Daniele Rotolo, PhD
Professor in Science, Technology and Innovation Policy – SPRU, University of Sussex Business School
Associate Professor - Department of Mechanics, Mathematics and Management, Politecnico di Bari
NET-GENESIS: Network Micro-Dynamics in Emerging Technologies
Aim and objectives
The NET-GENESIS project aims to investigate how networks form, evolve and are configured when a new technology emerges. These networks include a number of interlinked actors (e.g. individuals, organisations, institutions) extending across multiple domains in which the reward systems, incentives and power structures can differ markedly (open science vs. market-based).
The architecture of the relationships among these actors may exert a significant influence in shaping technological change in certain directions rather than others, which in turn may have the potential to provide more socially optimal or desirable technological options. A number of examples can be identified to highlight the importance of these networks for emerging technologies. For instance, networks can represent channels through which entrepreneurs and firms access the financial resources (e.g. venture capitals) required to pursue R&D activities. In addition, the open-innovation framework has highlighted how networks are critical conduits for the exchange of knowledge, ideas, and resources among the different actors involved in the innovation process. Finally, networks also extend across science and technology domains, thus stimulating scientific discoveries and supporting the development of novel technological applications.
While the literature contributing to our understanding on how network variables affect actors’ performance and behaviour is quite large, the genesis and dynamics of the networks surrounding emerging technologies remains a relatively unexplored area of research. The NET-GENESIS project aims to contribute to filling this gap by conducting a comparative study (involving 6 case-studies) on the network micro-dynamics of emerging technologies across three industries, i.e. pharmaceuticals, biotechnology, and nanotechnology. To this end, a mixed qualitative-quantitative approach involving several levels of analysis will be adopted.
Funded by
Project: PIOF-GA-2012-331107
Research organisations
SPRU (Science Policy Research Unit)
University of Sussex
Georgia Insitute of Technology
Team
Dr. Daniele Rotolo - PI (Fellow)
SPRU, University of Sussex &
School of Public Policy, Georgia Institute of Technology
Prof. Diana Hicks - Scientist in charge
School of Public Policy, Georgia Institute of Technology
Project outcomes
1) Defining and operationalising of emerging technologies
Despite being extensively investigated across fields, emerging technologies are seldom defined. For this reason, we have focused on the development of a definition of 'emerging technology' and on a framework for their operationalisation.
An emerging technology is "a radically novel and relatively fast growing technology characterised by a certain degree of coherence persisting over time and with the potential to exert a considerable impact on the socio-economic domain(s) which is observed in terms of the composition of actors, institutions and patterns of interactions among those, along with the associated knowledge production processes. Its most prominent impact, however, lies in the future and so in the emergence phase is still somewhat uncertain and ambiguous" (Rotolo et. al. 2015)
Rotolo D, Hicks D, Martin BR (2015). What is an emerging technology? Research Policy, 44(10): 1827-1843.
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2) Case-studies analysis
Building on the developed definition of emerging technologies, we have selected the follwing case-studies of emerging technologies, which will be examined from the diagnostic and therepeutic application perspectives:
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Microneedles
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Cervical cancer
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RNA interference (RNAi)
3) medlineR – A tool to match between databases
A tool (medlineR) was developed to enable the matching of publication records from MEDLINE/PubMed with ISI Web of Science data. This tool will be used for the data collection of the case-studies in the biomedical domain.
Rotolo D, Leydesdorff L (2015). Matching MEDLINE/PubMed data with Web of Science (WoS): A routine in R language. Journal of the Association for Information Science and Technology, 66(10): 2155–2159 (in press).
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4) A triple helix model of medical innovation
We explored the use of the Medical Subject Headings (MeSH) classification of MEDLINE/PubMed to trace the dynamics of medical innovation in terms of interplay among supply, demand, and technological capabilites.
Petersen AM, Rotolo D, Leydesdorff L (2016). A triple helix model of medical innovation: supply, demand, and technological capabilities in terms of medical subject headings Research Policy, 45(3): 666-681.
© 2014 Daniele Rotolo