Predicting innovative firms using web mining and deep learning


Kinne, Jan ; Lenz, David


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URL: https://madoc.bib.uni-mannheim.de/51168
URN: urn:nbn:de:bsz:180-madoc-511684
Document Type: Working paper
Year of publication: 2019
The title of a journal, publication series: ZEW Discussion Papers
Volume: 19-001
Place of publication: Mannheim
Publication language: English
Institution: Sonstige Einrichtungen > ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung
MADOC publication series: Veröffentlichungen des ZEW (Leibniz-Zentrum für Europäische Wirtschaftsforschung) > ZEW Discussion Papers
Subject: 330 Economics
Abstract: Innovation is considered as a main driver of economic growth. Promoting the development of innovation through STI (science, technology and innovation) policies requires accurate indicators of innovation. Traditional indicators often lack coverage, granularity as well as timeliness and involve high data collection costs, especially when conducted at a large scale. In this paper, we propose a novel approach on how to create firm-level innovation indicators at the scale of millions of firms. We use traditional firm-level innovation indicators from the questionnaire-based Community Innovation Survey (CIS) survey to train an artificial neural network classification model on labelled (innovative/non-innovative) web texts of surveyed firms. Subsequently, we apply this classification model to the web texts of hundreds of thousands of firms in Germany to predict their innovation status. Our results show that this approach produces credible predictions and has the potential to be a valuable and highly cost-efficient addition to the existing set of innovation indicators, especially due to its coverage and regional granularity. The predicted firm-level probabilities can also directly be interpreted as a continuous measure of innovativeness, opening up additional advantages over traditional binary innovation indicators.

Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt.




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Kinne, Jan ; Lenz, David (2019) Predicting innovative firms using web mining and deep learning. Open Access ZEW Discussion Papers Mannheim 19-001 [Working paper]
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