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Web mining of firm websites: A framework for web scraping and a pilot study for Germany

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  • Kinne, Jan
  • Axenbeck, Janna

Abstract

Nowadays, almost all (relevant) firms have their own websites which they use to publish information about their products and services. Using the example of innovation in firms, we outline a framework for extracting information from firm websites using web scraping and data mining. For this purpose, we present an easy and free-to-use web scraping tool for large-scale data retrieval from firm websites. We apply this tool in a large-scale pilot study to provide information on the data source (i.e. the population of firm websites in Germany), which has as yet not been studied rigorously in terms of its qualitative and quantitative properties. We find, inter alia, that the use of websites and websites' characteristics (number of subpages and hyperlinks, text volume, language used) differs according to firm size, age, location, and sector. Web-based studies also have to contend with distinct outliers and the fact that low broadband availability appears to prevent firms from operating a website. Finally, we propose two approaches based on neural network language models and social network analysis to derive firm-level information from the extracted web data.

Suggested Citation

  • 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.
    • Handle: RePEc:zbw:zewdip:18033
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    9. Rammer, Christian & Kinne, Jan & Blind, Knut, 2016. "Microgeography of innovation in the city: Location patterns of innovative firms in Berlin," ZEW Discussion Papers 16-080, ZEW - Leibniz Centre for European Economic Research.
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    Cited by:

    1. Proeger, Till & Meub, Lukas & Pölert, Hauke, 2021. "Analyse des Digitalisierungsgrads von Bildungseinrichtungen auf Basis von Webscraping - eine methodische Vorstudie," Göttinger Beiträge zur Handwerksforschung 56, Volkswirtschaftliches Institut für Mittelstand und Handwerk an der Universität Göttingen (ifh).
    2. Böhmecke-Schwafert, Moritz & García Moreno, Eduardo, 2023. "Exploring blockchain-based innovations for economic and sustainable development in the global south: A mixed-method approach based on web mining and topic modeling," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    3. Abbasiharofteh, Milad & Kinne, Jan & Krüger, Miriam, 2021. "The strength of weak and strong ties in bridging geographic and cognitive distances," ZEW Discussion Papers 21-049, 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. Proeger, Till & Meub, Lukas, 2022. "Innovative Betriebe und Innovationsmuster im Hamburger Handwerk," ifh Forschungsberichte 7, Volkswirtschaftliches Institut für Mittelstand und Handwerk an der Universität Göttingen (ifh).
    6. Kinne, Jan & Krüger, Miriam & Lenz, David & Licht, Georg & Winker, Peter, 2020. "Coronavirus pandemic affects companies differently: A high-frequency website analysis of companies' reactions to the coronavirus pandemic in Germany," ZEW Expert Briefs 20-05e, ZEW - Leibniz Centre for European Economic Research.
    7. German Data Forum RatSWD (ed.), 2020. "Big data in social, behavioural, and economic sciences: Data access and research data management," RatSWD Output Series, German Data Forum (RatSWD), volume 6, number 6-4en.
    8. Janna Axenbeck & Patrick Breithaupt, 2021. "Innovation indicators based on firm websites—Which website characteristics predict firm-level innovation activity?," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-23, April.
    9. Meub, Lukas & Proeger, Till & Bizer, Kilian, 2022. "Vernetzung von Unternehmen und Forschungseinrichtungen in regionalen Innovationssystemen durch Webscraping," Göttinger Beiträge zur Handwerksforschung 62, Volkswirtschaftliches Institut für Mittelstand und Handwerk an der Universität Göttingen (ifh).
    10. Jan Kinne & David Lenz, 2021. "Predicting innovative firms using web mining and deep learning," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-18, April.
    11. 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.
    12. Proeger, Till & Meub, Lukas & Bizer, Kilian, 2021. "Webscraping als Instrument zur tagesaktuellen und umfassenden digitalen Analyse des Handwerks," Göttinger Beiträge zur Handwerksforschung 55, Volkswirtschaftliches Institut für Mittelstand und Handwerk an der Universität Göttingen (ifh).
    13. Meub, Lukas & Proeger, Till & Fuhrich, Svenja & Ullrich, Matthias & Bizer, Kilian, 2023. "Zukunftsfelder für Smart City: Eine Webscraping-Analyse von Betrieben und Organisationen der Landkreise Hildesheim, Peine und der Region Hannover," ifh Forschungsberichte 15, Volkswirtschaftliches Institut für Mittelstand und Handwerk an der Universität Göttingen (ifh).

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    More about this item

    Keywords

    Web Mining; Web Scraping; R&D; R&I; STI; Innovation; Indicators; Text Mining;
    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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