Identifying the Determinants of Innovation Capability With Machine Learning and Patents

Abstract

Innovation capability (IC) is a fundamental firms’ strategic asset to sustain competitive advantage. In this article, relying on patent data, patents forward citations are used as proxy of IC and the main patents numerical and categorical variables are considered as proxy of IC determinants. The main purpose of this article is to understand which patents features are relevant to predict IC, i.e., which are the determinants of IC within patents. Three different algorithms of machine learning, widely applied to model real-world phenomena, regularized least squares, deep neural networks, and random forest are applied for this investigation. Results show that the most important patent features useful to predict IC refer to internal determinants, such as the technological scope of the company (technological domains and International Patent Classification classes), the backward citations, and technical concepts. Some external variables are also relevant, such as the family size, a time-related variable, and the timespan between the youngest and oldest patent of the family. Implications from this article concern two perspectives. From a methodological perspective, the study shows the usefulness of machine learning approaches in simplifying the decision-making process as they reduce the number of variables to be considered to investigate the company’s IC and they provide accurate and easily interpretable result. From a managerial standpoint, this article points out the few and relevant patent variables to be considered when dealing with patents and IC.