Preface

Hagenaars, Jacques A.; McCutcheon, Allan L.

doi:10.1017/cbo9780511499531.001

Cited by 317 publications

(391 citation statements)

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“…It therefore follows a finite mixture model rationale of disentangling a dataset into a finite mixture from a finite number of distinctly different populations. It is superior to traditional cluster analysis as it is based on a statistical model which allows for significance tests and measurements of fit (Jensen et al, 2007; for a detailed discussion see Hagenaars and McCutcheon, 2002).…”

Section: Estimation Strategy and Methodsmentioning

confidence: 99%

Search Patterns and Absorptive Capacity: A Comparison of Low- and High-Technology Firms from Thirteen European Countries

Grimpe,

Sofka

2007

SSRN Journal

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Section: Estimation Strategy and Methodsmentioning

confidence: 99%

Search Patterns and Absorptive Capacity: A Comparison of Low- and High-Technology Firms from Thirteen European Countries

Grimpe,

Sofka

2007

SSRN Journal

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Section: Estimation Strategy and Methodsmentioning

confidence: 99%

International Scientist Mobility and the Locus of Technology Transfer

Grimpe,

Edler,

Fier

2008

SSRN Journal

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Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW.Download this ZEW Discussion Paper from our ftp server:ftp://ftp.zew.de/pub/zew-docs/dp/dp08082.pdf Non-technical summaryScientific technology transfer has attracted considerable attention in the literature with a focus on the institutions (e.g. technology transfer offices), the agents involved in technology commercialisation or on the differentiation between formal and informal technology transfer mechanisms. Particularly the individual scientist has been shown to play a key role in technology transfer activities.We focus on the question how the scale and locus of technology transfer is influenced by individual characteristics and the scientist's international mobility. The effects of brain drain, gain or circulation have been subject to an intensive debate, both in the literature and in policy making. It seems that mobility of scientists, although highly praised as important for knowledge transfer and build-up of networks, cooperation potential and the like, does not loose its negative connotation of some kind of loss for the sending 'system', institution or nation. There has, however, been little systematic research on how the mobility of university scientists influences their propensity to engage in technology transfer activities and, particularly, on how mobility influences the locus of such activities.In a first step the mobility behaviour of university scientists is empirically explored by identifying subgroups with a characteristic mobility pattern. These subgroups are identified via latent class cluster analysis. Secondly, evidence is contributed on the determinants of technology transfer directed at the home or the (former) host countries of scientists. Regression analysis is used to approach this question. The empirical investigations are based on a sample of more than 500 German university scientists.We find that scientists who transfer knowledge do so, generally speaking, both at home and abroad. In other words, there is a complementary relationship between both activities. Transferring knowledge abroad while being mobile thus will not diminish the activities and effects of subsequent transfers but rather increase them. Second, we can confirm that the intensity of mobility has a positive impact on the inclination to transfer knowledge in the host country: while the simple transfer of artefacts or licenses can be done with short contacts, interactive transfer activities need intensity. Third, our results tend to confirm that the more frequent scientis...

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“…This technique addresses some weaknesses of traditional cluster analytical methods (for example, K-Means). It is based on a formal statistical model which allows probability based classifications and variables of mixed scale type (Jensen et al, 2007; for a detailed discussion see Hagenaars and McCutcheon, 2002). It also provides criteria for determining the appropriate number of classes which tends to be challenging with conventional cluster techniques.…”

Section: Clusters Of Interactionsmentioning

confidence: 99%

Successful Patterns of Scientific Knowledge Sourcing - Mix and Match

Aschhoff

Sofka

2008

SSRN Journal

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Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW.Download this ZEW Discussion Paper from our ftp server:ftp://ftp.zew.de/pub/zew-docs/dp/dp08033.pdf Non-technical SummaryUnique knowledge is a key factor for companies for generating new products, services and processes and thus, to remain competitive. It is increasingly emerging outside of firm boundaries. Universities and public research institutions have been identified as important sources of new knowledge. Academic knowledge spillovers appear especially promising as they usually have a high degree of novelty and therefore a large potential to generate important assets for differentiation in competition by creating radically new products and processes. Our goal is to provide a more detailed perspective on how firms can design their interactions with universities to acquire knowledge and apply it successfully. We extend existing research on industry-science interactions by moving from the effects of isolated types of interactions to combinations. We cover a broad set of potential interactions ranging from informal contacts and licensing to personnel exchange and joint research projects. First, we develop the concepts of breadth (diversity of interactions) and depth (intensity of interactions) in terms of the channels which are used by firms for knowledge acquisition from universities. Secondly, we condense both aspects into patterns of interactions arguing that different forms of interaction are complementary to one another. To test our theoretical framework empirically we use a survey of more than 800 firms from both manufacturing and services sectors in Germany and the way in which they organize their interactions with universities. This survey puts us in the position to relate these interaction strategies to innovation success which is directly based on university inputs.We find that the firms that are able and willing to engage in various types of interactions (breadth) and highly developed interactions (depth) perform better with regard to innovation success. When we compare both effects we find that broadening a firm's interaction approach with universities has stronger performance effects on innovation success (breadth) than strengthening the intensity of existing ones (depth). The explorative step of our analysis shows that interactions with universities can be grouped into four archetypical clusters. All clusters of interaction perform better than a sporadic one supporting our finding on the importance of broadening interaction approaches. Besides, we...

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Preface

Cited by 317 publications

References 0 publications

Search Patterns and Absorptive Capacity: A Comparison of Low- and High-Technology Firms from Thirteen European Countries

Search Patterns and Absorptive Capacity: A Comparison of Low- and High-Technology Firms from Thirteen European Countries

International Scientist Mobility and the Locus of Technology Transfer

Successful Patterns of Scientific Knowledge Sourcing - Mix and Match

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