ALBERT

Description: 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.

Description: Evidence-based STI (science, technology, and innovation) policy making requires accurate indicators of innovation in order to promote economic growth. However, traditional indicators from patents and questionnaire-based surveys often lack coverage, granularity as well as timeliness and may involve high data collection costs, especially when conducted at a large scale. Consequently, they struggle to provide policy makers and scientists with the full picture of the current state of the innovation system. In this paper, we propose a first approach on generating web-based innovation indicators which may have the potential to overcome some of the shortcomings of traditional indicators. Specifically, we develop a method to identify product innovator firms at a large scale and very low costs. We use traditional firm-level indicators from a questionnaire-based innovation survey (German Community Innovation Survey) to train an artificial neural network classification model on labelled (product innovator/no product innovator) web texts of surveyed firms. Subsequently, we apply this classification model to the web texts of hundreds of thousands of firms in Germany to predict whether they are product innovators or not. We then compare these predictions to firm-level patent statistics, survey extrapolation benchmark data, and regional innovation indicators. The results show that our approach produces reliable predictions and has the potential to be a valuable and highly cost-efficient addition to the existing set of innovation indicators, especially due to its coverage and regional granularity.