Category Archives: ST&I indicators

Qualitative Patents Evaluation Through the Analysis of Their Citations. Case of the Technological Sectors in the Basque Country

Citation analysis, Patents, Research Examples, ST&I indicators

Patents are an output of the level of innovation of a company or region. Patent quantitative studies are performed by simply counting the number of these documents. For the qualitative evaluation, there is a certain consensus among the authors to consider the citations as the most adequate indicator. However, this indicator presents several problems regarding its correct interpretation. In the present study, in order to avoid the typical citation interpretation biases, a precise methodology is presented. As an illustrative example, we present a comparative study of the quality of patents in technological sectors of the Basque Country region over the period 1991–2011.

DOI
https://doi.org/10.1007/978-3-319-96005-0_28

Author(s): J. Gavilanes-Trapote, Ernesto Cilleruelo-Carrasco, I. Etxeberria-Agiriano, Gaizka Garechana, Alejandro Rodríguez Andara
Organization(s): University of the Basque Country
Source: Engineering Digital Transformation. Lecture Notes in Management and Industrial Engineering. Springer, Cham
Year: 2018

Identifying translational indicators and technology opportunities for nanomedical research using tech mining: The case of gold nanostructures

Research Examples, Research Type, ST&I indicators

Clinical translation of scientific discoveries from bench to bedside is typically a challenging process with sporadic progress along its trajectory. Analyzing R&D can provide key intelligence on advancing biomedical innovation in target domains of interest. In this study, we explore the feasibility of using a streamlined tech mining approach for identification of translational indicators and potential opportunities, using observable markers extracted from selected research literature. We apply this strategy to analyze a set of 23,982 PubMed records that involved gold nanostructures (GNSs) research. Nine indicators are generated to assess what different GNSs research activities had achieved and to predict where GNSs research will likely go. We believe such analysis can provide useful translation intelligence for researchers, funding agencies, and pharmaceutical and biotech companies.

https://doi.org/10.1016/j.techfore.2018.08.002

Author(s): Jing Ma, Natalie F. Abrams, Alan L. Porter, Donghua Zhu, Dorothy Farrell
Organization(s): Shenzhen University, NIH, Beijing Institute of Technology
Source: Technological Forecasting and Social Change
Year: 2018

A case study on the use of machine learning techniques for supporting technology watch

Research Examples, Research Type, ST&I indicators, Tech mining

Technology Watch human agents have to read many documents in order to manually categorize and dispatch them to the correct expert, that will later add valued information to each document. In this two step process, the first one, the categorization of documents, is time consuming and relies on the knowledge of a human categorizer agent. It does not add direct valued information to the process that will be provided in the second step, when the document is revised by the correct expert.

This paper proposes Machine Learning tools and techniques to learn from the manually pre-categorized data to automatically classify new content. For this work a real industrial context was considered. Text from original documents, text from added value information and Semantic Annotations of those texts were used to generate different models, considering manually pre-established categories. Moreover, three algorithms from different approaches were used to generate the models. Finally, the results obtained were compared to select the best model in terms of accuracy and also on the reduction of the amount of document readings (human workload).

https://doi.org/10.1016/j.datak.2018.08.001

Author(s): Alain Perez, Rosa Basagoiti, Ronny Adalberto Cortez, Felix Larrinaga, Ekaitz Barrasa, Ainara Urrutia
Organization(s): Mondragon Unibertsitatea
Source: Data & Knowledge Engineering
Year: 2018

A new model based on patent data for technology early warning research

Research Examples, Research Type, ST&I indicators, Strategic planning

As technology competition among enterprises become more intense, technical crisis occurs in enterprises, such as technological substitution and technology divulgence. Thus, it is necessary to warn enterprises of those technical crises that can be called technology early warning. As patent data contains much technology information, it becomes an efficient source to analyse technology. This paper proposes a technology early warning model based on patent data to help enterprises execute technology early warning from the perspective of its technology status. To do so, we set ten indicators from four aspects to evaluate the enterprise’s technology status at first, calculate the index of enterprise’s technical crisis with AHP, and then propose five early warning levels. China Petroleum & Chemical Corporation (Sinopec Group) and the China National Petroleum Corporation (CNPC) are taken as comparative case studies.

https://doi.org/10.1504/IJTM.2018.092969

Author(s): Ying Guo, Ganlu Sun, Lili Zhang, Fan Yang, Junfang Guo, Lin Ma
Organization(s): Beijing Institute of Technology
Source: International Journal of Technology Management
Year: 2018

Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study

Research Examples, Research profiling, ST&I indicators, Tech mining

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.

https://doi.org/10.1016/j.techfore.2018.06.007

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
Year: 2018

A Study of Methods to Identify Industry-University-Research Institution Cooperation Partners based on Innovation Chain Theory

Network analysis, Research Examples, ST&I indicators, ST&I policy, Strategic planning

This study aims at identifying potential industry-university-research collaboration (IURC) partners effectively and analyzes the conditions and dynamics in the IURC process based on innovation chain theory.

