defined knowledge ontology that features both breadth (27,875 MeSH analyzed across 25 million articles indexed by PubMed that were published from 1902 onwards) and depth (we differentiate between Major and Minor MeSH terms to identify differences in the knowledge network representation constructed from primary research topics only). With this level of uniform resolution, we differentiate between three different modes of innovation contributing to the combinatorial knowledge network: (i) conceptual innovation associated with the emergence of new concepts and entities (measured as the entry of new MeSH) and (ii) recombinant innovation, associated with the emergence of new combinations, which itself consists of two types: peripheral (i.e. combinations involving new knowledge) and core (combinations comprised of pre-existing knowledge only). Another relevant question we seek to address is whether examining triplet and quartet combinations, in addition to the more traditional dyadic or pairwise combinations, provides evidence of any new phenomena associated with higher-order combinations. Analysis of the size, growth, and coverage of combinatorial innovation yield results that are largely independent of the combination order, with some subtle caveats identified at higher order (i.e. beyond the common dyadic or pairwise representation of combinations). Our main results are two-fold: (a) despite the persistent addition of new MeSH terms, the network is densifying over time meaning that scholars are increasingly exploringand realizing the vast space of all knowledge combinations and (b) conceptual innovation is increasingly concentrated within single research articles, a harbinger of the recent paradigm shift towards convergence science. [ABSTRACT FROM AUTHOR]

Digitization is increasing the complexity and variety of educational services supplied and demanded. However, there is a lack of a systematic treatment of this phenomenon from the perspective of innovation and entrepreneurship. This paper employs the complex adaptive systems approach to explore how digitization changes the characteristics, generativity (combinatorial possibilities) and architecture of online educational services. Our main claims are that: (a) online courses (OCs) are modular complex hierarchical systems; (b) the development of new OCs is a significant type of innovation (not limited merely to technological change) brought about by entrepreneurial reconfiguration of modules that create new combinations in the design space of new educational services; (c) these new combinations generate new OC systems; and (d) OCs are embedded in broader institutional and social structures (e.g. universities) that co-evolve with technological change. By means of a formal combinatorial model, we examine the computational mechanism and the principles of connection-making that govern how new OCs are created and adapted. [ABSTRACT FROM AUTHOR]

Knowledge networks play a vital role in the organizational pursuit of recombinant innovation. Based on the knowledge network, we propose a new concept of recombinant distance and investigate its effect on recombinant innovation. Specifically, we minutely explore the knowledge network to assess the recombinant distance between the knowledge elements of the focal organization and its alliance partners and to examine whether and how the recombinant distance in a knowledge network affects the focal firm's subsequent recombination performance. Further, we seek to explore how social network governance (i.e., structural and relational governance) moderates the abovementioned relationship. To examine these mechanisms, based on Zhongguancun China Science Park (ZCSP), we construct a unique database that combines survey data, technology alliance data, and Orbis and Patents big data. Based on 6,973,188 granted patents, we create knowledge networks and provide a new measurement of recombinant distance from partners. The empirical analyses reveal an inverted U-shaped relationship between the focal firm's recombinant distance from its partners and its recombinant innovation. Moreover, this inverse U-shaped relationship becomes steeper when the focal firm spans more structural holes or has a lower private–public ratio of alliance partners. • We contribute to recombination literature by introducing recombinant distance. • We construct knowledge networks to measure recombinant distance with partners. • Recombinant distance with partners positively affects recombinant innovation. • Structural and relational governance structure moderates the above relationship. • We build a unique dataset by combining survey, manual coding and patent big data. [ABSTRACT FROM AUTHOR]

