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Innovation Capability of Firms: A Big Data Approach with Patents

  • Linda PontaEmail author
  • Gloria Puliga
  • Luca Oneto
  • Raffaella Manzini
Conference paper
Part of the Proceedings of the International Neural Networks Society book series (INNS, volume 1)

Abstract

Capabilities and, in particular, Innovation Capability (IC), are fundamental strategic assets for companies in providing and sustaining their competitive advantage. IC is the firms’ ability to mobilize and create new knowledge applying appropriate process technologies and it has been investigated by means of its main determinants, usually divided into internal and external factors. In this paper, starting from the patent data, the patent’s forward citations are used as proxy of IC and the main patents’ features are considered as proxy of the determinants. In details, the main purpose of the paper is to understand the patent’s features that are relevant to predict IC. Three different algorithms of machine learning, i.e., Least Squares (RLS), Deep Neural Networks (DNN), and Decision Trees (DT), are employed for this investigation. Results show that the most important patent’s features useful to predict IC refer to the specific technological areas, the backward citations, the technological domains and the family size. These findings are confirmed by all the three algorithms used.

Keywords

Innovation Capability Patents’ data Least Squares Deep Neural Networks Decision Trees 

Notes

Acknowledgment

This work has been supported by LIUC - Cattaneo University under Grant “Data Analytics”.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Linda Ponta
    • 1
    Email author
  • Gloria Puliga
    • 1
  • Luca Oneto
    • 2
  • Raffaella Manzini
    • 1
  1. 1.LIUC Cattaneo UniversityCastellanzaItaly
  2. 2.DIBRIS - University of GenoaGenovaItaly

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