Tag Archives: Technical Emergence

From Research to Industry: A Quantitative and Qualitative Analysis of Science-Technology Transferences and Emergence Patterns in Bioremediation

This article uses text mining techniques to determine the time lag of knowledge transfer between research activity and technology development in bioremediation, complementing these with advanced visualization techniques in order to extract patterns that could be of interest for decision making in this field. The emergence patterns in this field have been identified and a method based on subject-action-object (SAO) semantic structure is proposed for characterizing such patterns, using 2-word tuples. Our results show that technology developments in heavy metal bioremediation swiftly follow scientific advances, as opposed to developments in bioremediation of organic chemical components. The science mapping reveals three distinct areas: 1) heavy metal remediation and phytoremediation; 2) aerobic and anaerobic remediation of chemical elements; and 3) bioremediation techniques for treating specific contamination sources such as oil. The emergence analysis points at activities involving energy recovery by bioremediation, and shows an increasing amount of technologies involving specific strains of microorganisms, which could gain significant traction in this field in an estimated time horizon of ten years. Our SAO approach, tested on the data sample corresponding to these strains, proves to be useful for characterizing the emerging technologies when applied to instrumental concepts.

10.1109/TEM.2019.2936364

Author(s): Gaizka Garechana, Rosa Rio-Belver, Enara Zarrabeitia,  Izaskun Alvarez-Meaza

Organization(s): University of the Basque Country

Source: IEEE Transactions on Engineering Management

Year: 2019

Evaluating technological emergence using text analytics: two case technologies and three approaches (FULL-TEXT)

Scientometric methods have long been used to identify technological trajectories, but we have seldom seen reproducible methods that allow for the identification of a technological emergence in a set of documents. This study evaluates the use of three different reproducible approaches for identifying the emergence of technological novelties in scientific publications. The selected approaches are term counting technique, the emergence score (EScore) and Latent Dirichlet Allocation (LDA). We found that the methods provide somewhat distinct perspectives on technological. The term count based method identifies detailed emergence patterns. EScore is a complex bibliometric indicator that provides a holistic view of emergence by considering several parameters, namely term frequency, size, and origin of the research community. LDA traces emergence at the thematic level and provides insights on the linkages between emerging research topics. The results suggest that term counting produces results practical for operational purposes, while LDA offers insight at a strategic level.

For FULL-TEXT https://doi.org/10.1007/s11192-019-03275-w

Author(s): Samira Ranaei, Arho Suominen, Alan Porter, Stephen Carley

Organization(s): VTT Technical Research Centre of Finland, Lappeenranta University of Technology, Search Technology

Source: Scientometrics

Year: 2019

Research addressing emerging technological ideas has greater scientific impact

This study empirically examines the association between the extent of emerging technological ideas in a scientific publication and its future scientific impact measured by number of citations. We analyze metadata of scientific publications in three scientific domains: Nano-Enabled Drug Delivery, Synthetic Biology, and Autonomous Vehicles. By employing a bibliometric indicator for identifying and quantifying emerging technological ideas – as derived terms from the titles and abstracts – we measure the extent to which the publication contains emerging technological ideas in each domain. Then, we statistically estimate the size and statistical significance of the relationship between the publication-level technological emergence score and the normalized number of citations accruing to the publication.

Our analysis shows that the degree to which a paper contains technologically emerging ideas is positively and strongly associated with its future citation impact in each of the three domains. An additional analysis demonstrates that this relationship holds for citations from other publications, both in the same field as, and in different fields from, the scientific domain of the focal publication. A series of tests for validation further support our argument that the greater the extent to which scientific knowledge (a paper) contains emerging ideas, the bigger its scientific impact. Implications for academic researchers, research policymakers, and firms are discussed.

