A novel three-dimension perspective to explore technology evolution

Li, Munan

doi:10.1007/s11192-015-1591-9

Cited by 23 publications

(9 citation statements)

References 38 publications

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“…A few papers compared the publications and patents on graphene to reveal the relationship between research and innovation (Peng, 2016). Still, a detailed and large scale analysis of graphene R&D is needed to fully reveal the landscape (Li, 2015).…”

Section: Introductionmentioning

confidence: 99%

Trends Analysis of Graphene Research and Development

Lixue

Wang

Yingqi³

et al. 2018

Journal of Data and Information Science

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Purpose: This study aims to reveal the landscape and trends of graphene research in the world by using data from Chemical Abstracts Service (CAS).Design/methodology/approach: Index data from CAS have been retrieved on 78,756 papers and 23,057 patents on graphene from 1985 to March 2016, and scientometric methods were used to analyze the growth and distribution of R&D output, topic distribution and evolution, and distribution and evolution of substance properties and roles. Findings:In recent years R&D in graphene keeps in rapid growth, while China, South Korea and United States are the largest producers in research but China is relatively weak in patent applications in other countries. Research topics in graphene are continuously expanding from mechanical, material, and electrical properties to a diverse range of application areas such as batteries, capacitors, semiconductors, and sensors devices. The roles of emerging substances are increasing in Preparation and Biological Study. More techniques have been included to improve the preparation processes and applications of graphene in various fields. Research limitations:Only data from CAS is used and some R&D activities solely reported through other channels may be missed. Also more detailed analysis need to be done to reveal the impact of research on development or vice verse, development dynamics among the players, and impact of emerging terms or substance roles on research and technology development. Practical implications:This will provide a valuable reference for scientists and developers, R&D managers, R&D policy makers, industrial and business investers to understand the landscape and trends of graphene research. Its methodologies can be applied to other fields or with data from other similar sources.

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Section: Introductionmentioning

confidence: 99%

Trends Analysis of Graphene Research and Development

Lixue

Wang

Yingqi³

et al. 2018

Journal of Data and Information Science

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“…The reason is that patent statistics provide an extremely useful data source, given their coverage, transparency and accessibility [ 81 ]. Therefore, patent data are considered by many as the most direct, detailed and objective measures of innovation [ 82 , 83 , 84 ]. In the words of Zvi Griliches [ 80 ], who was one of the first to study technological change empirically, “in this desert of data, patent statistics loom up as a mirage of wonderful plentitude and objectivity.” Especially within biotechnology, patent statistics are a good indicator of the evolution of technology [ 85 , 86 , 87 ].…”

Section: Methodsmentioning

confidence: 99%

A multi-level model of emerging technology: An empirical study of the evolution of biotechnology from 1976 to 2003

Oord¹,

Witteloostuijn²

2018

PLoS ONE

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In this paper, we develop an ecological, multi-level model that can be used to study the evolution of emerging technology. More specifically, by defining technology as a system composed of a set of interacting components, we can build upon the argument of multi-level density dependence from organizational ecology to develop a distribution-independent model of technological evolution. This allows us to distinguish between different stages of component development, which provides more insight into the emergence of stable component configurations, or dominant designs. We validate our hypotheses in the biotechnology industry by using patent data from the USPTO from 1976 to 2003.

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“…When traditional technology roadmapping emphasizes the use of qualitative methodologies, e.g., Delphi/interview (Phaal et al 2004) and TRIZ theory (Moehrle et al 2013), Kostoff et al (1999) headed a direction that exploits technology roadmapping as a bridge to connect bibliometric approaches with technological evolution, and the combination of qualitative and quantitative approaches becomes a trend (Zhang et al 2013). Related approaches include keyword-based analysis (Lee et al 2009;Zhou et al 2014), citation analysis (Choi & Park 2009), subject-action-object-based semantic analysis (Zhang et al 2014b), diffusion modeling (Cunningham & Kwakkel 2014), and the combination of certain above approaches (Li 2015;Guo et al 2016). Further, such approaches have already been applied to a number of industry sectors, in particular emerging sectors, such as electric vehicles (Huang et al 2014), dye-sensitized solar cells (Zhang et al 2014c), energy industry (Daim & Oliver 2008;Kajikawa et al 2008;Daim et al 2017), and 3D printing (Huang et al 2017b).…”

Section: Bibliometric Approaches For Tracing Technological Evolutionmentioning

confidence: 99%

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

Zhang

Huang

Porter

et al. 2019

Technological Forecasting and Social Change

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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.

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A novel three-dimension perspective to explore technology evolution

Cited by 23 publications

References 38 publications

Trends Analysis of Graphene Research and Development

Trends Analysis of Graphene Research and Development

A multi-level model of emerging technology: An empirical study of the evolution of biotechnology from 1976 to 2003

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

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