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The role of knowledge intensive business services in economic development: a bibliometric analysis from Bradford, Lotka and Zipf laws (FULL-TEXT)

The international scientific community considers Knowledge Intensive Business Services (KIBS) as one of the main themes related to innovation and economic development. This article presents a review based on Scopus and ISI Web of Knowledge databases, on the KIBS topic in the world, considering the period 2000-2017. The study aimed to understand the role of KIBS in regional development, and they were considered in their roles as innovations’ attributes and resources, methodologies and tools for innovation management, transfer, knowledge diffusion and networks. They concentrate in the areas of business, management and economics, developing approaches to seek innovation, competitiveness and economic development.

For FULL-TEXT http://dx.doi.org/10.1590/0104-530×4356-19

Author(s): Ronnie Figueiredo, Osvaldo Quelhas, Júlio Vieira Neto, João J. Ferreira
Organization(s): Universidade Beira Interior – UBI, Universidade Federal Fluminense – UFF
Source: Gestão & Produção
Year: 2019

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

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. VantagePoint used in data preprocessing. 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. VantagePoint used in data preprocessing.

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, Delft University of Technology
Source: Scientometrics
Year: 2019

A hybrid approach to detecting technological recombination based on text mining and patent network analysis

Detecting promising technology groups for recombination holds the promise of great value for R&D managers and technology policymakers, especially if the technologies in question can be detected before they have been combined. However, predicting the future is always easier said than done. In this regard, Arthur’s theory (The nature of technology: what it is and how it evolves, Free Press, New York, 2009) on the nature of technologies and how science evolves, coupled with Kuhn’s theory of scientific revolutions (Kuhn in The structure of scientific revolutions, 1st edn, University of Chicago Press, Chicago, p 3, 1962), may serve as the basis of a shrewd methodological framework for forecasting recombinative innovation. These theories help us to set out quantifiable criteria and decomposable steps to identify research patterns at each stage of a scientific revolution. The first step in the framework is to construct a conceptual model of the target technology domain, which helps to refine a reasonable search strategy. With the model built, the landscape of a field—its communities, its technologies, and their interactions—is fleshed out through community detection and network analysis based on a set of quantifiable criteria. The aim is to map normal patterns of research in the domain under study so as to highlight which technologies might contribute to a structural deepening of technological recombinations. Probability analysis helps to detect and group candidate technologies for possible recombination and further manual analysis by experts. To demonstrate how the framework works in practice, we conducted an empirical study on AI research in China. We explored the development potential of recombinative technologies by zooming in on the top patent assignees in the field and their innovations. In conjunction with expert analysis, the results reveal the cooperative and competitive relationships among these technology holders and opportunities for future innovation through technological recombinations.

https://doi.org/10.1007/s11192-019-03218-5

Author(s): Xiao Zhou, Lu Huang, Yi Zhang, Miaomiao Yu
Organization(s): Xidian University, Beijing Institute of Technology
Source: Scientometrics
Year: 2019

The Relationship between Forward and Backward Diversity in CORE Datasets

In this paper we seek to better understand the relationship between forward diversity in the Cognitive Science and Educational Research literature, as well as what we call Border fields (i.e. those fields which exist at the intersection of Cognitive Science and Education Research). We find a clear and convincing relationship between forward and backward diversity in the datasets we study. Among all available explanatory variables, Integration scores claim the strongest correlation in terms of their ability to account for forward diversity. When comparing results from this study to benchmark results from a prior study (using the same indicators) the datasets in this study show a tendency to be both more integrative and diffuse.

https://doi.org/10.1007/s11192-019-03163-3

Author(s): Stephen F. Carley, Seokbeom Kwon, Alan L. Porter, Jan L. Youtie
Organization(S): Search Technology, Georgia Institute of Technology
Source: Scientometrics
Year: 2019

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

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

Does deep learning help topic extraction? A kernel k-means clustering method with word embedding

Topic extraction presents challenges for the bibliometric community, and its performance still depends on human intervention and its practical areas. This paper proposes a novel kernel k-means clustering method incorporated with a word embedding model to create a solution that effectively extracts topics from bibliometric data. The experimental results of a comparison of this method with four clustering baselines (i.e., k-means, fuzzy c-mean as,principal component analysis, and topic models) on two bibliometric datasets demonstrate its effectiveness across either a relatively broad range of disciplines or a given domain. An empirical study on bibliometric topic extraction from articles published by three top-tier bibliometric journals between 2000 and 2017, supported by expert knowledge-based evaluations, provides supplemental evidence of the method’s ability on topic extraction. Additionally, this empirical analysis reveals insights into both overlapping and diverse research interests among the three journals that would benefit journal publishers, editorial boards, and research communities.

https://doi.org/10.1016/j.joi.2018.09.004

Author(s): Yi Zhang, Jie Lu, Feng Liu, Qian Liu, Alan Porter, Hongshu Chen, Guangquan Zhang
Organization(s): University of Technology Sydney, Beijing Institute of Technology, Georgia Institute of Technology
Source: Journal of Informetrics
Year: 2018

Chapter 2 – Lessons From 10 Years of Nanotechnology Bibliometric Analysis

This chapter summarizes the 10-year experiences of the Program in Science, Technology, and Innovation Policy (STIP) at Georgia Institute of Technology (Georgia Tech) in support of the Center for Nanotechnology in Society at Arizona State University (CNS-ASU) in understanding, characterizing, and conveying the development of nanotechnology research and application. This work was labeled “Research and Innovation Systems Assessment” or (RISA) by CNS-ASU. CNS-ASU was designed to implement a set of methods to anticipate societal impacts (including environmental, health, and safety impacts) and lay the foundation for making changes to emerging technologies at an early stage in their development.

