Capano, Benedetta (2016) A Partner Qualification Framework to Support Research and Innovation in Technology-Intensive Industries. [Tesi di dottorato]
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| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | A Partner Qualification Framework to Support Research and Innovation in Technology-Intensive Industries |
| Autori: | Autore Email Capano, Benedetta benedetta.capano@unina.it |
| Data: | 31 Marzo 2016 |
| Numero di pagine: | 154 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Ingegneria Industriale |
| Scuola di dottorato: | Ingegneria industriale |
| Dottorato: | Science and technology management |
| Ciclo di dottorato: | 26 |
| Coordinatore del Corso di dottorato: | nome email Zollo, Giuseppe giuseppe.zollo@unina.it |
| Tutor: | nome email lo Storto, Corrado [non definito] |
| Data: | 31 Marzo 2016 |
| Numero di pagine: | 154 |
| Parole chiave: | Collaborative R&D, Partner Qualification, Decision Support, Data Envelopment Analysis, Rating, Innovation Performance Evaluation, Open Innovation |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-IND/35 - Ingegneria economico-gestionale |
| Depositato il: | 14 Apr 2016 10:58 |
| Ultima modifica: | 08 Giu 2019 01:00 |
| URI: | http://www.fedoa.unina.it/id/eprint/11131 |
Abstract
In modern economies, where markets and technology are changing rapidly, innovation partnerships are among the major strategic choices for companies to create competitive long term advantages. Especially in high-tech sectors, companies are encouraged to leverage on external sources of knowledge in their R&D activities. Although the number of studies investigating the topic of R&D collaboration from different perspectives has increased over time, the problem of partner selection still lacks comprehensive analyses and operational frameworks to drive innovation alliances to success. In order to address such a gap and to overcome the aforementioned limits this thesis provides a systematic literature review on the R&D partner selection problem and proposes a quantitative and DEA-based decision- making framework to support organizations in identifying, qualifying and selecting the most suitable partners for technological innovation. The framework has been developed together with the innovation department of a large enterprise in the transportation industry, and it has been validated on relevant case-studies of industrial relevance addressing both emerging and mature technologies. Advantages and limitations of the proposed approach in innovation management research and practice are highlighted and discussed.
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