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.
Highlights
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
Given that the vast majority of innovative activity in Germany is conducted by the latter firm type (Rammer et al 2017), we can conclude that our web mining framework is suitable for analyzing the most important business-side parts of the German innovation ecosystem
We proposed a web mining framework for the mapping of innovation ecosystems by generating innovation indicators from website contents
Summary
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 a matter of public interest to measure innovation activities within innovation ecosystems. Firm-level innovation is often measured by means of indicators constructed using data from large-scale questionnaire-based surveys Examples of such surveys include the Oslo Manual-based (OECD and Eurostat 2018) biennial European Community Innovation Survey (CIS) and the annual Mannheim Innovation Panel (MIP), which constitutes the German contribution to the CIS. Both surveys provide firm-level information about innovative and non-innovative enterprises as well as their R&D expenditures. Voluntary surveys like the MIP suffer from uncompleted questionnaires and the desired information is not always accessible (Kleinknecht et al 2002)
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