RISA concentrates on identifying and documenting quantifiable aspects of nanotechnology, including academic, commercial/industrial, and government nanoscience and nanotechnology (nanotechnologies) activity, research, and projects. RISA at CNS-ASU engaged in the first systematic attempt of its kind to define, characterize, and track a field of science and technology. A key element to RISA was the creation of a replicable approach to bibliometrically defining nanotechnology. Researchers in STIP, and beyond, could then query the resulting datasets to address topical areas ranging from basic country and regional concentrations of publications and patents to findings about social science literature, environmental, health, and safety research and usage, to study corporate entry into nanotechnology and to explore application areas as special interests arose. Key features of the success of the program include the following:

  • Having access to “large-scale” R&D abstract datasets
  • Analytical software
  • A portfolio that balances innovative long-term projects, such as webscraping to understand nanotechnology developments in small and medium-sized companies, with research characterizing the emergence of nanotechnology that more readily produces articles
  • Relationships with diverse networks of scholars and companies working in the nanotechnology science and social science domains
  • An influx of visiting researchers
  • A strong core of students with social science, as well as some programming background
  • A well-equipped facility and management by the principals through weekly problem-solving meetings, mini-deadlines, and the production journal articles rather than thick final reports.

https://doi.org/10.1016/B978-0-12-813588-4.00002-6

Author(s): Jan Youtie, Alan L.Porter, Philip Shapira, Nils Newman
Organization(s): Georgia Institute of Technology, Search Technology
Source: Nanotechnology Environmental Health and Safety (Third Edition)
Year: 2018

Measuring Interdisciplinary Research Categories and Knowledge Transfer: A Case Study of Connections between Cognitive Science and Education

This is a “bottom-up” paper in the sense that it draws lessons in defining disciplinary categories under study from a series of empirical studies of interdisciplinarity. In particular, we are in the process of studying the interchange of research-based knowledge between Cognitive Science and Educational Research. This has posed a set of design decisions that we believe warrant consideration as others study cross-disciplinary research processes.

https://doi.org/10.1162/posc_a_00317

Author(s): Alan L. Porter, Stephen F. Carley, Caitlin Cassidy, Jan Youtie, David J. Schoeneck, Seokbeom Kwon and Gregg E. A. Solomon
Organization(s): Georgia Institute of Technology, Search Technology Inc.
Source: Perspectives on Science
Year: 2019

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

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

Bibliometric Analysis of Trends in Global Sustainable Livelihood Research (Full-Text)

The concept of sustainable livelihoods (SL) is one of the most important subjects of sustainable development, and is an important long-term goal for poverty alleviation. There has been growing interest in the nature and practical application of SL in recent decades. This paper applies bibliometric analysis to collect and analyze data on sustainable livelihoods from the expanded Science Citation index (SCIE) and the Social Sciences Citation Index (SSCI). Bibliometric maps can assist greatly in visualizing and summarizing large volumes of data and in studying scientific outputs. The findings offer insights into research trends pertaining to SL, such as these: (1) In recent decades there has been an increase in both the number of papers on SL and their scientific influence. (2) The most active journals are Sustainability, Ecology and Society, Land Use Policy, and International Journal of Sustainable Development and World Ecology. (3) SL papers are distributed mainly in the fields of Environmental Sciences, Environmental Studies, Ecology, Planning & Development, and Green & Sustainable Science & Technology. (4) The USA and UK are leaders in SL research as measured by both the quantity and quality of SL publications. Some developing countries, notably India and China, have seen an increase in SL publications in recent years. (5) Wageningen University in Netherlands, the Chinese Academy of Science, and the Center for International Forestry Research (CIFOR), headquartered in Indonesia, have had a major influence in the field of international SL research. (6) International cooperation has a positive effect on the growth of SL research, suggesting that there is a need for strengthening cooperation among countries, international institutions, and individuals. (7) Major areas of SL research (“hot topics”) are theoretical research on the SL concept; ecosystem conservation; poverty reduction in the poverty-stricken areas; the impact of climate change on livelihoods; and linkages between SL-related policies and institutional change

For Full-Text https://doi.org/10.3390/su11041150

Author(s): Chenjia Zhang, Yiping Fang, Xiujuan Chen, Tian Congshan
Organization(s): Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, University of Chinese Academy of Sciences
Source: Sustainability
Year: 2019