Search results

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.

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 research is to determine scenarios and perspectives for improving the cluster strategy of business integration in the post-COVID-19 era with the help of the methodology of the game theory.

The methodology of this research includes the complex method, statistical method, correlation analysis and the game theory of decision-making.

Based on the analysis of scientific approaches, we formulate the authors' treatment of the essence of the notion of clustering, which characteristics are evaluated in this work. In this treatment, we distinguish factors that influence the development of clustering of business structures of the state, which level is assessed within the analysis. The components of the competitiveness of business structures are among such factors. Cluster structures of certain countries successfully functioned during the COVID-19 pandemic, using effective strategies created independently (United States) and based on the strategies of non-market regulation (China).

The scientific novelty of this research consists in the identification of the types and characteristics of the strategies of clustering of business structures formed during the COVID-19 and post-COVID-19 eras.

The purpose of this paper is to propose an evaluation model for evaluating the innovation competency in the Yanliang Aviation Industry Park, which is a typical example of an aviation industry cluster.

A subjective weighting method based on the order relation is used to determine the index weights, which are utilized in grey incidence analysis to measure the innovation competency of the aviation industry cluster.

The application of the index methodology to the Yanliang Aviation Industry Park demonstrates that the industry cluster possesses a strong innovation competency, as well as the feasibility and practicability of employing this approach.

The method introduced in the paper can be used to solve practical problems. Moreover, it provides potential support for the development of the aviation industry in the future.

In this paper, the high technology aviation industry, which now plays a strategic industrial role in China, is systematically studied by using a new methodology based on grey systems. Additionally, a subjective weighting sequence model founded upon a grey relational analysis is utilized in place of the analytic hierarchy process (AHP).

The aim of this paper is to demonstrate that synergy between firms and local institutions may lead to success even in an economic environment which cannot offer competitive advantages to high tech industrial clustering. However, the condition for such a complex result is convergence between cluster‐based regional development policy, the related industry national strategy and the central government preferential policy for less developed areas.

The experiences selected as case studies are the Xi'an National Civil Aerospace Base in Shaanxi Province (China) and CampaniAerospace in Campania region (Italy). As information was gathered both through policy documents (China and Italy) and direct interviews to cluster's stakeholders (Italy), the outcome is, according to the criteria suggested by Eisenhardt and Yin, a qualitative research. Comparing economic data provided by the respective countries' Statistical Offices, the authors assessed that Shaanxi Province and the Campania region share a “peripheral” position within their respective national context. Starting from this, the authors analyzed the formal documents which reported the experience of the two clusters.

The finding is that the outcome (the status of cluster which is assumed as “a value in itself”) of the convergence between firms' will to gain agglomeration advantages and the policymakers' plan to redress interregional economic disparities is definitely not an efficiency‐driven process. In the high tech sectors which are very sensitive to the increasing competition for technology on the international market, the pursuit of efficiency may, on the contrary, be dissipated.

As attested by the comparative literature, aims and performance of industrial clusters differ in accordance with the stage of economic development as well as the institutional and regulatory framework. The paper demonstrates, however, that in the take‐off stage, it is the position occupied by the host region within the national context which determines the behaviour of the actors concerned as well as the outcome of their commitment.

The purpose of this study is to explore the early stage of development of a cluster. The literature on early stage of cluster development shows that there are often random effects such as an entrepreneur and spin-off companies, and in this study, a coordinated approach for cluster development is described.

A single exploratory case study approach is followed. The aerospace cluster in the Spokane region, State of Washington, is described. Data from a variety of sources are triangulated to enhance the credibility of the case study findings.

It was found that although there are many types of collaborations occurring in the region, which involve policy and government organizations, the main driver of the early-stage cluster development is manufacturers-led coordinating mechanism. Individual manufacturers are too small to be successful in the aerospace industry, and they are collaborating to present a united “front” to out-of-the-region customers. Once customers place an order, then within this coordinating mechanism, the work is divided among different manufacturers.

The research has two main limitations. First, it is a single case study, and therefore, the results may not be generalizable. Second, the cluster is in an early stage of development, so it is not (yet) clear whether this manufacturers-led coordinated approach will have long-term success.

The studies offer potential for cluster development that go beyond relying on a single entrepreneur or on mostly government- or policy-driven initiatives. Instead, this is an approach that can be used by industry to lift the overall competitiveness of their region.

