Technological innovation is a dynamic process that spans the life cycle of an idea, from scientific research to production. Within this process, there are often a few key innovations that significantly impact a technology’s development, and the ability to identify and trace the development of these key innovations comes with a great payoff for researchers and technology managers. In this article, we present a framework for identifying the technology’s main evolutionary pathway. What is unique about this framework is that we introduce new indicators that reflect the connectivity and the modularity in the interior citation network to distinguish between the stages of a technology’s development. We also show how information about a family of patents can be used to build a comprehensive patent citation network. Finally, we apply integrated approaches of main path analysis (MPA)—namely global MPA and global key-route main analysis—for extracting technological trajectories at different technological stages. We illustrate this approach with dye-sensitized solar cells (DSSCs), a low-cost solar cell belonging to the group of thin-film solar cells, contributing to the remarkable growth in the renewable energy industry. The results show how this approach can trace the main development trajectory of a research field and distinguish key technologies to help decision makers manage the technological stages of their innovation processes more effectively.
Author(s): Ying Huang, Fujin Zhu, Alan L. Porter, Yi Zhang, Donghua Zhu, Ying Guo
Organization(s): Wuhan University, Beijing Institute of Technology, Search Technology, University of Technology Sydney, China University of Political Science and Law
Source: IEEE Transactions on Engineering Management
Detecting promising technology groups for recombination holds the promise of great value for R&D managers and technology policymakers, especially if the technologies in question can be detected before they have been combined. However, predicting the future is always easier said than done. In this regard, Arthur’s theory (The nature of technology: what it is and how it evolves, Free Press, New York, 2009) on the nature of technologies and how science evolves, coupled with Kuhn’s theory of scientific revolutions (Kuhn in The structure of scientific revolutions, 1st edn, University of Chicago Press, Chicago, p 3, 1962), may serve as the basis of a shrewd methodological framework for forecasting recombinative innovation. These theories help us to set out quantifiable criteria and decomposable steps to identify research patterns at each stage of a scientific revolution. The first step in the framework is to construct a conceptual model of the target technology domain, which helps to refine a reasonable search strategy. With the model built, the landscape of a field—its communities, its technologies, and their interactions—is fleshed out through community detection and network analysis based on a set of quantifiable criteria. The aim is to map normal patterns of research in the domain under study so as to highlight which technologies might contribute to a structural deepening of technological recombinations. Probability analysis helps to detect and group candidate technologies for possible recombination and further manual analysis by experts. To demonstrate how the framework works in practice, we conducted an empirical study on AI research in China. We explored the development potential of recombinative technologies by zooming in on the top patent assignees in the field and their innovations. In conjunction with expert analysis, the results reveal the cooperative and competitive relationships among these technology holders and opportunities for future innovation through technological recombinations.
Author(s): Xiao Zhou, Lu Huang, Yi Zhang, Miaomiao Yu
Organization(s): Xidian University, Beijing Institute of Technology
Accurately evaluating opportunities in new and emerging science and technologies is a growing concern. This study proposes an integrated framework for identifying a range of potential innovation pathways and commercial applications for solid lipid nanoparticles – one particularly promising contender within the field of nano-enabled drug delivery. Several text mining techniques – term clumping, SAO technique, and net effect analysis – as well as technology roadmapping, are combined with expert judgment to identify the main areas of R&D in this field, and to track their evolution over time. Through analysis, data from multiple sources, including research publications, patents, and commercial press, reveal possible future applications and commercialization opportunities for this emerging technology. We find that research is moving away from materials and delivery outcomes toward clinical applications. The most promising markets are pharmaceuticals and cosmetics; however, the “time-to-market” is much shorter for cosmetics than it is for pharmaceuticals.
The most significant contributions of this paper have been highlighted as follows. One innovation is extracting the intelligence from three kinds of data sources after in-depth considering their characteristics and matching with the features of different technology development stages to identify innovative research topics. The second one is combining SAO technique with net effect analysis to identify what the evolutionary links between research topics are, and then to use TRM to visualize the evolution of the main areas of R&D over time.
Author(s):Xiao Zhou, Lu Huang, Alan Porter, Jose M.Vicente-Gomila
Organization(s): Xidian University, Beijing Institute or Technology
Source: Technological Forecasting and Social Change
Whereas traditional science maps emphasize citation statistics and static relationships, this paper presents a term-based method to identify and visualize the evolutionary pathways of scientific topics in a series of time slices. First, we create a data preprocessing model for accurate term cleaning, consolidating, and clustering. Then we construct a simulated data streaming function and introduce a learning process to train a relationship identification function to adapt to changing environments in real time, where relationships of topic evolution, fusion, death, and novelty are identified. The main result of the method is a map of scientific evolutionary pathways. The visual routines provide a way to indicate the interactions among scientific subjects and a version in a series of time slices helps further illustrate such evolutionary pathways in detail. The detailed outline offers sufficient statistical information to delve into scientific topics and routines and then helps address meaningful insights with the assistance of expert knowledge. This empirical study focuses on scientific proposals granted by the United States National Science Foundation, and demonstrates the feasibility and reliability. Our method could be widely applied to a range of science, technology, and innovation policy research, and offer insight into the evolutionary pathways of scientific activities.
