Giovanna Ferraro

Technology transfer in innovation networks: An empirical study of the Enterprise Europe Network

This article offers a network perspective on the collaborative effects of technology transfer, providing a research methodology based on the network science paradigm. We argue that such an approach is able to map and describe the set of entities acting in the technology transfer environment and their mutual relationships. We outline how the connections’ patterns shape the organization of the networks by showing the role of the members within the system. By means of a case study of a transnational initiative aiming to support the technology transfer within European countries, we analyse the application of the network science approach, giving evidence of its relative implications.

On the influence of nodes' characteristic in inter-organisational innovation networks structure

This paper studies inter-organisational innovation networks, investigating the relations between the structure of the connections among members and a given nodes characteristic. Such relations are used to provide some insights into the attitude of network innovation. We propose an original methodology that considers nodes belonging to two different classes detecting the available configurations. The intensity of connections, as an additional layer of information, is also included to examine its relative influence on the network topology. We complete the analysis through the introduction of a qualitative intensity/connectance matrix that attempts to connote the innovation attitude of the network. We test the methodology on a real case study, discussing the detected configuration and the implication in terms of innovation attitude.

Organizing Collaboration in Inter-Organizational Innovation Networks, from Orchestration to Choreography

This paper introduces the concept of choreography with respect to interorganizational innovation networks, as they constitute an attractive environment to create innovation in different sectors. We argue that choreography governs behaviours by shaping the level of connectivity and cohesion among network members. It represents a valid organizational system able to sustain some activities and to reach effects generating innovation outcomes. This issue is tackled introducing a new framework in which we propose a network model as prerequisite for our hypothesis. The analysis is focused on inter-organizational innovation networks characterized by the presence of hubs, semi-peripheral and peripheral members lacking hierarchical authority. We sustain that the features of a network, bringing to synchronization phenomena, are extremely similar to those existing in innovation network characterized by the emergence of choreography. The effectiveness of our model is verified by providing a real case study that gives preliminary empirical hints on the network aptitude to perform choreography. Indeed, the innovation network analysed in the case study reveals characteristics causing synchronization and consequently the establishment of choreography.

Some Insights into the Relevance of Nodes’ Characteristics in Complex Network Structures

Network structures describe a variety of systems and it is crucial to recognise essential functionalities that affect the dynamic of interactions. Nodes are often identified by certain characteristics, such as age or gender, and the tendency to link nodes with similar features is referred to as homophily. To verify which characteristic is able to address such behaviour has a computational complexity that becomes hard for large networks. In this paper we present a methodology that can be used as a pre-processing tool in order to avoid the study of non-effective nodes’ characteristics.

A network perspective on the visualization and analysis of bill of materials

A bill of materials (BoM), or product structure, is a diagram that lists all the components and parts required to produce one unit of a finished product, or end part. It is often represented as a tree structure with hierarchical relationships among different components and materials. In this article, we introduce two procedures to convert single and multiple BoM into networks. These procedures allow us to leverage the potentialities of networks analysis, providing new perspectives in terms of representation and extractable informative content, and thus gaining insights into the criticalities of parts and components. We conclude interpreting some network measures and their outcomes in terms of practical implications in industrial management, for example, product functional design and, above all, variety reduction programs.

Resilience of Core-Periphery Networks in the Case of Rich-Club

Core-periphery networks are structures that present a set of central and densely connected nodes, namely the core, and a set of non-central and sparsely connected nodes, namely the periphery. The rich-club refers to a set in which the highest degree nodes show a high density of connections. Thus, a network that displays a rich-club can be interpreted as a core-periphery network in which the core is made up by a number of hubs. In this paper, we test the resilience of networks showing a progressively denser rich-club and we observe how this structure is able to affect the network measures in terms of both cohesion and efficiency in information flow. Additionally, we consider the case in which, instead of making the core denser, we add links to the periphery. These two procedures of core and periphery thickening delineate a decision process in the placement of new links and allow us to conduct a scenario analysis that can be helpful in the comprehension and supervision of complex networks under the resilience perspective. The advantages of the two procedures, as well as their implications, are discussed in relation to both network effciency and node heterogeneity.

A new measure for community structures through indirect social connections

Abstract Based on an expert systems approach, the issue of community detection can be conceptualized as a clustering model for networks. Building upon this further, community structure can be measured through a clustering coefficient, which is generated from the number of existing triangles around the nodes over the number of triangles that can be hypothetically constructed. This paper provides a new definition of the clustering coefficient for weighted networks under a generalized definition of triangles. Specifically, a novel concept of triangles is introduced, based on the assumption that, should the aggregate weight of two arcs be strong enough, a link between the uncommon nodes can be induced. Beyond the intuitive meaning of such generalized triangles in the social context, we also explore the usefulness of them for gaining insights into the topological structure of the underlying network. Empirical experiments on the standard networks of 500 commercial US airports and on the nervous system of the Caenorhabditis elegans support the theoretical framework and allow a comparison between our proposal and the standard definition of clustering coefficient.