Design/methodology/approach: The method utilizes multisource data, combining bibliometric and econometrics analyses to capture the core network of the existing collaboration networks and institution competitiveness in the innovation chain. Furthermore, a new identification method is constructed that takes into account the law of scientific research cooperation and economic factors.
Findings: Empirical analysis of the genetic engineering vaccine field shows that through the distribution characteristics of creative technologies from different institutions, the analysis based on the innovation chain can identify the more complementary capacities among organizations.Research limitations: In this study, the overall approach is shaped by the theoretical concept of an innovation chain, a linear innovation model with specific types or stages of innovation activities in each phase of the chain, and may, thus, overlook important feedback mechanisms in the innovation process.
Practical implications: Industry-university-research institution collaborations are extremely important in promoting the dissemination of innovative knowledge, enhancing the quality of innovation products, and facilitating the transformation of scientific achievements.
Originality/value: Compared to previous studies, this study emulates the real conditions of IURC. Thus, the rule of technological innovation can be better revealed, the potential partners of IURC can be identified more readily, and the conclusion has more value.

http://manu47.magtech.com.cn/Jwk3_jdis/Y2018/V3/I2/38

Author(s): Haiyun Xu, Chao Wang, Kun Dong, Rui Luo, Zenghui Yue, Hongshen Pang
Organization(s):
Source: Journal of Data and Information Science
Year: 2018

Emergence scoring to identify frontier R&D topics and key players

Research Examples, ST&I indicators

Indicators of technological emergence promise valuable intelligence to those determining R&D priorities. We present an implemented algorithm to calculate emergence scores for topical terms from abstract record sets. We offer a family of emergence indicators deriving from those scores. Primary emergence indicators identify “hot topic” terms. We then use those to generate secondary indicators that reflect organizations, countries, or authors especially active at frontiers in a target R&D domain. We also flag abstract records (papers or patents) rich in emergent technology content, and we score research fields on relative degree of emergence. This paper presents illustrative results for example topics – Nano-Enabled Drug Delivery, Non-Linear Programming, Dye Sensitized Solar Cells, and Big Data.

https://doi.org/10.1016/j.techfore.2018.04.016

Author(s): Alan L. Porter, Jon Garner, Stephen F. Carley, Nils C. Newman
Organization: Georgia Institute of Technology
Source: Technological Forecasting and Social Change
Year: 2018

Tracking the emergence of synthetic biology (full-text)

Research Examples, Research Type, ST&I indicators,

Synthetic biology is an emerging domain that combines biological and engineering concepts and which has seen rapid growth in research, innovation, and policy interest in recent years. This paper contributes to efforts to delineate this emerging domain by presenting a newly constructed bibliometric definition of synthetic biology. Our approach is dimensioned from a core set of papers in synthetic biology, using procedures to obtain benchmark synthetic biology publication records, extract keywords from these benchmark records, and refine the keywords, supplemented with articles published in dedicated synthetic biology journals. We compare our search strategy with other recent bibliometric approaches to define synthetic biology, using a common source of publication data for the period from 2000 to 2015. The paper details the rapid growth and international spread of research in synthetic biology in recent years, demonstrates that diverse research disciplines are contributing to the multidisciplinary development of synthetic biology research, and visualizes this by profiling synthetic biology research on the map of science. We further show the roles of a relatively concentrated set of research sponsors in funding the growth and trajectories of synthetic biology. In addition to discussing these analyses, the paper notes limitations and suggests lines for further work.

Full-text available at https://link.springer.com/article/10.1007/s11192-017-2452-5

Author(s): Philip Shapira, Seokbeom Kwon, Jan Youtie
Organization(s): University of Manchester, Georgia Institute of Technology
Source: Scientometrics
Year: 2017

An indicator of technical emergence

Research Examples, Research Type, ST&I indicators

Developing useful intelligence on scientific and technological emergence challenges those who would manage R&D portfolios, assess research programs, or manage innovation. Recently, the U.S. Intelligence Advanced Research Projects Activity Foresight and Understanding from Scientific Exposition Program has explored means to detect emergence via text analyses. We have been involved in positing conceptual bases for emergence, framing candidate indicators, and devising implementations. We now present a software script to generate a family of Emergence Indicators for a topic of interest. This paper offers some background, then discusses the development of this script through iterative rounds of testing, and then offers example findings. Results point to promising and actionable intelligence for R&D decision-makers.

https://link.springer.com/article/10.1007/s11192-018-2654-5?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst

Author(s): Stephen F. Carley, Nils C. Newman, Alan L. Porter, Jon G. Garner
Organizations(s): Search Technology
Source: Scientometrics
Year: 2018

A measure of staying power: Is the persistence of emergent concepts more significantly influenced by technical domain or scale? (full-text)

Research Examples, Research Type, ST&I indicators

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.

https://link.springer.com/article/10.1007/s11192-017-2342-x

Full-text avalaible via ResearchGate
https://www.researchgate.net/publication/315462977_A_measure_of_staying_power_Is_the_persistence_of_emergent_concepts_more_significantly_influenced_by_technical_domain_or_scale

Author(s): Stephen F. Carley, Nils C. Newman, Alan L. Porter, Jon G. Garner
Organization(s): Georgia Institute of Technology, Search Technology
Source: Scientometrics
Year: 2017