Measuring Technology Complementarity Between Enterprises With an hLDA Topic Model

When considering a joint venture or merger, it is essential for firms to explore innovators with complementary technology to compensate for any internal limitations in R&D resources. In this article, we provide a framework for exploring the technology complementarity between enterprises in a quantitative manner based on text-mining patent data. A hierarchical latent Dirichlet allocation topic model identifies the technology topics hidden in patent documents along with the hierarchical structure of those topics. The technology complementarity between broad classes of technology and their subclassifications across enterprises is then measured with an improved formulation. An empirical study on three-dimensional printing illustrates the validity, reliability, and practicality of this method and the measurement formula used, endorsed by technical experts. This method can be used to identify R&D opportunities, to find appropriate acquisition targets and potential collaborators, and to support managerial decision-making with quantified information on technology complementarity.