Models for university technology transfer: resolving conflicts between mission and methods and the dependency on geographic location

Warren, Anthony; Hanke, Ralph; Trotzer, Daniel

doi:10.1093/cjres/rsm009

Cited by 44 publications

(42 citation statements)

References 19 publications

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“…These policy implications echo those of Warren et al (2008) who propose that, with respect to universities, policymakers should develop 'supportive innovation systems' for their locales, appropriate to the need of the firms within them. For instance, smart online services to match potential partners may be tailored towards the needs of particular types of firm, with smaller KIBS firms being perhaps best directed towards local universities.…”

Section: Discussionmentioning

confidence: 72%

“…While previous work has shown that university knowledge spillovers are effective at distances of between 97 Km and 161 Km (Woodward et al 2006;Warren et al 2008;Hausman 2012) the following analysis utilises a lower threshold to deliberately capture the intra-urban nature of collaborative network linkages in the UK. The UK is a densely populated nation, with an urban population density of over 4300 per KM, compared with around 430 for the country as a whole (ONS, 2013 …”

Section: Dependent Variablementioning

confidence: 99%

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The Spatio-Relational Nature of Urban Innovation Systems: Universities, Knowledge Intensive Business Service Firms, and Collaborative Networks

Johnston¹,

Huggins²

2016

Journal of Urban Technology

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The need to better identify the spatio-relational nature of urban innovation systems and spaces is increasingly acknowledged. The aim of this paper, therefore, is to provide an enhanced understanding of the knowledge networks existing between urban Knowledge Intensive Business Services firms (KIBS) and universities, which are often key components of such systems and spaces. Drawing on an analysis of urban KIBS firms and universities in the UK, it is found that the nature of firms, the location in which they are based, and the research intensity of their university partners are important determinants of the spatiality and localisation of the networks they form. The results show that the smallest urban KIBS firms have the highest propensity to engage in local links with universities, suggesting that they rely most significantly on their own urban innovation system for collaborative network ties.

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Section: Discussionmentioning

confidence: 72%

Section: Dependent Variablementioning

confidence: 99%

The Spatio-Relational Nature of Urban Innovation Systems: Universities, Knowledge Intensive Business Service Firms, and Collaborative Networks

Johnston¹,

Huggins²

2016

Journal of Urban Technology

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“…Transferors might include: universities; researchers; university scientists; technology transfer office (TTO); public universities; educational and research institutions; technological portfolio company; public technology company; knowledge and technology transfer organisations; PRO. Transferees might include: client companies; spin-offs and start-ups; entrepreneurs in business; researchers and industry project manager; all parties involved in the organisation: senior management, middle management, engineers, support, marketing and sales; inventors and future users of technology (Siegel et al, 2004;Gorschek et al, 2006;Harmon et al, 1997;Necoechea-Mondragón et al, 2013;Genet et al, 2012;Seaton and Cordeyhayes, 1993;ATTC Network, 2011;Gross, 2003;Landry et al, 2013;Heinzl et al, 2013;Warren et al, 2008).…”

Section: A Generic Kttmmentioning

confidence: 99%

“…Agents might include: TT company; non-profit organisation (NPO); industry liaison offices; communities of practice; TTO; federal agencies; direct links and dyadic alliances, among others (Siegel et al, 2004;Gorschek et al, 2006;Harmon et al, 1997;Necoechea-Mondragón et al, 2013;Genet et al, 2012;Seaton and Cordeyhayes, 1993;ATTC Network, 2011;Gross, 2003;Landry et al, 2013;Heinzl et al, 2013;Warren et al, 2008).…”

Section: A Generic Kttmmentioning

confidence: 99%

Technology transfer models: typology and a generic model

Pagani

Zammar

Kovaleski

et al. 2016

IJTTC

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Abstract:The technology transfer (TT) process has been adopted by companies throughout the world. For many companies and countries, the implementation of TT process is determinant to the economic growth and a way to keep updated with technology development and innovation. Ever since TT started to be discussed in the literature, several different models of TT have been proposed with the purpose of making the TT process easier and more successful. The purpose of this paper is to propose a generic model that can be adapted to any organisation and institution interested in transferring knowledge Technology transfer models: typology and a generic model 21and technology. To reach this purpose, a systematic review on technology transfer models was done using the methodology for systematic review, Methodi Ordinatio, using data from 1990 to 2015. Due to the great variety of models identified in the systematic review, and for better understanding those models and their purposes, a typology is also presented.

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“…Therefore, the actual definition of technology transfer efficiency may vary according to research objectives. Warren et al (2008) argue that a 'one size fits all' approach cannot be achieved which points out to the multifarious nature of UTTOs efficiency.…”

Section: Efficiency Evaluation Of Uttosmentioning

confidence: 98%

University technology transfer performance in Australia

Alhomayden¹

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University technology transfer is considered to be an important driver of national innovation and regional economic development. However, previous studies on university technology transfer productivity and efficiency have not evaluated the relative efficiency of Australian universities in technology transfer. In the first study of this thesis, secondary data was used to conduct data envelopment analysis to measure the technical efficiency of Australian universities in producing four technology transfer related outputs, namely the number of invention disclosures, the number of licenses executed, the amount of licensing royalty income and the number of spin-offs created with university equity. Universities were then ranked according to the average technical efficiency scores for producing the four technology transfer related outputs. Subsequently, taking differences between universities in technology transfer efficiency into account; semi-structured interviews were conducted with 25 University Technology Transfer Office (UTTO) representatives to identify 12 antecedents of university technology transfer efficiency.The literature on university technology transfer has proposed individual, organisational and environmental determinants to superior university technology transfer performance.However, most of the previous studies on university technology transfer productivity and efficiency have not made a theoretical contribution to the field which might have limited the generalisability of their findings. In the second study of this thesis, the Resource-Based Theory (RBT) was adopted to examine and explain performance differences between universities in technology transfer considering three resource factors, namely the number of ARC Linkage funded projects (financial capital), the number of UTTO staff holding a PhD (human capital) and the joining of a UTTO to a consortium (social capital). Using primary and secondary data, these resource factors were regressed against six university technology transfer performance measures, namely the number of invention disclosures, the number of filed patents, the number of executed licenses, the amount of licensing royalty income, the number of all spin-offs created and the number of spin-offs created with university equity.Empirical findings show that the application of financial, human and social capital matters for technology transfer performance. UTTOs that possess these tangible and intangible resources report higher performance on most of the performance measures applied.Previous studies have not established a relationship between the organisational structure of UTTOs and their performance. In the third study of this thesis, the RBT was adopted to 3 | P a g e examine and explain performance differences between universities in technology transfer considering four resource factors in relation to the organisational structure of UTTOs; centralisation, specialisation, configurational autonomy and financial dependence. Using primary and secondary data, these resource factors ...

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Models for university technology transfer: resolving conflicts between mission and methods and the dependency on geographic location

Cited by 44 publications

References 19 publications

The Spatio-Relational Nature of Urban Innovation Systems: Universities, Knowledge Intensive Business Service Firms, and Collaborative Networks

The Spatio-Relational Nature of Urban Innovation Systems: Universities, Knowledge Intensive Business Service Firms, and Collaborative Networks

Technology transfer models: typology and a generic model

University technology transfer performance in Australia

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