Although many methods have been proposed for engineering service systems and customer solutions, most of these approaches give little consideration to recombinant service innovation. Recombinant innovation refers to reusing and integrating resources that were previously unconnected. In an age of networked products and data, we can expect that many service innovations will be based on adding, dissociating, and associating existing value propositions by accessing internal and external resources instead of designing them from scratch. The purpose of this paper is to identify if current service engineering approaches account for the mechanisms of recombinant innovation and to design a method for recombinant service systems engineering. In a conceptual analysis of 24 service engineering methods, the study identified that most methods (1) focus on designing value propositions instead of service systems, (2) view service independent of physical goods, (3) are either linear or iterative instead of agile, and (4) do not sufficiently address the mechanisms of recombinant innovation. The paper discusses how these deficiencies can be remedied and designs a revised service systems engineering approach that reorganizes service engineering processes according to four design principles. The method is demonstrated with the recombinant design of a service system for predictive maintenance of agricultural machines. [ABSTRACT FROM AUTHOR]

We analyse the autocatalytic structure of technological networks and evaluate its significance for the dynamics of innovation patenting. To this aim, we define a directed network of technological fields based on the International Patents Classification, in which a source node is connected to a receiver node via a link if patenting activity in the source field anticipates patents in the receiver field in the same region more frequently than we would expect at random. We show that the evolution of the technology network is compatible with the presence of a growing autocatalytic structure, i.e. a portion of the network in which technological fields mutually benefit from being connected to one another. We further show that technological fields in the core of the autocatalytic set display greater fitness, i.e. they tend to appear in a greater number of patents, thus suggesting the presence of positive spillovers as well as positive reinforcement. Finally, we observe that core shifts take place whereby different groups of technology fields alternate within the autocatalytic structure; this points to the importance of recombinant innovation taking place between close as well as distant fields of the hierarchical classification of technological fields.

Mashups enable end-users to "mix and match" data and services available on the web to create applications. Their creation is supported by a complex ecosystem of i) data providers who offer open APIs to users, ii) users who combine APIs into mashups, and iii) platforms, such as the ProgrammableWeb or Mashape, that facilitate the construction and publication of mashups. In this article, we argue that the evolution of the mashup ecosystem can be explained in terms of ecosystem niches anchored around hub or keystone APIs. The members of a niche are focused on an area of specialization (e.g., mapping applications) and contribute their knowledge to the value proposition of the ecosystem as a whole. To demonstrate the formation of niches in the mashup ecosystem, we model groups of related mashups as species, and we reconstruct the evolution of mashup species through phylogenetic analysis.

Today, the EU has changed its economic development support strategy for regions from a mainly procedural budgeting model to a more substantive one, especially regarding the priority to promote regional innovation. Thus, instead of a regional operational programme that emphasized budgetary controls, phasing and expenditure refinement, this was to be paralleled by a regional innovation strategy (RIS phase 3 or RIS3) to attract EU regional funding assistance. This was a radical departure from all hitherto prevailing ERDF/FEDER methodologies by which regions and their member states submitted such funding requests. This article explores this policy transition, its strengths and weaknesses. In particular, it presents empirically informed accounts of the ways in which RIS3 was received, comprehended and implemented in three regions of Portugal. In addition, it presents results of interviews also conducted with key central state actors in Lisbon. A major aim was to see how each policy level reacted to the way innovation was pre-defined in terms of regional ‘smart specialization' whereby regions dropped un-innovative activities and projected their best candidate specializations. Questioning of the validity of the notion in the innovation context occurs based on both primary and secondary research. A secondary aim was to investigate the methodologies, concepts and policy frameworks actually deployed in developing RIS3s in three Portuguese regions; Algarve, Centro and Norte. Third, another important aim in a follow-up section was to conduct anex postevaluation of the resulting approved implementation strategy. Discussion and conclusions are drawn in the final section. [ABSTRACT FROM AUTHOR]

Evolutionary economic geographers propose that regional diversification is a pathdependent process whereby industries grow out of pre-existing industrial structures through technologically related localised knowledge spillovers and learning. This article examines whether this also applies to emerging radical technologies that create the foundation for new industries. The article develops a new measure for technological relatedness between the knowledge base of a region and that of a radical technology based on patent classes. It demonstrates that emerging fuel cell technology develops where the regional knowledge base is technologically related to that of fuel cells and consequently confirms the evolutionary thesis. [ABSTRACT FROM AUTHOR]

