Category Archives: Research Type

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

Parallel or Intersecting Lines? Intelligent Bibliometrics for Investigating the Involvement of Data Science in Policy Analysis

Efforts to involve data science in policy analysis can be traced back decades but transforming analytic findings into decisions is still far from straightforward task. Data-driven decision-making requires understanding approaches, practices, and research results from many disciplines, which makes it interesting to investigate whether data science and policy analysis are moving in parallel or whether their pathways have intersected. Our investigation, from a bibliometric perspective, is driven by a comprehensive set of research questions, and we have designed an intelligent bibliometric framework that includes a series of traditional bibliometric approaches and a novel method of charting the evolutionary pathways of scientific innovation, which is used to identify predecessor–descendant relationships in technological topics. Our investigation reveals that data science and policy analysis have intersecting lines, and it can foresee that a cross-disciplinary direction in which policy analysis interacting with data science has become an emergent area in both communities. However, equipped with advanced data analytic techniques, data scientists are moving faster and further than policy analysts. The empirical insights derived from our research should be beneficial to academic researchers and journal editors in related research communities, as well as policy-makers in research institutions and funding agencies.

10.1109/TEM.2020.2974761

Author(s): Yi Zhang, Alan L. Porter, Scott Cunningham, Denise Chiavetta, Nils Newman
Organization(s): University of Technology Sydney, Search Technology Inc., University of Strathclyde
Source: IEEE Transactions on Engineering Management
Year: 2020

Parameter tuning Naïve Bayes for automatic patent classification

In an era of exponential technological growth, business intelligence professionals are more in need than ever of an organized patent landscape in which to conduct technology forecasting and industry positioning. However, the construction of such a system requires time and trained experts, both of which are expensive investments for such a small part of any actual analysis. A natural solution is to employ machine learning (ML), a branch of artificial intelligence that uses statistical information to find patterns and make inferences. The primary benefit of using ML is that these algorithms do not require explicit instruction. In this paper, I present an analysis of feature selection for automatic patent categorization. For a corpus of 7,309 patent applications from the World Patent Information (WPI) Test Collection (Lupu, 2019), I assign International Patent Classification (IPC) section codes using a modified Naïve Bayes classifier. I compare precision, recall, and f-measure for a variety of meta-parameter settings including data smoothing and acceptance threshold. Finally, I apply the optimized model to IPC class and group codes and compare the results of patent categorization to academic literature.

https://doi.org/10.1016/j.wpi.2020.101968

Author(s): Caitlin Cassidy
Organization(s): Search Technology
Source: World Patent Information
Year: 2020

Do national funding organizations properly address the diseases with the highest burden? – Observations from China and the UK (Full-Text)

Recent years have witnessed an incipient shift in science policy from a focus mainly on academic excellence to a focus that also takes into account “societal impact”. This shift raises the question as to whether medical research has given proper attention to the diseases imposing the greatest burden on society. Therefore, with the aim of identifying correlations between research funding priorities and public demand in health, we examine grants issued by the major medical research funding bodies of China and the UK during the decade 2006-2017 and compare the focus of their funded projects with the diseases that carry the highest burden of death, risk, or loss of health. The results indicate that the funding decisions of both nations do correspond to the illnesses with the highest health impact on their citizens. For both regions, the greatest health concerns surround non-communicable diseases, and neoplasms and cardiovascular disease in particular. In China, national health priorities have remained focused on these illnesses for the benefit of its own population, whereas the UK has funded a wider variety of research, extending to projects with impacts outside its borders to some developing countries. Additionally, despite an increased incidence of mental illness and HIV/AIDs in China, there is evidence that less priority has been given to these conditions. Both of these health areas seem to require more attention from China’s national funding agencies and the society in general. Methodologically, this study can serve as an example of how to conduct analyses related to public health issues by combining informetric methods and data with data and tools from other fields, thereby inspiring other scientometrics studies.