This cluster development approach offers potential for economic development of smaller regions which mainly consist of small- and medium-sized companies without endowment benefits or a large local customer base.

This study adds to the existing knowledge on clusters and cluster types. The identified cluster approach does not fit with the main types of clusters that have been identified in the literature. The companies involved are mainly small- to medium-sized companies, but by coordinating their capabilities, they are able to present core capabilities in a much more attractive manner to customers. This cluster development approach is not driven by or achieved through advantages in innovation, vertical or horizontal supply chain competition and advantages, creation of spin-off firms, or a regional demand base as customers are located outside the region. It deviates in terms of the types of companies involved and, mostly, in a sense that it acts as one unit to customers who are located outside the region.

The purpose of this paper is to explore the emergence of innovation in smart service systems to conceptualize how actor’s relationships through technology-enabled interactions can give birth to novel technologies, processes, strategies and value. The objectives of the study are: to detect the different enablers that activate innovation in smart service systems; and to explore how these can lead dynamically to the emergence of different innovation patterns.

The empirical research adopts an approach based on constructivist grounded theory, performed through observation and semi-structured interviews to investigate the development of innovation in the Italian CTNA (Italian acronym of National Cluster for Aerospace Technology).

The identification and re-elaboration of the novelties that emerged from the analysis of the Cluster allow the elaboration of a diagram that classifies five different shades of innovation, introduced through some related theoretical propositions: technological; process; business model and data-driven; social and eco-sustainable; and practice-based.

The paper embraces a synthesis view that detects the enabling structural and systems dimensions for innovation (the “what”) and the way in which these can be combined to create new technologies, resources, values and social rules (the “how” dimension). The classification of five different kinds of innovation can contribute to enrich extant research on value co-creation and innovation and can shed light on how given technologies and relational strategies can produce varied innovation outcomes according to the diverse stakeholders engaged.

The purpose of this paper is to investigate the impact of the different drivers of the establishment of innovation relationships in an aerospace cluster. In particular, the work investigates the impact of the various forms of proximity in the formation of inter-organisational collaborations for innovation.

The analysis is based on primary data collected through interviews and questionnaires on innovation collaborations, administered to all the firms operating in the aerospace cluster in Tuscany. The work applies social network analysis and Exponential Random Graph Models to analyse the forces that drive inter-organisational collaborations for innovation.

Results confirm the importance of geographical proximity in the formation of ties in the cluster, but social proximity is one of the main drivers for tie formation. Reciprocity shows that companies develop innovations in a reciprocal way and that most relationships are bidirectional. Triadic closure is also relevant, where the role played by trust and previous relationships is evident. Finally, hierarchy network processes are underlined, where the most central actors of the network are the most popular confirming a processes of preferential attachment. The central organisations gradually are more important, whereas the marginal ones are left in the periphery.

The work presents some novelties. First, it measures the different impacts of the various forms of proximity together with more advanced measures of network analysis. It allows pointing out the relevance of a firm’s network position in clusters and the fact that clusters assume hierarchical structures similar to centre-periphery networks, where most relevant nodes are in the inner core and marginal organisations are relegated.

Purpose – The purpose of the chapter is to sketch the historical and evolutionary development of the Wichita Aircraft Manufacturing Cluster from inception to present and provide a descriptive narrative of aircraft industry knowledge spillovers currently driving effort to establish a Medical Device Manufacturing Cluster. The chapter illustrates how carbon-fiber composite materials knowledge and technology developed for use in the aviation industry is facilitating the creation and growth of medical device manufacturing.

Methodology/approach – We use an historical case study approach to trace the development of the aircraft cluster in the Wichita, KS metropolitan area. A number of technologies are identified that had initially been adopted by one firm but eventually diffused through other firms in the local cluster and ultimately throughout the industry.

Findings – In addition to providing examples of within industry knowledge spillovers, we provide an example of technology-based knowledge that is diffusing through the aircraft manufacturing industry and is now being used as the basis for establishing an unrelated industry manufacturing cluster. The use of carbon-fiber composites in aircraft manufacturing has diffused from one manufacturer to many in the industry. Subsequently, the knowledge base surrounding carbon-fiber composite materials is being used in a local R&D effort to create a second manufacturing cluster producing medical devices ranging from surgical instruments to joint-replacement implants.

Originality/value of paper – The chapter illustrates a unique example of a manufacturing cluster, intra-industry knowledge spillovers, and inter-industry knowledge spillovers to create a new manufacturing cluster.