Category Archives: ST&I indicators

Learning about learning: patterns of sharing of research knowledge among Education, Border, and Cognitive Science fields

Citation analysis, Research Examples, ST&I indicators

This study explores the patterns of exchange of research knowledge among Education Research, Cognitive Science, and what we call “Border Fields.” We analyze a set of 32,121 articles from 177 selected journals, drawn from five sample years between 1994 and 2014. We profile the references that those articles cite, and the papers that cite them. We characterize connections among the fields in sources indexed by Web of Science (WoS) (e.g., peer-reviewed journal articles and proceedings), and connections in sources that are not (e.g., conference talks, chapters, books, and reports). We note five findings—first, over time the percentage of Education Research papers that extensively cite Cognitive Science has increased, but the reverse is not true. Second, a high percentage of Border Field papers extensively cite and are cited by the other fields. Border Field authors’ most cited papers overlap those most cited by Education Research and Cognitive Science. There are fewer commonalities between Educational research and Cognitive Science most cited papers. This is consistent with Border Fields being a bridge between fields. Third, over time the Border Fields have moved closer to Education Research than to Cognitive Science, and their publications increasingly cite, and are cited by, other Border Field publications. Fourth, Education Research is especially strongly represented in the literature published outside those WoS-indexed publications. Fifth, the rough patterns observed among these three fields when using a more restricted dataset drawn from the WoS are similar to those observed with the dataset lying outside the WoS, but Education Research shows a far heavier influence than would be indicated by looking at WoS records alone.

https://doi.org/10.1007/s11192-019-03012-3

Author(s): Alan L. Porter, David J. Schoeneck, Jan Youtie, Gregg E. A. Solomon, Seokbeom Kwon, Stephen F. Carley
Organization(s): Search Technology, Georgia Tech, US National Science Foundation
Source: Scientometrics
Year: 2019

An integrated solution for detecting rising technology stars in co-inventor networks

Network analysis, Research Examples, Research Type, ST&I indicators

Online patent databases are powerful resources for tech mining and social network analysis and, especially, identifying rising technology stars in co-inventor networks. However, it’s difficult to detect them to meet the different needs coming from various demand sides. In this paper, we present an unsupervised solution for identifying rising stars in technological fields by mining patent information. The solution integrates three distinct aspects including technology performance, sociability and innovation caliber to present the profile of inventor, meantime, we design a series of features to reflect multifaceted ‘potential’ of an inventor. All features in the profile can get weights through the Entropy weight method, furthermore, these weights can ultimately act as the instruction for detecting different types of rising technology stars. A K-Means algorithm using clustering validity metrics automatically groups the inventors into clusters according to the strength of each inventor’s profile. In addition, using the nth percentile analysis of each cluster, this paper can infer which cluster with the most potential to become which type of rising technology stars. Through an empirical analysis, we demonstrate various types of rising technology stars: (1) tech-oriented RT Stars: growth of output and impact in recent years, especially in the recent 2 years; active productivity and impact over the last 5 years; (2) social-oriented RT Stars: own an extended co-inventor network and greater potential stemming from those collaborations; (3) innovation-oriented RT Stars: Various technical fields with strong innovation capabilities. (4) All-round RT Stars: show prominent potential in at least two aspects in terms of technical performance, sociability and innovation caliber.

https://doi.org/10.1007/s11192-019-03194-w

Author(s): Lin Zhu, Donghua Zhu, Xuefeng Wang, Scott W. Cunningham, Zhinan Wang
Organization(s): Beijing Institute of Technology
Source: Scientometrics
Year: 2019

What people learn about how people learn: An analysis of citation behavior and the multidisciplinary flow of knowledge

Citation analysis, Interdisciplinarity, Research Examples, Research Type, ST&I indicators