Free download available through September 14, 2019 at https://authors.elsevier.com/a/1ZS8SB5ASBuce

Otherwise, https://doi.org/10.1016/j.respol.2019.103834

Author(s): Seokbeom Kwon, Xiaoyu Liu, Alan L. Porter, Jan Youtie
Organization(s): Georgia Institute of Technology, Beijing Institute of Technology
Source: Research Policy
Year: 2019

Emergence scoring to identify frontier R&D topics and key players

Indicators of technological emergence promise valuable intelligence to those determining R&D priorities. We present an implemented algorithm to calculate emergence scores for topical terms from abstract record sets. We offer a family of emergence indicators deriving from those scores. Primary emergence indicators identify “hot topic” terms. We then use those to generate secondary indicators that reflect organizations, countries, or authors especially active at frontiers in a target R&D domain. We also flag abstract records (papers or patents) rich in emergent technology content, and we score research fields on relative degree of emergence. This paper presents illustrative results for example topics – Nano-Enabled Drug Delivery, Non-Linear Programming, Dye Sensitized Solar Cells, and Big Data.

https://doi.org/10.1016/j.techfore.2018.04.016

Author(s): Alan L. Porter, Jon Garner, Stephen F. Carley, Nils C. Newman
Organization: Georgia Institute of Technology
Source: Technological Forecasting and Social Change
Year: 2018

Tracking the emergence of synthetic biology (full-text)

Synthetic biology is an emerging domain that combines biological and engineering concepts and which has seen rapid growth in research, innovation, and policy interest in recent years. This paper contributes to efforts to delineate this emerging domain by presenting a newly constructed bibliometric definition of synthetic biology. Our approach is dimensioned from a core set of papers in synthetic biology, using procedures to obtain benchmark synthetic biology publication records, extract keywords from these benchmark records, and refine the keywords, supplemented with articles published in dedicated synthetic biology journals. We compare our search strategy with other recent bibliometric approaches to define synthetic biology, using a common source of publication data for the period from 2000 to 2015. The paper details the rapid growth and international spread of research in synthetic biology in recent years, demonstrates that diverse research disciplines are contributing to the multidisciplinary development of synthetic biology research, and visualizes this by profiling synthetic biology research on the map of science. We further show the roles of a relatively concentrated set of research sponsors in funding the growth and trajectories of synthetic biology. In addition to discussing these analyses, the paper notes limitations and suggests lines for further work.

Full-text available at https://link.springer.com/article/10.1007/s11192-017-2452-5

Author(s): Philip Shapira, Seokbeom Kwon, Jan Youtie
Organization(s): University of Manchester, Georgia Institute of Technology
Source: Scientometrics
Year: 2017

A measure of staying power: Is the persistence of emergent concepts more significantly influenced by technical domain or scale? (full-text)

This study advances a four-part indicator for technical emergence. While doing so it focuses on a particular class of emergent concepts—those which display the ability to repeatedly maintain an emergent status over multiple time periods. The authors refer to this quality as staying power and argue that those concepts which maintain this ability are deserving of greater attention. The case study we consider consists of 15 subdatatsets within the dye-sensitized solar cell framework. In this study the authors consider the impact technical domain and scale have on the behavior of persistently emergent concepts and test which of these has a greater influence.

https://link.springer.com/article/10.1007/s11192-017-2342-x

Full-text avalaible via ResearchGate
https://www.researchgate.net/publication/315462977_A_measure_of_staying_power_Is_the_persistence_of_emergent_concepts_more_significantly_influenced_by_technical_domain_or_scale

Author(s): Stephen F. Carley, Nils C. Newman, Alan L. Porter, Jon G. Garner
Organization(s): Georgia Institute of Technology, Search Technology
Source: Scientometrics
Year: 2017

A Systematic Method for Technology Assessment: Illustrated for Big Data Analytics (full-text)

Historically, Technology Assessment (TA) refers to studying the societal effects of the development and application of a technology. A key challenge for modern TA is to assess emerging technology fields as they are emerging – this is crucial for producing actionable strategic intelligence for use in decision-making.  To contribute to addressing this challenge, the aim of this research is to advance methods to generate effective technology assessment intelligence, and to showcase the approach with an application to the rapidly evolving field of “Big Data.”  The key contributions of this paper are twofold: 1) Methodological: To advance the Forecasting Innovation Pathway (FIP) methodology to identify potential impacts of an emerging technology, and to gauge their likelihood and magnitude of importance for further study; 2) Substantive: To estimate the likelihood and importance of potential impacts of big data analytics (BDA) more broadly, and to help inform U.S. policy considerations in particular.

Full-text of presentation

 

Author(s): Ying Guo, Jianhua Liu, Alan L. Porter
Organization(s): Beijing Institute of Technology, Chinese Academy of Science, Georgia Institute of Technology
Source: Annual Conference on Big Data and Business Analytics (Shanghai, China)
Year: 2017