Category Archives: Future-oriented technology analysis

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

Scientometric analysis of the emerging technology landscape (full-text)

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
Year: 2016

Mapping of the Use of Waste as Raw Materials for Biogas Production (full-text)

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
Year: 2017

A hybrid method to trace technology evolution pathways: a case study of 3D printing

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.

http://link.springer.com/article/10.1007/s11192-017-2271-8

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
Source: Scientometrics
Year: 2017

Forecasting Cloud Computing: Producing a Technological Profile

Migrating to cloud computing is one of the current enterprise challenges. In this sense, the small and medium enterprise should be the most interested, given that initial investments are avoided and the technology offers gradual implementation. However, 54.9 % of SMEs confess that they have no knowledge of cloud technology. Accordingly, this paper aims at generating a relevant profile of cloud computing technology, as the first part of a novel approach based on four families of technological forecasting methods to gather and structure information concerning an emerging technology, generating a relevant profile, identifying its past development, forecasting the short and medium-term evolution and integrating all of the elements graphically into a hybrid roadmap. The outcome of the approach will raise the awareness of such technology as well as facilitate its implementation.

http://link.springer.com/chapter/10.1007/978-3-319-45748-2_3

Author(s): Iñaki Bildosola, Rosa Rio-Bélver, Ernesto Cilleruelo, Javier Gavilanes
Organization(s): University of the Basque Country UPV/EHU
Source: Engineering Systems and Networks:The Way Ahead for Industrial Engineering and Operations Management (Springer International Publishing)
Year: 2017

International Collaboration Patterns and Effecting Factors of Emerging Technologies

With the globalization of the world economy, international innovation collaboration has taken place all over the world. This study selects three emerging technologies (3D printing, big data and carbon nanotubes and graphene technology) among 20 countries as the research objects, using three patent-based indicators and network relationship analysis to reflect international collaboration patterns. Then we integrate empirical analyses to show effecting factors of international collaboration degrees by using panel data. The results indicate that while 3D printing technology is associated with a “balanced collaboration” mode, big data technology is more accurately described by a radial pattern, centered on the United States, and carbon nanotubes and graphene technology exhibits “small-world” characteristics in this respect. It also shows that the factors GDP per capita (GPC), R&D expenditure (RDE) and the export of global trade value (ETV) negatively affect the level of international collaboration. It could be useful for China and other developing countries to make international scientific and technological collaboration strategies and policies in the future.

For FULL-TEXT go to http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0167772#sec020

Author(s): Xu Bai, Yun Liu
Organization(s): Beijing Institute of Technology
Source: PLoS ONE
Year: 2016

Generating Competitive Technical Intelligence Using Topical Analysis, Patent Citation Analysis, and Term Clumping Analysis

Because of the flexibility and complexity of Newly Emerging Science and Technologies (NESTs), traditional statistical analysis fails to capture technology evolution in detail. Tracking technology evolution pathways supports industrial, governmental, and academic decisions to guide future development trends. Patents are one of the most important NESTs data sources and are pertinent to developmental paths. This paper draws upon text analyses, augmented by expert knowledge, to identify key NESTs sub-domains and component technologies. We then complement those analyses with patent citation analysis to help track developmental progressions. We identify key sub-domain patents, associated with particular component technology trajectories, then extract pivotal patents via citation analysis. We compose evolutionary pathways by combining citation and topical intelligence obtained through term clumping. We demonstrate our approach with empirical analysis of dye-sensitized solar cells (DSSCs), as an example of a promising NESTs, contributing to the remarkable growth in the renewable energy industry. The systematic approach we proposed not only offers a macro-perspective covering technology development levels and future trends, but also makes R&D information accessible for micro-level probes as needed. We work to uncover developmental trends and to compile mentions of possible applications, and this study informs NESTs management by spotting prime opportunities for innovation.

Author(s): Ying Huang, Yi Zhang, Jing Ma, Alan L. Porter, Xuefeng Wang, Ying Guo
Organization(s): Beijing Institute of Technology, University of Technology Sydney
Source: Anticipating Future Innovation Pathways Through Large Data Analysis pp 153-172
Year: 2016

Nano-enabled Drug Delivery in Cancer Therapy: Literature Analysis Using the MeSH System

Biomedical literature provides abundant knowledge on R&D development and emerging themes and techniques to researchers and to enhance clinical treatment. Tracing research topic activity and researcher connections, and understanding evolving research landscapes, supports identification of research domain potential and informs R&D portfolio management. Methods: We offer a systematic approach to summarize biomedical research information compiled from the MEDLINE database. Selected MeSH qualifiers are applied as properties for clustering terms. Linkages among clusters are measured based on an object–attribute–value, relative research concentration. By arraying selected technical dimensions against each other, we enable identification and evaluation of latent connections. Results: 10354 MEDLINE records from 2000 to 2013 on nano-enabled drug delivery (NEDD) for cancer treatment are retrieved and analyzed. Seven topical clusters are generated with relatively clear boundaries. Elements with high relative research concentration but low number of records show emerging trends. And the concentrations’ decline indicates the universalization of drugs and nano components in cancer treatment. Conclusions: This systematic topical analysis process helps explore particular technological trends and potentials in biomedical areas. It combines an algorithm to reveal latent connections hidden in literature text content with expert judgement. From the standpoint of technology assessment, it provides researchers and administrators the ability to capture biomedical research dynamics.

http://www.eurekaselect.com/144824/article

Author(s): Tejraj M Aminabhavi, Jing Ma and Alan L Porter
Organization(s): Shree Dhanvantary Pharmacy College
Source: Pharmaceutical Nanotechnology
Year: 2016