We explore the contention that the seminal US National Academies consensus report,How People Learn(HPL),played a major role in bridging the flow of knowledge from Cognitive Science to Education. Our paper yielded four important results: First, HPL is, on a number of bibliometric measures, an unusually interdisciplinary work.Focusing on the fields of particular interest here, our citation analysis shows the Education, Cognitive Science,and Borderfield (e.g., Educational Psychology, Learning Sciences, and Learning Technology and Human-Computer Interaction) literatures all to have been major influences on it. Second, we found HPL to be unusually highly cited–and by publications from an unusually diverse set of disciplines. Beyond Education, Cognitive Science, and Border field publications, HPL was also relatively highly cited by publications in Medical/Health-related, Engineering, and other Discipline-Based Education Research fields. Third, undermining the claim that HPL served as a gateway to the Cognitive Science literature, we found Education articles citing HPL not to be more likely to have Cognitive Science as a major influence than are Education articles more generally, as in-dicated by their cited references. Finally, the Education publications that cited HPL were far more likely to refer to concepts in HPL that were already prevalent in the Education literature rather than to concepts from Cognitive Science. Conversely, the Cognitive Science publications that cited HPL were more apt to refer to concepts already in the Cognitive Science literature. Taken together, these results are a caution that, even for a highly regarded multidisciplinary work cited widely by publications from multiple disciplines, its direct influence could be largely disciplinary. Implications for the policy goals of fostering interdisciplinary research and the role of National Academies consensus reports are discussed.

https://doi.org/10.1016/j.respol.2019.103835

Author(s): Gregg E. A. Solomon, Jan Youtie, Stephen Carley, Alan L. Porter  
Organization(s): National Science Foundation, Georgia Institute of Technology                                                                                                        
Source: Research Policy                                                                                       
Year: 2019

Data Analytics for Better Informed Technology & Engineering Management

Research Examples, ST&I indicators

Many fields are managing better by incorporating advanced data analytics – e.g., even sports such as baseball. We illustrate with a novel set of indicators that distinguish cutting edge R&D activity to provide competitive technical intelligence on “who’s doing what, where, and when.” Our aim is to stimulate your thinking about what data analytics could help you achieve your technology management goals more effectively.

10.1109/EMR.2019.2928265

Author: Alan Porter
Organization: Search Technology
Source:  IEEE Engineering Management Review
Year: 2019

Research addressing emerging technological ideas has greater scientific impact

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

This study empirically examines the association between the extent of emerging technological ideas in a scientific publication and its future scientific impact measured by number of citations. We analyze metadata of scientific publications in three scientific domains: Nano-Enabled Drug Delivery, Synthetic Biology, and Autonomous Vehicles. By employing a bibliometric indicator for identifying and quantifying emerging technological ideas – as derived terms from the titles and abstracts – we measure the extent to which the publication contains emerging technological ideas in each domain. Then, we statistically estimate the size and statistical significance of the relationship between the publication-level technological emergence score and the normalized number of citations accruing to the publication.

Our analysis shows that the degree to which a paper contains technologically emerging ideas is positively and strongly associated with its future citation impact in each of the three domains. An additional analysis demonstrates that this relationship holds for citations from other publications, both in the same field as, and in different fields from, the scientific domain of the focal publication. A series of tests for validation further support our argument that the greater the extent to which scientific knowledge (a paper) contains emerging ideas, the bigger its scientific impact. Implications for academic researchers, research policymakers, and firms are discussed.

https://doi.org/10.1016/j.respol.2019.103834

Author(s): Seokbeom Kwon, Xiaoyu Liu, Alan L. Porter, Jan Youtie
Organization(s): Georgia Institute of Technology, Beijing Institute of Technology
Source: Research Policy
Year: 2019

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

Research Examples, Research Type, ST&I indicators

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: 2019

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

Patents, Research Examples, Research Type, ST&I indicators

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

Research Examples, Research Type, ST&I indicators

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

Research Examples, ST&I indicators

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

Network analysis, Research Examples, Research Type, ST&I indicators

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