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Книга

Anticipating Future Innovation Pathways Through Large Data Analysis

Netherlands: Springer, 2016.
Под общей редакцией: T. Daim, D. Chiavetta, A. Porter, O. Saritas.

This book aims to identify promising future developmental opportunities and applications for Tech Mining. Specifically, the enclosed contributions will pursue three converging themes:

  • The increasing availability of electronic text data resources relating to Science, Technology & Innovation (ST&I)
  • The multiple methods that are able to treat this data effectively and incorporate means to tap into human expertise and interests
  • Translating those analyses to provide useful intelligence on likely future developments of particular emerging S&T targets.

Tech Mining can be defined as text analyses of ST&I information resources to generate Competitive Technical Intelligence (CTI). It combines bibliometrics and advanced text analytic, drawing on specialized knowledge pertaining to ST&I. Tech Mining may also be viewed as a special form of “Big Data” analytics because it searches on a target emerging technology (or key organization) of interest in global databases. One then downloads, typically, thousands of field-structured text records (usually abstracts), and analyses those for useful CTI.  Forecasting Innovation Pathways (FIP) is a methodology drawing on Tech Mining plus additional steps to elicit stakeholder and expert knowledge to link recent ST&I activity to likely future development.  A decade ago, we demeaned Management of Technology (MOT) as somewhat selfsatisfied and ignorant.  Most technology managers relied overwhelmingly on casual human judgment, largely oblivious of the potential of empirical analyses to inform R&D management and science policy.  CTI, Tech Mining, and FIP are changing that.

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Anticipating Future Innovation Pathways Through Large Data Analysis