dc.contributor.author | Bonaventura, Moreno | |
dc.date.accessioned | 2017-06-26T10:29:26Z | |
dc.date.available | 2017-06-26T10:29:26Z | |
dc.date.issued | 2017-04-18 | |
dc.date.submitted | 2017-06-25T14:41:54.497Z | |
dc.identifier.citation | Bonaventura, M. Shortest paths to success: Network indicators of performance in innovation ecosystems. Queen Mary University of London | en_US |
dc.identifier.uri | http://qmro.qmul.ac.uk/xmlui/handle/123456789/24555 | |
dc.description | PhD | en_US |
dc.description.abstract | In this thesis I show how various theories and methodologies borrowed from complexity
science, organisation science, and network science can be suitably integrated to provide
a comprehensive and interdisciplinary approach to the study of innovation processes. I
study the network foundations of success in innovation ecosystems and I conduct several
empirical investigations to identify those network characteristics that are expected to correlate
with positive outcomes and success. I assess the extent to which the diversity and
the strength in the networks of relationships boost the performance and success of scientists
and early-stage firms. To this end I analyse two large-scale data sets about scientific
publishing and start-up firms by making use of already existing topological network measures
and by proposing novel measures to characterise the degree of interdisciplinarity
and access to diverse pools of knowledge in scientific collaborations. Results provide
empirical support to the idea that collaboration sustains innovation and performance
by facilitating knowledge diffusion, acquisition and creation. First, results indicate that
the networks of interaction between start-ups have a strong impact on the firms' longterm
success. Second I find that, while abandoning specialisation in favour of moderate
degrees of interdisciplinarity deteriorates scientific performance, very interdisciplinary
scientists tend to outperform specialised ones. Additionally, I address the computational
challenges related to the size of the data sets used and their time-varying nature. In
particular I focus on the scalability challenges of incremental graph algorithms. The
thesis contributes in this direction by proposing new efficient algorithms and data structures to handle and to analyse large graphs whose nodes and edges change rapidly over
time. These efforts have been collected and made available to the public in the form of
a web platform (http://lab.startup-network.org/) and an open-source python package,
NetworkL (https://networkl.github.io/). | en_US |
dc.language.iso | en | en_US |
dc.publisher | Queen Mary University of London | en_US |
dc.rights | The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author | |
dc.subject | Innovation processes | en_US |
dc.subject | Business and Management | en_US |
dc.subject | Mathematics | en_US |
dc.title | Shortest paths to success: Network indicators of performance in innovation ecosystems | en_US |
dc.type | Thesis | en_US |