Final Report Summary - NET-GENESIS (NET-GENESIS: Network Micro-Dynamics in Emerging Technologies)
First, the project has provided an element of conceptual clarity to notion of an ‘emerging technology’. Despite growing interest around the phenomenon of emerging technologies, there is a lack of consensus on what characterises a technology as ’emergent’. For this reason, a definition of emerging technology was developed by combining a basic understanding of the concept of ’emergence’ with a review of key innovation studies dealing with definitional issues of technological emergence. The definition is reported in the following.
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, Hicks, & Martin, 2015, p. 1828)
As a result, five attributes of emerging technologies were identified: (i) radical novelty, (ii) relatively fast growth, (iii) coherence, (iv) prominent impact, and (v) uncertainty and ambiguity. On the basis of these attributes, a framework that links what are conceptualised as ‘emerging technologies’ with their measurement was developed. To do so, major empirical approaches (developed by scholars mainly in, although not limited to, the scientometric domain) for the detection and study of emerging technologies were reviewed and integrated in the framework (see Rotolo et al., 2015).
Second, a data processing tool, namely ‘medlineR’, was developed to aid the study of emerging technologies (Rotolo & Leydesdorff, 2015). This tool specifically combines the bibliographic information of MEDLINE/PubMed records with ISI Web of Science (WoS) data by performing a match between these two databases. This, in turn, enables one to exploit the vast Medical Subject Heading classification (MeSH) used by MEDLINE/PubMed to classify publications on the basis of their content along with the complete information on authors’ affiliational addresses provided by WoS. The latter data are of particular relevance for generating longitudinal collaborative (co-authorship) networks of institutions and authors. It is worth noting that medlineR is a routine written within the R-statistics environment and it is freely available (with a video tutorial illustrating its use) at www.danielerotolo.com/medliner.
Third, a Triple Helix model based on co-occurrence networks of terms was developed to study emerging technologies and technological change in the medical domain (Petersen, Rotolo, & Leydesdorff, 2016). The model allows one to trace the interplay along three key dimensions of the emergence process: (i) demand of and (ii) supply for technological innovation, and (iii) technological capabilities available to generate these in the form of new products, processes, and services. An indicator of the reduction in uncertainty along these three dimensions over the emergence process was developed on the basis of entropy statistics defined by using the MeSH classification of MEDLINE/PubMed.
A mix of recent and historical case-studies of emerging technologies was then examined: (i) human papilloma virus (including molecular biology-based and visual inspection diagnostic technologies for cervical cancer), (ii) RNA interference (including both diagnostic and therapeutic applications), and (iii) magnetic resonance imaging. The statistical analysis provided evidence of periods of strong synergy among demand, supply, and technological capabilities, thus suggesting the importance of examining these three-dimensional interactions for both the understanding and the governance of uncertainty associated with technological emergence (especially in the medical context).
Fourth, the project has examined the use and application of funding data reported in the acknowledgment sections of scientific publications to generate a funding landscape that can capture the networks of public and private funders that may support emerging technologies (Grassano, Rotolo, Hutton, Lang, & Hopkins, 2016). To do so, heuristics for extracting funding data from the paratext of acknowledgement sections were developed and a systematic comparison (in terms of precision, recall, and accuracy) between manually extracted, WoS and MEDLINE/PubMed funding data was undertaken. This provided evidence that manually extracted funding data tend to be of a better ‘quality’ then those provided by other publication databases.
Funding data are particularly promising for studying emerging technologies for a number of reasons: (i) the proportion of public funding in relation to private investment can provide some indication of the levels of uncertainty and ambiguity associated with a given emerging technology; (ii) longitudinal funding data may be used to generate a more timely indicator of relatively fast growth, thus overcoming the time-lag between actual emergence and emergence detected in publications and patents; and (iii) relatively large amounts of funding for a given emerging technology may suggest that prominent impact is expected from that technology.
Finally, the project has examined the emergence process from an inter-organisational network perspective. One study was undertaken to increase understanding of how different institutional groups of actors (research and higher education, governmental, hospital and care, industrial, and non-governmental organisations) contribute to the emergence process by examining the case of diagnostic technologies for cervical cancer (Rotolo, Hopkins, Rafols, & Hogarth, 2016). Co-authorship data of scientific articles published in the field were used to generate a longitudinal inter-organisational network. This was then examined, over the emergence process, in terms of formation of ties (dyads) within and between institutional groups as well as in terms of brokerage positions (triads) occupied by different institutional groups. The findings provided evidence that the processes of tie formation and brokerage activity follow different patterns over the emergence process and depending on the institutional group considered: (i) institutional groups (except research and higher education organisations) form predominantly intergroup ties; (ii) ties with hospital and care organizations in the early phase of emergence are frequent for most groups (especially for research and higher education organizations and industrial actors); (iii) institutional groups perform the full variety of brokering roles (i.e. coordinator, gatekeeper, itinerant broker, liaison); (iv) the predominant forms of brokering roles played by different institutional groups are relatively stable during the emergence process, with non-governmental and industrial actors acting as liaison-type brokers. A second study has instead developed an Exponential Random Graph Model to test for different network dynamics featuring in the emergence of ‘microneedles’ technologies (Rotolo, 2016). Evidence of the extent to which the likelihood of forming, maintaining, or terminating ties among actors (authors or affiliations) is affected by actor covariates such as type of organisations, diversity/specialisation of the research undertaken, and status has been provided.
In summary, the project has increased our understanding of the emerging process and the network dynamics featuring in the emergence process as well as providing a tool for examining emerging technologies from a network perspective. These, in turn, have the potential to generate important policy implications for the governance of emerging technologies. In particular, they can be used by policymakers to generate knowledge and intelligence on different cases of emerging technologies to understand how networks surrounding emerging technologies are likely to form, evolve, and disappear over the emergence process (e.g. how actors enter and exit the network, which actors are more likely to shape the network structure, and how the network structure is likely evolve with the involvement of certain actors). This knowledge may feed into the policymaking process for the design of policy mechanisms favouring the emergence of networks that support the plurality and diversity of technological options generated in key industries such as biotechnology and pharmaceuticals.
- Grassano, N., Rotolo, D., Hutton, J., Lang, F., & Hopkins, M. M. (2016). Funding data from publication acknowledgements: Coverage, uses and limitations. Journal of the American for Information Science and Technology, in press.
- Petersen, A. M., 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. http://doi.org/10.1016/j.respol.2015.12.004
- Rotolo, D. (2016). Networks dynamics in the case of emerging technologies. Work-in-progress: SPRU, University of Sussex, Brighton, United Kingdom.
- Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology? Research Policy, 44(10), 1827–1843. http://doi.org/10.1016/j.respol.2015.06.006
- Rotolo, D., Hopkins, M. M., Rafols, I., & Hogarth, S. (2016). The emergence of molecular biology in the diagnosis of cervical cancer: A network perspective. Work-in-progress: SPRU, University of Sussex, Brighton, United Kingdom.
- Rotolo, D., & Leydesdorff, L. (2015). Matching MEDLINE/PubMed data with Web of Science (WoS): A routine in R-language. Journal of the American Society for Information Science and Technology, 66(10), 2155–2159. http://doi.org/10.1002/asi.23385