TL;DR: Digital Layer is an alternative method for analyzing company networks. Unlike previous approaches, it uses digital mass data to identify and analyze business networks. An effectively networked company can draw on a wide range of knowledge, learn from its partners, contribute existing knowledge and thus generate innovations.
Companies that are well-connected are more innovative
Twenty-five years ago, sociologist Manuel Castells coined the term of the Network Society. In this context, society and technological progress are shaped to a large extent by knowledge flows in networks. Those who have access to relevant and valuable information can use it to generate new, more complex knowledge and know-how and are in a better position to compete in the market. This recombination of already existing knowledge into something new is called innovation. For companies, this means that their innovativeness and eventually their competitiveness depend on the extent to which they have access to information and technological know-how via their individual network.
Unlike in the past, nowadays the focus is not only on the immediate physical company location, which enables companies to establish connections and knowledge flows with other local players. In addition to such local business clusters, a company’s embedding in regional, national and international digital networks also plays an increasingly important role in a globalized world. An effectively networked company can draw on a wide range of knowledge, learn from its partners, contribute existing knowledge and thus generate innovations
For many years, academia has been studying how innovative companies network, also in comparison to less innovative companies. Do innovative companies take a more central position in their networks? What is the right mix of local and transregional relationships? To answer these questions, researchers rely on data that map formal and informal connections between companies – and preferably do so dynamically over a longer period of time in order to derive changes and trends.
Digital Layer reveals company networks
For decades, patent data in particular has been used for this purpose. Patent databases allow researchers to identify and analyze links between patenting companies. However, this type of network reflects only a small and very specific part of the overall innovation system. For example, patents are much less relevant to certain sectors of the economy, such as the software industry, than they are to the pharmaceutical industry, for example, and can hardly be used to monitor knowledge flows there.
The ” Digital Layer” approach was proposed by Miriam Krüger and her colleagues as an alternative method for analyzing company networks (Krüger, Miriam, Jan Kinne, David Lenz und Bernd Resch, 2020, The Digital Layer: How Innovative Firms Relate on the Web) and is being further developed by istari.ai. Unlike previous approaches, it uses digital mass data to identify and analyze business networks. Companies, like all other economic actors, are progressively leaving digital traces of their activities. This can be in the form of posts on social media platforms, references in news articles or on the company’s own website. This ” Digital Layer” can be used to identify relationships between companies and examine the networks thus exposed. For example, the figure below shows the locations of more than 600,000 companies in Germany, their degree of innovation (calculated using webAI InnoProb) and about seven million hyperlink connections between their corporate websites.
Image1: Digital Layer for Germany: Locations and InnoProb scores for over 600,000 companies in Germany and Berlin (top); approximately seven million detected hyperlink connections between company websites in Germany and Berlin (middle); national and regional hyperlink network of a single company (bottom). Source: Krüger et al. 2020.
How can companies achieve optimal networking?
Companies link to the websites of other economic players for various reasons. For example, companies often name reference customers on their own websites and then usually also set up a hyperlink to that customer’s website. In their scientific work, Krüger and her colleagues combine these hyperlinks with associated web texts in order to assess the quality of existing hyperlinks as well. “It is not only the existence of a link between two companies that matters, but also the quality of the link” explains Miriam Krüger. “The importance of a partner for a company can depend, for example, on what other connections the company already maintains and whether this new connection expands a company’s portfolio in a useful way.”
One of these criteria, for example, is the potential to learn from each other. In other words, the question of whether new, productive re-combinations of knowledge and technological know-how will emerge from this connection. “In science, this is referred to as the optimal cognitive distance. If the knowledge bases of two actors are too far apart, they could learn a great deal from each other, but in the worst case the distance is too great, so that they do not understand each other. If the knowledge bases are too close, communication might be easy, but the knowledge exchanged hardly differs. So, as is often the case, there is a golden mean,” says Miriam Krüger.
The integration of the Digital Layer approach into webAI – our AI-based system for evaluating web data and providing market and company information in real time – gives our customers a new tool for capturing and analyzing company networks. For example, webAI can be used to identify regional, cross-regional and even international company networks and key players. An example of this can be seen in the figure below, which shows the network links of companies from the German regions of Ostwestfalen-Lippe and Heilbronn. Another use case is the focused analysis of individual company networks, the comparison with networks of other companies, as well as the derivation of target-oriented recommendations for action.
Images: Digital Layer for the regions Heilbronn (top) and East Westphalia-Lippe (bottom)
1 Krüger et al. 2020: http://ftp.zew.de/pub/zew-docs/dp/dp20003.pdf
Authors of this article are Miriam Krüger and Dr. Jan Kinne.