3D Printing - A patent overview (November 2013)
Graphene - The worldwide patent landscape in 2013 (March 2013)
Alan L. Porter and Scott W. Cunningham
Tech Mining makes exploitation of text databases meaningful to those who can gain from derived knowledge about emerging technologies. It begins with the premise that we have the information, the tools to exploit it, and the need for the resulting knowledge.
The information provided puts new capabilities at the hands of technology managers. Using the material present, these managers can identify and access the most valuable technology information resources (publications, patents, etc.); search, retrieve, and clean the information on topics of interest; and lower the costs and enhance the benefits of competitive technological intelligence operations.
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Description: Published in 1991, the first edition of Forecasting and Management of Technology was one of the leading handful of books to deal with the topic of forecasting of technology and technology management as this discipline was emerging. The new, revised edition of this book will build on this knowledge in the context of business organizations that now place a greater emphasis on technology to stay on the cutting edge of development. The scope of this edition has broadened to include management of technology content that is relevant to now to executives in organizations while updating and strengthening the technology forecasting and analysis content that the first edition is reputed for.
Updated by the original author team, plus new author Scott Cunningham, the book takes into account what the authors see as the innovations to technology management in the last 17 years: the Internet; the greater focus on group decision-making including process management and mechanism design; and desktop software that has transformed the analytical capabilities of technology managers. Included in this book will be 5 case studies from various industries that show how technology management is applied in the real world.
by: Alan L. Porter and Yi Zhang
The Millenium Project provides a well-coordinated set of methods chapters on various aspects of futures research [https://themp.org/]. We have expanded our chapter on “TECH MINING of Science & Technology Information Resources for Future-oriented Technology Analyses” in March, 2015. The chapter overviews tech mining and spotlights three case analyses:
Volume 48, Issue 9, November 2019
Seokbeom Kwon, Xiaoyu Liu, Alan L. Porter, and Jan Youtie
Abstract - 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.
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Technological Forecasting and Social Change
Volume 146, September 2019, Pages 628-643
Alan L. Porter, Jon Garner, Stephen F. Carley, and Nils C.Newman
Search Technology, Inc., Norcross, GA, USA
Abstract - 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.
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Alan L. Porter and Nils C. Newman, Search Technology, Inc. (CIMS Technology Management Report, pp 17-19, Spring 2011)
"Tech mining is an essential tool for enabling open innovation," wrote Alan L. Porter in the Spring 2011 CIMS Technology Management Report. He detailed in that article how Tech Mining can help managers in the biotech industry search research publications for answers to "who, what,when,and where?" questions. Porter and his Search Technology, Inc, colleague Nils C. Newman cull the literature for examples of tech mining successes outside of strictly academic research. They illustrate the progress being made in applying tech mining more broadly, and also point toward applying these capabilities to the identification of potential technology innovation pathways.
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