Purpose: The measure of companies' Innovation Performance is fundamental for enhancing the value and decision-making processes of firms. The purpose of this paper is to present a new measure of Innovation Performance, called Innovation Patent Index (IPI), which makes it possible to quantitatively summarize different aspects of firms' innovation.Design/methodology/approach: In order to define the IPI, a secondary source, i.e. patent data, has been used. The five dimensions of IPI, i.e. efficiency, time, diversification, quality and internationalization have been defined both analyzing the literature and applying three different machine learning algorithms (regularized least squares, deep neural networks and decision trees), considering patent forward citations as a proxy of the innovation performance.Findings: Results show that the IPI index is a very useful tool, simple to use and very promptly. In fact, it is possible to get important results without making time consuming analysis with primary sources. It is a tool that can be used by managers, businessmen, policymakers, organizations, patent experts and financiers to evaluate and plan future activities, to enhance the innovation capability, to find financing and to support and improve innovation.Research limitations/implications: Patent data are not widely used in all the sectors. Moreover, the pure number of forward citations is not the only forward looking indicator suggested by the literature.Originality/value: The demand for a useable Innovation Performance tool, as well as the lack of tools able to grasp different aspects of the innovation, highlight the need to develop new instruments. In fact, although previous studies provide several measures of Innovation Performance, these are often difficult for managers to use, do not appreciate different aspects of the innovation and are not forward looking.

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.