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Web-based innovation indicators: Which firm website characteristics relate to firm-level innovation activity?

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  • Axenbeck, Janna
  • Breithaupt, Patrick

Abstract

Web-based innovation indicators may provide new insights into firm-level innovation activities. However, little is known yet about the accuracy and relevance of web-based information. In this study, we use 4,485 German firms from the Mannheim Innovation Panel (MIP) 2019 to analyze which website characteristics are related to innovation activities at the firm level. Website characteristics are measured by several text mining methods and are used as features in different Random Forest classification models that are compared against each other. Our results show that the most relevant website characteristics are the website's language, the number of subpages, and the total text length. Moreover, our website characteristics show a better performance for the prediction of product innovations and innovation expenditures than for the prediction of process innovations.

Suggested Citation

  • Axenbeck, Janna & Breithaupt, Patrick, 2019. "Web-based innovation indicators: Which firm website characteristics relate to firm-level innovation activity?," ZEW Discussion Papers 19-063, ZEW - Leibniz Centre for European Economic Research.
    • Handle: RePEc:zbw:zewdip:19063
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    References listed on IDEAS

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    1. Kinne, Jan & Lenz, David, 2019. "Predicting innovative firms using web mining and deep learning," ZEW Discussion Papers 19-001, ZEW - Leibniz Centre for European Economic Research.
    2. Bertschek, Irene & Kesler, Reinhold, 2022. "Let the user speak: Is feedback on Facebook a source of firms’ innovation?," Information Economics and Policy, Elsevier, vol. 60(C).
    3. Becker, Wolfgang & Dietz, Jurgen, 2004. "R&D cooperation and innovation activities of firms--evidence for the German manufacturing industry," Research Policy, Elsevier, vol. 33(2), pages 209-223, March.
    4. David Lenz & Peter Winker, 2020. "Measuring the diffusion of innovations with paragraph vector topic models," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.
    5. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    6. Gerard Hoberg & Gordon Phillips, 2016. "Text-Based Network Industries and Endogenous Product Differentiation," Journal of Political Economy, University of Chicago Press, vol. 124(5), pages 1423-1465.
    7. Sanjay K. Arora & Jan Youtie & Philip Shapira & Lidan Gao & TingTing Ma, 2013. "Entry strategies in an emerging technology: a pilot web-based study of graphene firms," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(3), pages 1189-1207, June.
    8. J Sylvan Katz & Viv Cothey, 2006. "Web indicators for complex innovation systems," Research Evaluation, Oxford University Press, vol. 15(2), pages 85-95, August.
    9. Bersch, Johannes & Gottschalk, Sandra & Müller, Bettina & Niefert, Michaela, 2014. "The Mannheim Enterprise Panel (MUP) and firm statistics for Germany," ZEW Discussion Papers 14-104, ZEW - Leibniz Centre for European Economic Research.
    10. Abdullah Gök & Alec Waterworth & Philip Shapira, 2015. "Use of web mining in studying innovation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 653-671, January.
    11. Max Nathan & Anna Rosso, 2017. "Innovative events," Development Working Papers 429, Centro Studi Luca d'Agliano, University of Milano, revised 08 Apr 2019.
    12. Stefan Lachenmaier & Ludger Wößmann, 2006. "Does innovation cause exports? Evidence from exogenous innovation impulses and obstacles using German micro data," Oxford Economic Papers, Oxford University Press, vol. 58(2), pages 317-350, April.
    13. Kinne, Jan & Axenbeck, Janna, 2018. "Web mining of firm websites: A framework for web scraping and a pilot study for Germany," ZEW Discussion Papers 18-033, ZEW - Leibniz Centre for European Economic Research.
    14. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    15. Bruno Cassiman & Elena Golovko, 2011. "Innovation and internationalization through exports," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 42(1), pages 56-75, January.
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    Cited by:

    1. Max Nathan & Anna Rosso, 2017. "Innovative events," Development Working Papers 429, Centro Studi Luca d'Agliano, University of Milano, revised 08 Apr 2019.
    2. Nathan, Max & Rosso, Anna, 2022. "Innovative events: product launches, innovation and firm performance," Research Policy, Elsevier, vol. 51(1).
    3. Daniel Feser, 2023. "Innovation intermediaries revised: a systematic literature review on innovation intermediaries’ role for knowledge sharing," Review of Managerial Science, Springer, vol. 17(5), pages 1827-1862, July.
    4. Mazzoni Leonardo & Pinelli Fabio & Riccaboni Massimo, 2023. "Measuring Corporate Digital Divide with web scraping: Evidence from Italy," Papers 2301.04925, arXiv.org.

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    More about this item

    Keywords

    text as data; innovation indicators; machine learning;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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