Web mining for innovation ecosystem mapping: a framework and a large-scale pilot study
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Web mining for innovation ecosystem mapping: a framework and a large‑scale pilot study Jan Kinne1,2,3,4 · Janna Axenbeck5,6 Received: 8 November 2019 © The Author(s) 2020
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. Keywords Web mining · Web scraping · Innovation JEL Classification O30 · C81 · C88
* Jan Kinne [email protected] Extended author information available on the last page of the article
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Scientometrics
Introduction The disruptive force of radical innovation has the ability to reshape the economy and pave the way for new periods of long-term economic growth, while incremental innovation causes continuous change. It is therefore a matter of public interest to measure innovation activities within innovation ecosystems. Measuring these innovation activities to a sufficient degree of accuracy allows researchers to analyze a system’s driving factors as well as the effectiveness of innovation policies. However, there is evidence that traditional indicators of i
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