Identifying emerging technologies has been of long-standing interest to many scholars and practitioners. Previous studies have introduced methods to capture the concept of emergence from bibliographic records, including the recently proposed Technology Emergence Indicator (Carley et al. 2018). This indicator method has shown to be applicable to various technological fields. However, the indicator uses a limited time window, which can overlook the potential long-term evolution of emerging technologies. Moreover, the existing method suffers from interpretability, because it can be difficult to understand the context in which identified emerging terms are used. In this paper, we propose an improved version of the Technology Emergence Indicator that addresses these issues. In doing so, we examine emerging topics within the field of autonomous vehicles technologies during the period of 1991-2018, guided by a proposition about the long-term diffusion of an emerging technology topic. The results show that different autonomous vehicle technology topics emerge during each of the three 10-year periods under analysis, including an initial period of understanding the surrounding environment and path planning, a second period marked by DARPA Grand Challenge motivated factors associated with the urban environment and communication technologies, and a third period relating to machine learning and object detection. This association with certain emerging technology topics in each decade is also characterized by different trajectories of continued or cyclical carryover across the decades. The results suggest a methodology that practitioners can use in examining research areas to understand which topics are likely to persist into the future.
Author(s): Seokkyun Woo, Jan Youtie,Ingrid Ott, Fenja Scheu
Organization(s): Georgia Institute of Technology, Karlsruhe Institute of Technology
Source: Technological Forecasting and Social Change
This article puts forth a new indicator of emerging technological topics as a tool for addressing challenges inherent in the evaluation of interdisciplinary research. We present this indicator and test its relationship with interdisciplinary and atypical research combinations. We perform this test by using metadata of scientific publications in three domains with different interdisciplinarity challenges: Nano-Enabled Drug Delivery, Synthetic Biology, and Autonomous Vehicles. Our analysis supports the connection between technological emergence and interdisciplinarity and atypicality in knowledge combinations. We further find that the contributions of interdisciplinary and atypical knowledge combinations to addressing emerging technological topics increase or stay constant over time. Implications for policymakers and contributions to the literature on interdisciplinarity and evaluation are provided.
Author(s): Seokbeom Kwon, Jan Youtie, and Alan L. Porter
Organization(s): The University of Tokyo, Georgia Institute of Technology
Source: Research Evaluation
- Thirteen teams strive to distinguish emerging research topics in synthetic biology.
- Analyses of ten years of article abstracts predict topics in the next two years.
- Augmenting, consolidating, embedding, and clustering text help detect emergence.
- Analyses of citation patterns and research networking also help discern emergence.
We conducted a contest to predict highly active research topics. Participants analyzed ten years of Web of Science abstract records in a target technological domain (synthetic biology) so as to indicate cutting edge sub-topics likely to be actively pursued in the following two years. We describe contest procedures and results provided by thirteen participating teams.
Contestants used various topical and other fields in the abstract records; some augmented with external data. They applied at least 19 diverse methods in deriving emerging topics predicted to be actively researched in the coming two years. Besides topical text analyses, contestants variously brought to bear both backward and forward citation analyses, and network analyses, to help identify topics apt to be highly researched in the near future. This communal exercise on forecasting near-future research activity using a wide array of text analytic and other bibliometric tools provides a stimulating resource.
Author(s): Alan L. Porter, Denise Chiavetta, Nils C. Newman
Organization(s): Search Technology, Inc.
Source: Technological Forecasting and Social Change
Technology emergence has become a hot topic in R&D policy and management communities. Various methods of measuring technology emergence have been developed. However, there is little literature discussing how to evaluate the results identified by different methods. This research sharpens a promising Technology Emergence Indicator (TEI) set by assessing alternative formulations on three distinct datasets: Dye-Sensitized Solar Cells, Non-Linear Programming, and Nano-Enabled Drug Delivery. Our TEIs derive from a conceptual foundation including three attributes of emergence: persistence, community, and growth that we systematically address through a 3-dimensional evaluation framework. Comparing TEI behavior through sensitivity analyses shows good robustness for the measures. The TEI serve to distinguish emerging R&D topics in the field under study. They can further be used to identify highly active players publishing on those topics. Importantly, results show that identified emerging terms and topics persist to a strong degree; thus, they serve to predict highly active R&D foci within the technical domain under study.
DOI: https://doi.org/10.1007/s11192-020-03432-6 432-6
Author(s): Xiaoyu Liu, Alan L. Porter
Organization(s): Beijing Institute of Technology, Search Technology
A new bibliometric technique enables one to distinguish high emergence topical content. This technique can be applied to sets of research publication abstracts reflecting a given technical domain (here, nanotechnology) to score cutting edge research terms. The resulting high emergence terms warrant special consideration in setting R&D priorities. The researchers (individuals, organizations, or countries) whose publications address those emergent terms heavily deserve consideration as possible leaders in that technical domain. This paper studies nanotechnology research publications using the new emergence scoring in conjunction with established bibliometric publication and citation measures. Findings challenge U.S. superiority in cutting edge nanotechnology research. China shows strongest at addressing emergent nanotechnology topics, followed by the U.S., South Korea, India, and, surprisingly, Iran.
Author(s): Alan L. Porter, Jon Garner, Nils C. Newman, Stephen F. Carley, Jan Youtie, Seokbeom Kwon, Yin Li
Organization(s): Search Technology, Georgia Institute of Technology, Fudan University
Source: Technology Analysis & Strategic Management
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.
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
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
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.
Author(s): Seokbeom Kwon, Xiaoyu Liu, Alan L. Porter, Jan Youtie
Organization(s): Georgia Institute of Technology, Beijing Institute of Technology
Source: Research Policy
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.
Author(s): Alan L. Porter, Jon Garner, Stephen F. Carley, Nils C. Newman
Organization: Georgia Institute of Technology
Source: Technological Forecasting and Social Change