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| 001 | 829132 | ||
| 005 | 20240712113124.0 | ||
| 024 | 7 | _ | |a 10.1007/s11192-016-1888-3 |2 doi |
| 024 | 7 | _ | |a 0138-9130 |2 ISSN |
| 024 | 7 | _ | |a 1588-2861 |2 ISSN |
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| 037 | _ | _ | |a FZJ-2017-02939 |
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| 100 | 1 | _ | |a Simon, H. |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Technological distance measures: new perspectives on nearby and far away |
| 260 | _ | _ | |a Dordrecht [u.a.] |c 2016 |b Springer Science + Business Media B.V |
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| 520 | _ | _ | |a Understanding the competitive environment of one’s company is crucial for every manager. One tool to quantify the technological relationships between companies, evaluate industry landscapes and knowledge transfer potential in collaborations is the technological distance. There are different methods and many different factors that impact the results and thus the conclusions that are drawn from distance calculation. Therefore, the present study derives guidelines for calculating and evaluating technological distances for three common methods, i.e. the Euclidean distance, the cosine angle and the min-complement distance. For this purpose, we identify factors that influence the results of technological distance calculation using simulation. Subsequently, we analyze technological distances of cross-industry collaborations in the field of electric mobility. Our findings show that a high level of detail is necessary to achieve insightful results. If the topic in scope of the analysis does not represent the core business of the companies, we recommend filters to focus on the respective topic. Another key suggestion is to compare the calculated results to a peer group in order to evaluate if a distance can be evaluated as ‘near’ or ‘far’. |
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| 773 | _ | _ | |a 10.1007/s11192-016-1888-3 |g Vol. 107, no. 3, p. 1299 - 1320 |0 PERI:(DE-600)2018679-4 |n 3 |p 1299 - 1320 |t Scientometrics |v 107 |y 2016 |x 1588-2861 |
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