Technological Convergence (TC) reflects developmental processes that overlap different technological fields. It holds promise to yield outcomes that exceed the sum of its subparts. Measuring emergence for a TC environment can inform innovation management. This paper suggests a novel approach to identify Emergent Topics (ETopics) of the TC environment within a target technology domain using patent information. A non-TC environment is constructed as a comparison group. First, TC is operationalized as a co-classification of a given patent into multiple 4-digit IPC codes (≥2-IPC). We take a set of patents and parse those into three sub-datasets based on the number of IPC codes assigned 1-IPC (Non-TC), 2-IPC and ≥3-IPC. Second, a method is applied to identify emergent terms (ETs) and calculate emergence score for each term in each sub-dataset. Finally, we cluster those ETs using Principal Components Analysis (PCA) to generate a factor map with ETopics. A convergent domain – 3D printing – is selected to present the illustrative results. Results affirm that for 3D printing, emergent topics in TC patents are distinctly different from those in non-TC patents. The number of ETs in the TC environment is increasing annually.
Author(s): Zhinan Wang, Alan L.Porter, Xuefeng Wang, Stephen Carley
Organization(s): Beijing Institute of Technology, Georgia Institute of Technology
Source: Technological Forecasting and Social Change
Current enterprises face organizational and cultural barriers to adopt and harness the potential of strategic emerging technologies. Late adoption of these technologies will affect competitiveness from which it will be hard to recover. Within the frame of technology analysis field, the present work aims at introducing an approach to obtain the characterization of emerging technologies, which facilitates understanding and identifies their potential. This characterization is based on the analysis of scientific activity, to which a set of quantitative methods is applied, namely bibliometrics, text mining, principal component analysis and time series analysis. The outcome is based on obtaining a set of dominant sub-technologies, which are described by means of individual time series, which also allow evolution of the technology as a whole to be forecasted. The approach is applied to the Big Data technology field and the results suggest that sub-technologies such as Mobile Telecommunications and Internet of things will lead this field in the near future.
Author(s): Iñaki Bildosola, Gaizka Garechana Enara Zarrabeitia, Ernesto Cilleruelo
Organizations(s): University of the Basque Country (UPV/EHU)
Source: Central European Journal of Operations Research
As one of the most impactful emerging technologies, big data analytics and its related applications are powering the development of information technologies and are significantly shaping thinking and behavior in today’s interconnected world. Exploring the technological evolution of big data research is an effective way to enhance technology management and create value for research and development strategies for both government and industry. This paper uses a learning-enhanced bibliometric study to discover interactions in big data research by detecting and visualizing its evolutionary pathways. Concentrating on a set of 5840 articles derived from Web of Science covering the period between 2000 and 2015, text mining and bibliometric techniques are combined to profile the hotspots in big data research and its core constituents. A learning process is used to enhance the ability to identify the interactive relationships between topics in sequential time slices, revealing technological evolution and death. The outputs include a landscape of interactions within big data research from 2000 to 2015 with a detailed map of the evolutionary pathways of specific technologies. Empirical insights for related studies in science policy, innovation management, and entrepreneurship are also provided.
Author(s): Yi Zhang, Ying Huang, Alan L. Porter, Guangquan Zhang, Jie Lu
Organization(s): University of Technology Sydney, Hunan University
Source: Technological Forecasting and Social Change
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.
For full-text view, http://rdcu.be/qfTB
Author(s): Stephen F. Carley, Nils C. Newman, Alan L. Porter, Jon G. Garner
Organization(s): Georgia Tech, Search Technology
For researchers and decision makers in any technical domain, understanding the state of their area of interest is of critical importance. This “landscape‟ of emerging technologies is constantly evolving, and the sheer scale of research publication output in the modern era makes qualitative review increasingly difficult. Scientometric analysis is a valuable tool for the quantitative analysis of research output, and is employed by the Defence Science and Technology Laboratory (Dstl) Knowledge and Information Services in support of our research activities, for applications including identifying opportunities for academic collaboration, and technology watching/forecasting to identify emerging technologies and opportunities that may have implications for UK Defence. This paper provides an overview of our approach to conducting scientometric analysis of research papers and patent submissions. The methods for extracting and disambiguating publications are described, and the qualitative inferences we seek to make, along with some of the associated limitations and potential pitfalls are also discussed.
For FULL-TEXT see http://www.qqml.net/papers/March_2016_Issue/511QQML_Journal_2016_I_Anson_1-10.pdf
Author(s): Ian I’Anson
Organization: Defence Science and Technology Laboratory
Source: Qualitative and Quantitative Methods in Libraries