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| 001 | 829136 | ||
| 005 | 20240712113052.0 | ||
| 024 | 7 | _ | |a 10.1016/j.techfore.2014.05.001 |2 doi |
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| 037 | _ | _ | |a FZJ-2017-02943 |
| 082 | _ | _ | |a 300 |
| 100 | 1 | _ | |a vom Stein, Nicole |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a How to measure technological distance in collaborations — The case of electric mobility |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2015 |b Elsevier Science |
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| 520 | _ | _ | |a Innovation collaborations experienced a substantial growth during recent decades, so that research interest in factors contributing to successful collaboration increased. One proposed success factor is technological distance, which determines the probability of receiving new knowledge from a partner as well as the ability of absorbing it. The methodology for measuring this distance is receiving broad attention in current literature. Therefore, we compare well-established measuring methods based on Euclidian distances with the recently introduced method of the min-complement distance.Collaborations along the entire value chain are seen as a way to overcome technological deficiencies associated with battery development for electric mobility, which implies collaboration of partners with different technological distances. Hence, we specifically focus on cross-industry collaborations comprising partners from the chemical and automobile industries.Our results show that the methodology used highlights different aspects of the approximation of technological distance in the examined collaborations. The use of the min-complement distance seems to be reasonable due to the intuitive property of the independence of irrelevant patent classes in cross-industry collaboration settings. |
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| 773 | _ | _ | |a 10.1016/j.techfore.2014.05.001 |g Vol. 97, p. 154 - 167 |0 PERI:(DE-600)2015184-6 |p 154 - 167 |t Technological forecasting and social change |v 97 |y 2015 |x 0040-1625 |
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