Author(s): Yi Zhang, Guangquan Zhang, Donghua Zhu, Jie Lu
Organization(s): University of Technology Sydney, Beijing Institute of Technology
Source: Journal of the Association for Information Science and Technology
In this study, the evolution of the connected health concept is analysed and visualized to investigate the ever-tightening relationship between health and technology as well as emerging possibilities regarding delivery of healthcare services. A scientometric analysis was undertaken to investigate the trends and evolutionary relations between health and information systems through the queries in the Web of Science database using terms related to health and information systems. To understand the evolutionary relation between different concepts, scientometric analyses were conducted within five-year intervals using the VantagePoint, SciMAT, and CiteSpace II software. Consequently, the main stream of publications related to the connected health concept matching telemedicine cluster was determined. All other developments in health and technologies were discussed around this main stream across years. The trends obtained through the analysis provide insights about the future of healthcare and technology relationship particularly with rising importance of privacy, personalized care along with mobile networks and mobile infrastructure.
Author(s): Serhat Burmaoglu, Ozcan Saritas, Levent Bekir, Kıdak, and İpek Camuz Berber
Organization(s): Izmir Katip Celebi University, National Research University Higher School of Economics
The understanding of emerging technologies and the analysis of their development pose a great challenge for decision makers, as being able to assess and forecast technological change enables them to make the most of it. There is a whole field of research focused on this area, called technology forecasting, in which bibliometrics plays an important role. Within that framework, this paper presents a forecasting approach focused on a specific field of technology forecasting: research activity related to an emerging technology. This approach is based on four research fields—bibliometrics, text mining, time series modelling and time series forecasting—and is structured in five interlinked steps that generate a continuous flow of information. The main milestone is the generation of time series that measure the level of research activity and can be used for forecasting. The usefulness of this approach is shown by applying it to an emerging technology: cloud computing. The results enable the technology to be structured into five main sub-technologies which are characterised through five time series. Time series analysis of the trends related to each sub-technology shows that Privacy and Security has been the most active sub-technology to date in this area and is expected to maintain its level of interest in the near future.
Author(s): Iñaki Bildosola, Pilar Gonzalez, Paz Moral
Organization(s): University of the Basque Country (UPV/EHU)
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)
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
Anthropic methane emissions can largely be prevented or minimized using
technologies that are already available. One such technology is anaerobic digestion
(AD), which is used commercially around the world, especially in Europe
and the United States, where some challenging targets have been set to
diversify the energy mix with more renewable energy. This foresight study was
designed to identify which technological solutions out of the many options
available for biogas production are attracting most interest, for which purpose
patent documents and scientific publications were analyzed. The aim is to
identify which raw materials are most attractive for AD and biogas production.
It was found that the raw materials that have attracted most research and
patenting activity are sludge, sewage, and wastewater, livestock waste, and
agriculture waste, which together account for 62% of all the patents filed and
74% of all the scientific publications. The countries most engaged in producing
biogas from AD plants are China, Germany, and the United States. We
also identified a rising trend in the use of biogas around the world, and a
steady increase in the number of patents filed on the subject, especially in Japan
and South Korea. This growth is driven, amongst other things, by strategic
governmental actions, global environmental pacts, and the realization on
the part of industry that anaerobic digestion can be used as an efficient method
for treating waste and effluents.
For FULL-TEXT go to http://file.scirp.org/pdf/JEP_2017021016142340.pdf
Author(s): Rafaela Lora Grando, Fabiana Valeria da Fonseca, Adelaide Maria de Souza Antunes
Oganization(s): Universidade Federal do Rio de Janeiro, Instituto Nacional de Propriedade Industrial (INPI)
Source: Journal of Environmental Protection
Whether it be for countries to improve the ability to undertake independent innovation or for enterprises to enhance their international competitiveness, tracing historical progression and forecasting future trends of technology evolution is essential for formulating technology strategies and policies. In this paper, we apply co-classification analysis to reveal the technical evolution process of a certain technical field, use co-word analysis to extract implicit or unknown patterns and topics, and employ main path analysis to discover significant clues about technology hotspots and development prospects. We illustrate this hybrid approach with 3D printing, referring to various technologies and processes used to synthesize a three-dimensional object. Results show how our method offers technical insights and traces technology evolution pathways, and then helps decision-makers guide technology development.
Author(s): Ying Huang, Donghua Zhu, Yue Qian, Yi Zhang, Alan L. Porter, Yuqin Liu, Ying Guo
Organization(s): Beijing Institute of Technology, Georgia Institute of Technology