For FULL-TEXT download at DOI: 10.31219/osf.io/ckpf8

Author(s): Lin Zhang, Wenjing ZHAO, Jianhua Liu, Gunnar Sivertsen, Ying HUANG
Organization(s): Wuhan University, KU Leuven, Beijing Wanfang Data Ltd., Nordic Institute for Studies in Innovation Research and Education (NIFU)
Source: SocArXiv
Year: 2020

A Multi-match Approach to the Author Uncertainty Problem (Full-Text)

The ability to identify the scholarship of individual authors is essential for performance evaluation. A number of factors hinder this endeavor. Common and similarly spelled surnames make it difficult to isolate the scholarship of individual authors indexed on large databases. Variations in name spelling of individual scholars further complicates matters. Common family names in scientific powerhouses like China make it problematic to distinguish between authors possessing ubiquitous and/or anglicized surnames (as well as the same or similar first names). The assignment of unique author identifiers provides a major step toward resolving these difficulties. We maintain, however, that in and of themselves, author identifiers are not sufficient to fully address the author uncertainty problem. In this study we build on the author identifier approach by considering commonalities in fielded data between authors containing the same surname and first initial of their first name. We illustrate our approach using three case studies.

For FULL-TEXT see https://doi.org/10.2478/jdis-2019-0006 

Author(s): Stephen F. Carley, Alan L. Porter, Jan L. Youtie
Organization(s): Georgia Institute of Technology
Source: Journal of Data and Information Science
Year: 2019 (online. 2017 print)

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

Technological Convergence: The Analysis of Emergent Topics on Chitosan (FULL-TEXT)

This research identifies emergent topical trends of chitosan technology and its applications and constructs technological directions for business strategy in a hyper-competitive environment. A total of 2,612 scientific papers on chitosan technology published between 2010 and 2019 was retrieved from Web of Science (WoS) using various search queries. Results from ibliometric predictive intelligence (BPI) modelling highlight four major emergent topics related to technology convergence, namely shelf life, regenerative medicine, therapeutic agents, and antioxidant capacities. Four potential industries for chitosan application were identified: healthcare; cosmetics; agriculture; and food and beverages. The findings reveal a 75% increase in research publications since 2016 compared with previous years, which in turn illustrates the potential of technological goals to stimulate socially responsible research in the future.

For FULL-TEXT click HERE

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

Study on Worldwide Development and Trends of Quantum Technologies Based on Patent Data (FULL-TEXT)

Quantum technologies attracted much attention for their disruptive potential in last two decades. The article analyzes the worldwide patent landscape for quantum technologies based on data extracted from Derwent Innovation and Web of Science. The quantum technologies were grouped
into three distinct technology areas of quantum computing, quantum communication and quantum sensing, to demonstrate detailed development and trends respectively. It shows that quantum technology is a highly competitive research field, and United States, China and Japan are the most prominent countries, in particular China made a great progress in recent years. United States has a significant advantage in the field of quantum computing, which is the most promising field, meanwhile China has a significant advantage in the field of quantum communication and succeeds in launching a quantum satellite.

FULL-TEXT http://www.ijiet.org/vol10/1370-CP2-026.pdf

Author(s): Juan Zhang, Qianfei Tian, Chuan Tang, Lina Wang, Jing Xu, and Junmin Fang
Organization(s): Chengdu Library and Information Center
Source: International Journal of Information and Education Technology
Year: 2020

Fuel-Cell Electric Vehicles: Plotting a Scientific and Technological Knowledge Map (FULL-TEXT)

The fuel-cell electric vehicle (FCEV) has been defined as a promising way to avoid road transport greenhouse emissions, but nowadays, they are not commercially available. However, few studies have attempted to monitor the global scientific research and technological profile of FCEVs. For this reason, scientific research and technological development in the field of FCEV from 1999 to 2019 have been researched using bibliometric and patent data analysis, including network analysis. Based on reports, the current status indicates that FCEV research topics have reached maturity. In
addition, the analysis reveals other important findings: (1) The USA is the most productive in science and patent jurisdiction; (2) both Chinese universities and their authors are the most productive in science; however, technological development is led by Japanese car manufacturers; (3) in scientific research, collaboration is located within the tri-polar world (North America–Europe–Asia-Pacific); nonetheless, technological development is isolated to collaborations between companies of the same
automotive group; (4) science is currently directing its efforts towards hydrogen production and storage, energy management systems related to battery and hydrogen energy, Life Cycle Assessment, and greenhouse gas (GHG) emissions. The technological development focuses on technologies related to electrically propelled vehicles; (5) the International Journal of Hydrogen Energy and SAE Technical Papers are the two most important sources of knowledge diffusion. This study concludes by outlining the knowledge map and directions for further research.

https://doi.org/10.3390/su12062334 for FULL-TEXT

Author(s): Izaskun Alvarez-Meaza, Enara Zarrabeitia-Bilbao, Rosa Maria Rio-Belver, and Gaizka Garechana-Anacabe
Organization(s): University of the Basque Country
Source: Sustainability
Year: 2020