Gloria Puliga

The space economy has historically been synonymous with innovation, with extensive R&D activities and numerous technologies developed in this field extending beyond their original applications, thereby bringing significant benefits to both other industries and people's daily lives. Over the past decades, the domain has experienced significant transformations not only at a technological level but also in its value chain structures and business models. Furthermore, these changes have had a substantial impact on the innovative activity and society, as demonstrated by the diffusion of open innovation and R&D practices and the growth of the adoption of space technologies and patents in non-space sectors. Given the critical role of technological advancements in shaping the space economy and their broader economic and social impact, understanding how the above-mentioned dynamics influence the innovation rate, quality and diffusion (across application domains and geographical areas) is of paramount importance. Therefore, the objective of this paper is to analyse the innovation activities and trends in the space economy. In particular, the research aims to (1) analyse innovative activity in the space economy, covering the entire value chains, and (2) address the innovative activity of companies involved in space activities. First, we analyse 357,945 patents filed between 2000 and 2022 with worldwide coverage to gain a general overview. Secondly, to assess the second objective, we examined the innovative activity of Italian companies involved in the space economy. We analysed 1,033 patents from Italian space companies, providing both quantitative and qualitative assessments. Data collection relied on a taxonomy of the Italian space industry consisting of 496 companies and research institutions. For both worldwide and country-specific levels, we evaluate the innovative activity, enabling the assessment of the innovative dynamics and the connections with other technological and application domains. We also apply the Innovation Patent Index (IPI), a measure of innovation performance representative of innovative capacity, providing a qualitative perspective on innovation in the domain. We validate the results through interviews with senior managers from space and non-space companies involved in the innovation activities. We discuss space innovation rate and quality across different dimensions: longitudinal, territorial (i.e. geographical extensions), space value chains, and technological domains.

To stay abreast of the dynamic market and technological environment, firms should improve their innovation and economic performance continuously. To cope with such requirements, companies often adopt an Open Innovation (OI) strategy. Among several possible OI strategies, co-patenting is a tool to develop innovation with more actors. Thus, companies leverage time by time their innovation network in which each actor represents a node and the knowledge between the companies the link existing between the nodes. The work aims to investigate if and how different innovation networks, foster the innovation and economic performance of firms. An agent-based model and simulator have been developed to investigate the emergence of hubs and the impact on firm performance. The economic and innovation performances are evaluated respectively using turnover and the Innovation Patent Index (IPI). Results show that the presence of hubs in the network helps firms to increase their performance.

Nowadays, industrial firms are increasingly required to develop resilient supply chains to better face turbulent environments by adapting to unforeseen and frequent disruptions. In this regard, researchers strongly agree that fostering innovation toward circular business models can influence resilience capability development. Findings, however, are still fragmented and sparse. To this aim, a systematic literature review of previous studies is conducted. The results of content analyses are presented, and their implications discussed.

Teaching innovation management to engineers is becoming increasingly relevant. However, it can be difficult to involve engineers in a discipline in which technical competences do not represent the core whereas professional and soft skills play a critical role. For this reason, adopting the proper teaching approach is key to capture the students' attention and interest. In our study, we propose a laboratory for teaching innovation based upon two elements that are very closed to the engineering mindset: patents and machine learning algorithms. The laboratory proposes the application of machine learning approaches to patents data, for studying the innovation activity of companies. Three machine learning algorithms, Least Squares, Deep Neural Networks and Decision Trees are exploited. Their application is proposed to capture the relationships between relevant patents output variables (such as, for example, the number of forward citations, as proxy of the company's innovation capability) and the related input features (such as, for example, the number and type of patent technological classes). By practically using this approach, students can be introduced to some relevant topics in innovation management, such as investments, protection, market identification, cumulation of knowledge.

Capabilities and, in particular, Innovation Capability (IC), are fundamental strategic assets for companies in providing and sustaining their competitive advantage. IC is the firms' ability to mobilize and create new knowledge applying appropriate process technologies and it has been investigated by means of its main determinants, usually divided into internal and external factors. In this paper, starting from the patent data, the patent's forward citations are used as proxy of IC and the main patents' features are considered as proxy of the determinants. In details, the main purpose of the paper is to understand the patent's features that are relevant to predict IC. Three different algorithms of machine learning, i.e., Least Squares (RLS), Deep Neural Networks (DNN), and Decision Trees (DT), are employed for this investigation. Results show that the most important patent's features useful to predict IC refer to the specific technological areas, the backward citations, the technological domains and the family size. These findings are confirmed by all the three algorithms used.

Innovation Capability (IC) are important strategic asset used by companies for providing and sustain their competitive advantage. IC is the firm's ability to mobilize and create new knowledge applying appropriate process technologies. The main purpose of the paper is to understand the patent's features that are relevant to predict IC. Starting from patent data, patent's forward citations are used as proxy of IC and the main patents features are considered as proxy of IC determinants. For this analysis, patents issued by firms with registered office in Italy and Sweden or by inventors with residence in Italy or Sweden are investigated, using three different algorithms of machine learning: Least Squares (RLS), Deep Neural Networks (DNN), and Decision Trees (DT). Results are two-fold. First, from a methodological perspective, machine learning approaches are useful in reducing the number of features needed to explain IC, and so in reducing the complexity of the analyses by focusing only on the features with high predictive power. Second, from a managerial standpoint, the study suggests which few, but relevant, variables managers should look at in writing and issuing patents.