Anticipating Future Innovation Pathways Through Large Data Analysis

  • Tugrul U. Daim
  • Denise Chiavetta
  • Alan L. Porter
  • Ozcan Saritas

Part of the Innovation, Technology, and Knowledge Management book series (ITKM)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Data Science/Technology Review

  3. Text Analytic Methods

    1. Front Matter
      Pages 97-97
    2. Christopher L. Benson, Christopher L. Magee
      Pages 119-131
    3. Cherie Courseault Trumbach, Christopher McKesson, Parisa Ghandehari, Lawrence DeCan, Owen Eslinger
      Pages 133-151
    4. Ying Huang, Yi Zhang, Jing Ma, Alan L. Porter, Xuefeng Wang, Ying Guo
      Pages 153-172
    5. Lu Huang, Lining Shang, Kangrui Wang, Alan L. Porter, Yi Zhang
      Pages 173-186
    6. Hongshu Chen, Yi Zhang, Donghua Zhu
      Pages 187-209
    7. Victoria Kayser, Erduana Shala
      Pages 229-245
  4. Anticipating the Future-Cases and Frameworks

    1. Front Matter
      Pages 247-247
    2. Marisela Rodríguez Salvador, Ana Marcela Hernández de Menéndez, David Alfredo Arcos Novillo
      Pages 249-271
    3. Tugrul U. Daim, Monticha Khammuang, Edwin Garces
      Pages 273-302
    4. Cristina d’Urso de Souza Mendes, Adelaide Maria de Souza Antunes
      Pages 303-320

About this book

Introduction

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 self-satisfied 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. The accumulation of Tech Mining research over the past decade offers a rich resource of means to get at emerging technology developments and organizational networks to date.  Efforts to bridge from those recent histories of development to project likely FIP, however, prove considerably harder. One focus of this volume is to extend the repertoire of information resources; that will enrich FIP.

Featuring cases of novel approaches and applications of Tech Mining and FIP, this volume will present frontier advances in ST&I text analytics that will be of  interest to students, researchers, practitioners, scholars and policy makers in the fields of R&D planning, technology management, science policy and innovation strategy.

Keywords

Big Data Competitive Technical Intelligence Future-Oriented Technology Analysis Innovation Management Tech Mining Technology Assessment Technology Forecasting Text Analytics Text Mining

Editors and affiliations

  • Tugrul U. Daim
    • 1
  • Denise Chiavetta
    • 2
  • Alan L. Porter
    • 3
  • Ozcan Saritas
    • 4
  1. 1.Dept. of Engineering and Technology ManaPortland State UniversityPortlandUSA
  2. 2.Search Technology, IncNorcrossUSA
  3. 3.Georgia Institute of TechnologyRoswellUSA
  4. 4.Higher School of EconomicsNational Research UniversityMoscowRussia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-39056-7
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-39054-3
  • Online ISBN 978-3-319-39056-7
  • Series Print ISSN 2197-5698
  • Series Online ISSN 2197-5701
  • About this book