Copyright of Regional Studies is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates the antecedents of patent importance by looking at the prior knowledge set of academic inventors. Using independent methodologies, we distinguish between the scientific knowledge set and the technical knowledge set, and separate these from other kinds of prior expertise. We find that the patents of the inventors who have a prior scholarly knowledge of the topic are on average more important (more cited after 6 years). Conversely, we find an inverted U-shape relationship between prior technical relatedness and patent importance. These results are potentially useful to support the work of practitioners such as university technology managers, which often face difficulties in identifying the importance and perspective value of the disclosed inventions, amid high market and legal uncertainty and budget shortages. [ABSTRACT FROM AUTHOR]

Mashups enable end-users to "mix and match" data and services available on the web to create applications. Their creation is supported by a complex ecosystem of i) data providers who offer open APIs to users, ii) users who combine APIs into mashups, and iii) platforms, such as the ProgrammableWeb or Mashape, that facilitate the construction and publication of mashups. In this article, we argue that the evolution of the mashup ecosystem can be explained in terms of ecosystem niches anchored around hub or keystone APIs. The members of a niche are focused on an area of specialization (e.g., mapping applications) and contribute their knowledge to the value proposition of the ecosystem as a whole. To demonstrate the formation of niches in the mashup ecosystem, we model groups of related mashups as species, and we reconstruct the evolution of mashup species through phylogenetic analysis. [ABSTRACT FROM AUTHOR]

Abstract: We propose a new evolutionary approach to model technological change based on an extension of replicator dynamics with recombination and mutation. It gives rise to interactive innovation–selection dynamics. The model allows studying the combined effects of selection and variety generation on evolutionary–economic change. The developed framework describes a population of boundedly rational entrepreneurs who decide each period on the allocation of investments in different production technologies. They tend to invest in below-average cost technologies, just as under replicator dynamics. In addition, they spend a constant fraction of investments, captured by mutation and recombination rates, on alternative technologies and research on recombinant innovation. As opposed to most previous studies, mutation and recombination are here conceptual variables with a concrete behavioral interpretation, namely describing the decision rules (heuristics) of investors. We compare the dynamics of shares of investments in various technologies for three cases: with constant costs of capital, with costs decreasing steadily and exogenously over time, and with costs depending on the level of cumulative investments. For each model version, we examine under which conditions the coexistence of technological options is feasible and optimal in terms of minimizing the average cost of investments. [Copyright &y& Elsevier]

• This study uncovers which network configurations are responsible for recombinant innovation and how firms develop network management capabilities. • These issues are explored through a qualitative study undertaken in the European motorsport industry. • The findings show that motorsport companies embark on two different network configurations which allow for unique recombination processes. • Motorsport companies develop three overarching network management capabilities which contribute to managing diversity and knowledge integration efforts. Research linking interorganizational networks to innovation has emphasized the knowledge recombination benefits associated with spanning structural holes and crossing institutional boundaries. However, network research has also highlighted that, while structural and institutional separation may provide increasing levels of knowledge diversity, this heterogeneity does not necessarily translate into novel knowledge re-combinations. Our study addresses these issues by uncovering which network configurations are responsible for novel knowledge recombination and how firms develop a capability at managing diversity through a qualitative study undertaken in the European motorsport industry. The findings show that motorsport companies embark on two different network configurations which allow for unique recombination processes and outcomes. This also underlines the development of three overarching network management capabilities which contribute to managing diversity and knowledge integration efforts. Our research, therefore, provides a deeper understanding of how firms may configure their networks for recombinant innovation and the role of agency in mobilizing and managing diverse knowledge towards innovation. [ABSTRACT FROM AUTHOR]