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Web mining for innovation ecosystem mapping: a framework and a large-scale pilot study

Author

Listed:
  • Jan Kinne

    (ZEW – Leibniz Centre for European Economic Research
    University of Salzburg
    Harvard University
    istari.ai)

  • Janna Axenbeck

    (ZEW - Leibniz Centre for European Economic Research
    Justus-Liebig-University)

Abstract

Existing approaches to model innovation ecosystems have been mostly restricted to qualitative and small-scale levels or, when relying on traditional innovation indicators such as patents and questionnaire-based survey, suffered from a lack of timeliness, granularity, and coverage. Websites of firms are a particularly interesting data source for innovation research, as they are used for publishing information about potentially innovative products, services, and cooperation with other firms. Analyzing the textual and relational content on these websites and extracting innovation-related information from them has the potential to provide researchers and policy-makers with a cost-effective way to survey millions of businesses and gain insights into their innovation activity, their cooperation, and applied technologies. For this purpose, we propose a web mining framework for consistent and reproducible mapping of innovation ecosystems. In a large-scale pilot study we use a database with 2.4 million German firms to test our framework and explore firm websites as a data source. Thereby we put particular emphasis on the investigation of a potential bias when surveying innovation systems through firm websites if only certain firm types can be surveyed using our proposed approach. We find that the availability of a websites and the characteristics of the website (number of subpages and hyperlinks, text volume, language used) differs according to firm size, age, location, and sector. We also find that patenting firms will be overrepresented in web mining studies. Web mining as a survey method also has to cope with extremely large and hyper-connected outlier websites and the fact that low broadband availability appears to prevent some firms from operating their own website and thus excludes them from web mining analysis. We then apply the proposed framework to map an exemplary innovation ecosystem of Berlin-based firms that are engaged in artificial intelligence. Finally, we outline several approaches how to transfer firm website content into valuable innovation indicators.

Suggested Citation

  • Jan Kinne & Janna Axenbeck, 2020. "Web mining for innovation ecosystem mapping: a framework and a large-scale pilot study," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2011-2041, December.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:3:d:10.1007_s11192-020-03726-9
    DOI: 10.1007/s11192-020-03726-9
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    References listed on IDEAS

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    Cited by:

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    2. MOTOHASHI Kazuyuki, 2023. "Identifying Technology Opportunity Using a Dual-attention Model and a Technology-market Concordance Matrix," Discussion papers 23024, Research Institute of Economy, Trade and Industry (RIETI).
    3. Axenbeck, Janna & Breithaupt, Patrick, 2022. "Measuring the digitalisation of firms: A novel text mining approach," ZEW Discussion Papers 22-065, ZEW - Leibniz Centre for European Economic Research.
    4. Rammer, Christian & Es-Sadki, Nordine, 2023. "Using big data for generating firm-level innovation indicators - a literature review," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    5. Occhini, Giulia & Tranos, Emmanouil & Wolf, Levi John, 2023. "Measuring a country’s digital industrial structure: commercial websites and weakly supervised classification to the rescue," SocArXiv h572n, Center for Open Science.
    6. Chenxi Liu & Zhenghong Peng & Lingbo Liu & Shixuan Li, 2023. "Innovation Networks of Science and Technology Firms: Evidence from China," Land, MDPI, vol. 12(7), pages 1-21, June.
    7. Bahoo, Salman & Cucculelli, Marco & Qamar, Dawood, 2023. "Artificial intelligence and corporate innovation: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    8. Anita Thonipara & Rolf Sternberg & Till Proeger & Lukas Haefner, 2023. "Digital divide, craft firms’ websites and urban-rural disparities—empirical evidence from a web-scraping approach [Digital Divide, Websites von Handwerksunternehmen und städtisch-ländliche Disparit," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 43(1), pages 69-99, April.
    9. Axenbeck, Janna & Bertschek, Irene & Breithaupt, Patrick & Erdsiek, Daniel, 2023. "Firm digitalisation and mobility - Do Covid-19-related changes persist?," ZEW Discussion Papers 23-011, ZEW - Leibniz Centre for European Economic Research.
    10. Schmidt, Sebastian & Kinne, Jan & Lautenbach, Sven & Blaschke, Thomas & Lenz, David & Resch, Bernd, 2022. "Greenwashing in the US metal industry? A novel approach combining SO2 concentrations from satellite data, a plant-level firm database and web text mining," ZEW Discussion Papers 22-006, ZEW - Leibniz Centre for European Economic Research.
    11. Schubert, Torben & Ashouri, Sajad & Deschryvere, Matthias & Jäger, Angela & Visentin, Fabiana & Cunningham, Scott & Hajikhani, Arash & Pukelis, Lukas & Suominen, Arho, 2023. "The role of product digitization for productivity," MERIT Working Papers 2023-004, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    12. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    13. Motohashi, Kazuyuki & Zhu, Chen, 2023. "Identifying technology opportunity using dual-attention model and technology-market concordance matrix," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    14. Mazzoni Leonardo & Pinelli Fabio & Riccaboni Massimo, 2023. "Measuring Corporate Digital Divide with web scraping: Evidence from Italy," Papers 2301.04925, arXiv.org.
    15. Julian Schwierzy & Robert Dehghan & Sebastian Schmidt & Elisa Rodepeter & Andreas Stoemmer & Kaan Uctum & Jan Kinne & David Lenz & Hanna Hottenrott, 2022. "Technology Mapping Using WebAI: The Case of 3D Printing," Papers 2201.01125, arXiv.org.
    16. Dörr, Julian Oliver & Kinne, Jan & Lenz, David & Licht, Georg & Winker, Peter, 2021. "An integrated data framework for policy guidance in times of dynamic economic shocks," ZEW Discussion Papers 21-062, ZEW - Leibniz Centre for European Economic Research.

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    More about this item

    Keywords

    Web mining; Web scraping; Innovation;
    All these keywords.

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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