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Related Experiment Video

Updated: Nov 10, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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Predicting innovative firms using web mining and deep learning.

Jan Kinne1,2,3, David Lenz3,4

  • 1Department of Economics of Innovation and Industrial Dynamics, ZEW Centre for European Economic Research, Mannheim, Germany.

Plos One
|April 1, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for generating web-based innovation indicators to support science, technology, and innovation (STI) policy. The approach efficiently identifies product innovator firms, offering a cost-effective alternative to traditional metrics.

Related Experiment Videos

Last Updated: Nov 10, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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Area of Science:

  • Economics of Innovation
  • Science, Technology, and Innovation (STI) Policy
  • Data Science and Machine Learning

Background:

  • Effective STI policy making relies on accurate innovation indicators for economic growth.
  • Traditional indicators (patents, surveys) suffer from limitations in coverage, timeliness, granularity, and cost.
  • Existing indicators fail to provide a comprehensive view of the innovation system for policymakers and researchers.

Purpose of the Study:

  • To propose and develop a novel approach for generating web-based innovation indicators.
  • To create a scalable and cost-efficient method for identifying product innovator firms.
  • To overcome the limitations of traditional innovation metrics.

Main Methods:

  • Utilized the German Community Innovation Survey for labeled firm data (product innovator/no product innovator).
  • Developed an artificial neural network classification model trained on web text data of surveyed firms.
  • Applied the trained model to a large dataset of German firms' web texts to predict product innovator status.

Main Results:

  • The web-based approach successfully identified product innovator firms at a large scale and low cost.
  • Predictions demonstrated reliability when compared against patent statistics, survey benchmarks, and regional indicators.
  • The method offers significant potential for enhanced coverage and regional granularity in innovation metrics.

Conclusions:

  • The proposed web-based indicator generation method is a valuable and cost-efficient addition to existing innovation metrics.
  • This approach addresses shortcomings of traditional indicators, particularly in terms of coverage, timeliness, and cost.
  • The findings support the use of web data and machine learning for more comprehensive STI policy support.