Category Archives: ST&I indicators

An approach to identify emergent topics of technological convergence: A case study for 3D printing

Technological Convergence (TC) reflects developmental processes that overlap different technological fields. It holds promise to yield outcomes that exceed the sum of its subparts. Measuring emergence for a TC environment can inform innovation management. This paper suggests a novel approach to identify Emergent Topics (ETopics) of the TC environment within a target technology domain using patent information. A non-TC environment is constructed as a comparison group. First, TC is operationalized as a co-classification of a given patent into multiple 4-digit IPC codes (≥2-IPC). We take a set of patents and parse those into three sub-datasets based on the number of IPC codes assigned 1-IPC (Non-TC), 2-IPC and ≥3-IPC. Second, a method is applied to identify emergent terms (ETs) and calculate emergence score for each term in each sub-dataset. Finally, we cluster those ETs using Principal Components Analysis (PCA) to generate a factor map with ETopics. A convergent domain – 3D printing – is selected to present the illustrative results. Results affirm that for 3D printing, emergent topics in TC patents are distinctly different from those in non-TC patents. The number of ETs in the TC environment is increasing annually.

https://doi.org/10.1016/j.techfore.2018.12.015

Author(s): Zhinan Wang, Alan L.Porter, Xuefeng Wang, Stephen Carley
Organization(s): Beijing Institute of Technology, Georgia Institute of Technology
Source: Technological Forecasting and Social Change
Year: 2018

UNIVERSITY-INDUSTRY COOPERATION NETWORK IN ACADEMIC AND TECHNOLOGICAL PRODUCTIVITY (full-text)

Knowing the gear of the triple-helix is fundamental to analyze the impact of public policies in the scenario of a country, especially when the variables linked to innovation refer to the chronological production of the facts. In this perspective, an analysis was assembled from intentional samples per regions of Brazil linked to the engineering areas, identifying indices that could demonstrate this evolutionary line, highlighting mainly in their numbers, the quantitative of patents of engineering deposited with and without the relation university-enterprise partnership (EU), with state mapping of the federation, public and private investments in P&D, patents with their respective classifications and scientific production of Engineering indexed to Scopus .It was concluded that from the years of 2005 with the Innovation Law there was a boost in these indices making It possible to understand that the numbers of articles began to scale a greater use for the production of patents, with emphasis on the South and Southeast universities of the country, although It is still a number that needs greater expressiveness for the country’s future.

http://www.revistageintec.net/index.php/revista/article/view/1331

Author(s): Carlos Tadeu Santana Tatum, Flávio Ferreira da Conceição Franceschi, Letícia-Maria Macedo Tatum, Jonas Pedro Fabris, Suzana Leitão Russo
Organization(s): Federal University of Sergipe
Source: Revista GEINTEC
Year: 2019

Characterization of strategic emerging technologies: the case of big data

Current enterprises face organizational and cultural barriers to adopt and harness the potential of strategic emerging technologies. Late adoption of these technologies will affect competitiveness from which it will be hard to recover. Within the frame of technology analysis field, the present work aims at introducing an approach to obtain the characterization of emerging technologies, which facilitates understanding and identifies their potential. This characterization is based on the analysis of scientific activity, to which a set of quantitative methods is applied, namely bibliometrics, text mining, principal component analysis and time series analysis. The outcome is based on obtaining a set of dominant sub-technologies, which are described by means of individual time series, which also allow evolution of the technology as a whole to be forecasted. The approach is applied to the Big Data technology field and the results suggest that sub-technologies such as Mobile Telecommunications and Internet of things will lead this field in the near future.

https://doi.org/10.1007/s10100-018-0597-9

Author(s): Iñaki Bildosola, Gaizka Garechana Enara Zarrabeitia, Ernesto Cilleruelo
Organizations(s): University of the Basque Country (UPV/EHU)
Source: Central European Journal of Operations Research
Year: 2018

How is Data Science Involved in Policy Analysis?: A Bibliometric Perspective

What are the implications of big data in terms of big impacts? Our research focuses on the question, “How are data analytics involved in policy analysis to create complementary values?” We address this from the perspective of bibliometrics. We initially investigate a set of articles published in Nature and Science, seeking cutting-edge knowledge to sharpen research hypotheses on what data science offers policy analysis. Based on a set of bibliometric models (e.g., topic analysis, scientific evolutionary pathways, and social network analysis), we follow up with studies addressing two aspects: (1) we examine the engagement of data science (including statistical, econometric, and computing approaches) in current policy analyses by analyzing articles published in top-level journals in the areas of political science and public administration; and (2) we examine the development of policy analysis-oriented data analytic models in top-level journals associated with computer science (including both artificial intelligence and information systems). Observations indicate that data science contribution to policy analysis is still an emerging area. Data scientists are moving further than policy analysts, due to technical difficulties in exploiting data analytic models. Integrating artificial intelligence with econometrics is identified as a particularly promising direction.

https://ieeexplore.ieee.org/abstract/document/8481979

Author(s): Yi Zhang, Alan L. Porter, Scott Cunningham, Denise Chiavetta, Nils Newman
Organization(s): University of Technology, Georgia Institute of Technology, Delft University of Technology
Source: 2018 Portland International Conference on Management of Engineering and Technology (PICMET)
Year: 2018

A Link Prediction-Based Method for Identifying Potential Cooperation Partners: A Case Study on Four Journals of Informetrics

Global academic exchange and cooperation have become an increasing trend in both academia and industry, but how to quickly and effectively identify potential partners is becoming an urgent problem. This paper proposes a link prediction-based model to help researchers identify partners from a large collection of academic articles in a given technological area. We initially construct a co-authorship network, and take a series of indices based on network and similarity of researchers into consideration. A fitting model of link prediction is then established, in which logistic regression analysis is involved. An empirical study on four journals of informetrics is conducted to demonstrate the reliability of the proposed method.

https://ieeexplore.ieee.org/document/8481974

Author(s): Lu Huang, Yihe Zhu, Yi Zhang, Xiao Zhou, Xiang Jia
Organization(s): Beijing Institute of Technology
Source: 2018 Portland International Conference on Management of Engineering and Technology (PICMET)
Year: 2018

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

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

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

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

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

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