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Little is known, about the role played by start-ups in open innovation networks. Start-ups – due to their nature of new and emerging companies – can largely benefit from the knowledge that can flow intentionally or unintentionally from external partners during open innovation practices. When open innovation networks are not set among peers on both sides the authors expect to have more unintended knowledge flows. Such knowledge “leaks” – as the authors named them – in open innovation networks are totally unexplored in literature. Hence, the purpose of this paper is to focus “whether and how knowledge leaks occur in open innovation networks with start-ups”.

The research design of this study relies on social network analysis methods and techniques to disentangle the role of start-ups in open innovation networks – in a major Italian aerospace cluster – vis-à-vis the three types of knowledge considered in this study. Then the authors confirmed knowledge leaks to occur through a multiplexity analysis. In the second stage of the research, the authors decided to strengthen the results, making them more vivid and thorough, relying on four case studies.

The paper sheds light on a totally unexplored phenomenon, theorizing on the role of start-ups in open innovation networks and suggesting intriguing implications both for theory and managers on whether and how knowledge leaks occur.

The main limitations arise from the specific research context, in fact the study has been conducted in an aerospace cluster. So future studies might consider to explore knowledge leaks in non-cluster settings and in low tech industries.

The results have practical implications both for policy makers and for managers. First of all, the research confirms how open innovation often originates from a combination of different knowledge types acquired through the collaboration with heterogeneous players, start-ups included. Hence, managers may design open innovation strategies balancing their portfolio of collaborations to maximize the absorption of relevant knowledge and start-uppers may consider to engage in open innovation practices to accelerate knowledge absorption. Nevertheless, the study warns managers against the risk of knowledge leaks, especially in cases like start-ups where the eagerness to participate or the prestige associated with participating in open innovation networks with key players may hamper the control over knowledge leaks.

This opens up for possible interventions for policy makers too. First of all, policy makers may consider incorporating the concept of knowledge leaks in their campaign in favour of open innovation. Second, the study may help policy makers in designing programmes for knowledge transfer partnerships amongst the various players of a cluster in a more conscious way, especially warning new to business companies, like start-ups, about possible leaks. Finally, there is also the need of developing professional figures like consultants capable of supporting start-ups in their open innovation practices.

Findings reported in the paper confirm multiplexity and heteromorphism in knowledge exchanges and shed the light on a completely unexplored field (i.e. open innovation and start-ups), focussing on knowledge leaks. Relevant implications for policy makers and managers are included in the study.

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.

This study aims to investigate the relationship between the presence of the family variable within a business enterprise and the managerial factors affecting the success of new product development (NPD). This can be structured into three research questions: What is the relationship between the presence of the family variable within a business enterprise and the managerial factors affecting the success of NPD activities? How the managerial factors affecting the NPD process are faced in family firms? Which are the main differences (e.g. strengths and/or weaknesses) in dealing with the managerial factors affecting the NPD process between family and non‐family firms?

The study employs a grounded‐theory and case‐study approach to investigate the relationship between the presence of the family variable within a business enterprise and the managerial factors affecting the success of NPD. The starting point is an in‐depth literature review on the managerial factors differentiating family from non‐family firms, and the managerial factors affecting NPD success. Then, a multiple case‐study on five Italian family firms and five Italian non‐family enterprises is conducted. The case‐studies lead to the development of an empirically grounded theoretical framework that outlines how the distinctive characteristics of family businesses are related to the managerial factors affecting NPD success.

Family firms clearly emerge as more long‐term oriented than non‐family enterprises. The long‐term orientation of family businesses vs non‐family companies seems to play a pivotal role in originating NPD projects with long‐term thrust. If a company is long‐term oriented it is reasonable to expect that it will put its long‐term vision in NPD programs, thus reaching a NPD long‐term thrust.

The study advances research on strategic innovation and NPD in family vs non‐family firms. It develops new theory at the important intersection of family business and innovation/NPD research, filling a gap in the literature and providing justification and guidance for the design of more comprehensive studies. Future research could investigate and test the theoretical framework on a wider empirical base, using either qualitative or quantitative methods.

The paper addresses the failure of innovation management research to recognize, embrace, and deliberately incorporate family firms. It therefore fills a gap in the literature and extends prior research by introducing specific propositions that are supported by the case data and originally integrating them in the general research stream on NPD and family‐firm characteristics. The originality of the study lies also in the fact that it appears to be the first comparative analysis on this specific topic involving both family and non‐family enterprises.

The purpose of this paper is twofold: firstly, to identify the factors that are capable of influencing the value of a technological asset that is exchanged in the context of a business transaction and, secondly, to identify the direction of the relationship between each factor and the technological asset value.

First of all, an in‐depth analysis of the available literature about the assessment of technological asset value was conducted. Then a panel study was organised, involving several intellectual property managers and consultants and senior practitioners working in the field of IP assessment. Finally, an illustrative case study was conducted.

The paper proposes a framework that encompasses the following classes of factors: asset related; firm related; context related; risk related; and transaction related. It is shown that these factors are capable of influencing the value of a technological asset that is exchanged in the context of a business transaction and the direction of their impact is indicated.

The paper is believed to be a useful instrument capable of supporting managers and appraisers who, in the context of a specific business transaction involving the exchange of a technological asset between the counterparts, are called to assess its value.

The value of a technological asset is typically estimated through monetary techniques (cost, income and market methods) that often turn out to be disappointing in practice. This is mainly due to their quantitative nature, that impedes them to appropriately take into account qualitative variables capable of affecting the value of the asset. This paper is the first attempt, to the best knowledge of the authors, that draws together these variables in a comprehensive form and suggests the direction of their impact on the asset value.