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In the intellectual capital literature, no studies have examined the causal relationship between Italian Fintech companies' performance and intellectual capital, especially the impact of digital industrialization on human capital. This paper aims to fill this gap in measuring human capital efficiency in the Italian Fintech market.

The authors adopt Pulic's model and define the intellectual capital through three components (human capital, structural capital and capital employed) and perform an exploratory analysis of the Italian Fintech companies by using principal component analysis. Then the authors investigate the effects of the intellectual capital and its components on the Italian Fintech companies' performance by using parametric and nonparametric regression models.

Results of regression models reveal that human capital and employed capital are positively related to the companies' performance, except for the structural capital.

The study focuses on the Italian level, and future research could be extended to different European countries or to the global Fintech market. Moreover, it is advised to explore more components that contribute to intellectual capital measurement inside the companies operating in the 4.0 industrial revolution, such as the innovative capital and the relational capital.

This study proposes a new vision for managerial procedures to find which features are critical for achieving profitability in this digital era. The study offers interesting reflections on the management decisions for both companies and public decision-makers. Results suggest that, among intellectual capital components, human capital plays a strategic role for the knowledge-intensive companies that are interested in potentiating their performance and competitiveness. Furthermore, this study finds that human capital is critical factor for achieving profitability in this digital era.

The Fintech sector is one that most benefited from the Digital Revolution, and if it is adequately managed, it can bring great benefits in terms of major employment, especially for the young population, and bring major financial inclusiveness all over the world.

This is the first study that examines the Italian Fintech market and analyzes the dependence relationship between companies' performance and intellectual capital components, identifying the role of human capital in a new completely digital sector. The analysis findings are strategic for the business decisions-making process.

This paper aims at investigating the multifaceted nature of innovation networks by focusing on two research questions: Do cluster actors exchange only one type of innovation-related knowledge? Do cluster actors play different roles in innovation-related knowledge exchange?

This paper builds on data collected at the firm level in an Italian aerospace cluster, that is a technology-intensive industry where innovation is at the base of local competitiveness. A questionnaire was used to collect both attribute data and relational data concerning collaboration and the flows of knowledge in innovation networks. The authors distinguished among three types of knowledge (technological, managerial and market knowledge) and five types of brokerage roles (coordinator, gatekeeper, liaison, representative and consultant). Data analysis relied on social network analysis techniques and software.

Concerning the first research question, the findings show that different types of knowledge flow in different ways in innovation networks. The different types of knowledge are unevenly exchanged. The exchange of technological knowledge is open to everyone in the cluster. The exchange of market and managerial knowledge is selective. Concerning the second research question, the authors suggest that different types of cluster actors (large firms, small- and medium-sized enterprises, research centers and universities and institutions for collaboration) do play different roles in innovation networks, especially with reference to the three types of knowledge considered in this study.

The present paper has some limitations. First of all, the analysis focuses on just one cluster (one industry in one specific location), cross- and comparative analyses with other clusters may illuminate the findings better, eliminating industry and geographical biases. Second, the paper focuses only on innovation-related knowledge exchanges within the cluster and not across it.

The results have practical implications both for policy makers and for managers. First, this research stresses how innovation often originates from a combination of different knowledge types acquired through the collaboration with heterogeneous cluster actors. Further, the analysis of brokerage roles in innovation-driven collaborations may help policy makers in designing programs for knowledge-transfer partnerships among the various actors of a cluster.

The paper suggests a clear need of developing professional figures capable of operating at the interface of different knowledge domains.

The data illuminate several aspects of how innovation takes place in a cluster opening up intriguing aspects that have been overlooked by extant literature. The authors believe that this may trigger several lines of further research on the topic.