At the Science, Business and Innovation department at VU Amsterdam, students frequently need to assess the strategies of various high tech firms. In this post, I will outline a basic toolkit that academic researchers can use to draw and analyze two basic networks of a company – knowledge and collaboration network. Collaboration network refers to explicit partnerships that members of a firm have with other institutions. The collaboration network is usually obtained from looking at the co-authorship in a firm’s works. Meanwhile, knowledge network is related to the sources of knowledge that a firm uses in its own innovation. This knowledge network can be traced by looking at the citations of a company’s output. The main difference between the two networks is that a company does not have to formally partner with another organization in order to learn from it, rather it can also do so by tracking the other company’s activities or through informal social networks. This form of learning is not manifested through co-authorships but through citations. By analyzing the citation network, we can see whether this knowledge relationship is one-sided or whether both companies cite each other’s works.
In order to draw the various networks of a high tech firm, the first step is simply to look at the company website. It usually has tons of information about a company already. It shows its founders, its services and perhaps even its collaborations. With basic company information known, it is now possible to draw various network maps either by looking at the firm’s patents or publications.
One of the things I would check first, especially for a high tech startup is the publication set of the company. High tech startups publish due to a variety of reasons, such as for marketing, sometimes using the publication as a signal to investors that the company is innovative. Moreover, if a company is a pioneer in a field, publishing can help it gain legitimacy for the emerging field that it is part of. Using the Web of Science or Scopus, one could do a basic search of the firm name. In Web of Science’s advanced search, you could use the tags OO for organization, OG for organization-enhanced and AD for address. I prefer to use the address tag as the database’s preprocessing algorithm can sometimes modify the name of companies. However, the problem might be that you would not be able to find any publication because the company has just kicked off and thus, has not carried out any activity under its own name. In such cases, especially for academic spinoffs, you can resort to searching the founders’ names. For many startups based in academia, the founder might still be affiliated with the university, causing most of the company’s publications still tied to the originating university’s name.
The other logical thing to search would be patents. I have found the Patentsview platform covering the US PTO to be a very reliable source for patents. Having an API feature allows automatized downloading of patents from the website (you just need however to read the documentation found in the website). Same comment with the publications, if the patents cannot be found through company search, sometimes they might be registered under the university or under just the founder’s name.
Through these two methods, various interesting analyses can be carried out. To draw the knowledge network, I would look at the cited works of the publications/patents of the company. For publications, this can easily be done through the cited works/authors feature in VosViewer. For patents, however, preprocessing should be done to format the cited works, which can be fed to programs such as VosViewer / Gephi / Pajek.
To draw the collaboration network, we have to look at the co-authorships of publications or patents. Once again, this can easily be carried out with VosViewer for publications but preprocessing should be done for patents.