Category Archives: Future-oriented technology analysis

Measuring tech emergence: A contest (FULL-TEXT)

Access to full-text of this paper is available through August 20, 2020 at https://authors.elsevier.com/a/1bKYd98SGmQ4B

Highlights

  • 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
Year: 2020

Exploring Technology Evolution Pathways to Facilitate Technology Management: From a Technology Life Cycle Perspective

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.

For source 10.1109/TEM.2020.2966171

Author(s): Ying Huang, Fujin Zhu, Alan L. Porter, Yi Zhang, Donghua Zhu, Ying Guo
Organization(s): Wuhan University, Beijing Institute of Technology, University of Technology Sydney, China University of Political Science and Law
Source: IEEE Transactions on Engineering Management
Year: 2020

Foresight through Strategic Technology Intelligence for Collaboration and Innovation Pathways (FULL-TEXT)

Formulating a strategic direction for long-term growth is critical to address the challenges of technological competitiveness in the globalisation era. This article presents a case study of the technology of shelf life extension for agricultural foods. We propose strategic technology intelligence (STI) as an approach that combines quantitative tools with a qualitative technique using technological experts to judge and strengthen the findings. Cases from the patents database were extracted for analysis using the text mining software. The results illustrate that this particular technology has potential for growth and related research has been increasing. In addition, collaboration among researchers and organisations is essential to foster the speed of R&D and boost knowledge exchange. The findings can be useful for policymakers, managers, and researchers as a decision-making tool for further implementation and execution.

For FULL-TEXT click HERE

Author(s): Jakkrit Thavorn, Nongnuj Muangsin, Chupun Gowanit, Veera Muangsin
Organization: Chulalongkorn University
Source: Proceedings: 2020 ISPIM Connects Bangkok – Partnering for an Innovative Community
Year: 2020

Exploring Technology Evolution Pathways to Facilitate Technology Management: From a Technology Life Cycle Perspective

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.

10.1109/TEM.2020.2966171

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
Year: 2020

A hybrid approach to detecting technological recombination based on text mining and patent network analysis

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.

https://doi.org/10.1007/s11192-019-03218-5

Author(s): Xiao Zhou, Lu Huang, Yi Zhang, Miaomiao Yu
Organization(s): Xidian University, Beijing Institute of Technology
Source: Scientometrics
Year: 2019

Tracing the system transformations and innovation pathways of an emerging technology: Solid lipid nanoparticles

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.

https://doi.org/10.1016/j.techfore.2018.04.026

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
Year: 2018

Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics

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.

http://onlinelibrary.wiley.com/doi/10.1002/asi.23814/full

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
Year: 2017

Evolution of connected health: a network perspective

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.

https://link.springer.com/article/10.1007/s11192-017-2431-x

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
Source: Scientometrics
Year: 2017

An approach for modelling and forecasting research activity related to an emerging technology

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.

https://link.springer.com/article/10.1007/s11192-017-2381-3

Author(s): Iñaki Bildosola, Pilar Gonzalez, Paz Moral
Organization(s): University of the Basque Country (UPV/EHU)
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