Junho Yoon

In contrast with the accelerating trend of boundary-spanning (horizontal) technological innovation, the current Cooperative Patent Classification (CPC) scheme applies a hierarchical (vertical) structure to innovation output in terms of patents. For this reason, we argue that the CPC can be complemented with dynamic technological innovation system (TIS) discovery through machine learning that accounts for horizontal relationships across seemingly disparate technologies. Using a design science approach, we propose a framework to discover boundary-spanning TISs by leveraging the textual information from millions of patents. We validate our framework in terms of the ability of discovered relationships to predict future innovation quantity and quality in different technology classes. Our novel TIS-based innovation metrics that leverage patenting activity in related technology classes are significantly associated with future innovation intensity in focal technologies. We conduct experiments with machine learning models to further tease out the predictive utility of our TIS discovery framework.

Interfirm alliances in high-tech industries are a synergistic resource-sharing mechanism that can lead to efficient innovation. As an initial critical step in forming alliances, firms conduct partner search, which involves finding other firms that possess complementary technological resources and capabilities. This study proposes an automated alliance prediction framework that can simplify partner search as well as provide valuable intelligence to third parties such as analysts and investors. Our graph neural network-based framework utilizes a key alliance theory on relational pluralism, which refers to multitudes of relationships that exist between firms that can provide an understanding of how firms interact and collaborate. To operationalize our prediction framework, we compile a rigorous firm-level network data set derived from 8,739 alliances between 11,499 firms in 11 high-tech industries through the period of 1990-2018. Our theory-driven predictive models incorporate multiple innovation-based relations—interfirm collaborations, human capital flow, and knowledge spillovers—into graph neural networks (GNNs). Our prediction results show that the plurality of interfirm relations collectively contributes to superior predictive performance across varying evaluation metrics, highlighting the practical utility of our predictive models as a partner search instrument. We also discuss the stakeholders who can benefit from our prediction framework in practice, as well as several contributions to the alliance literature.