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How university spin-offs differ in composition and interaction: a qualitative approach

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Abstract

During their early development, academic spin-off projects are embedded in the context of research institutions. However, knowledge is still lacking on whether the influence of university structures on spin-off projects or the need for resources and the initial setting of these projects vary across research disciplines or university departments. To enhance our understanding of the development of spin-off projects, it is necessary to identify strategies focusing on the specific characteristics of spin-off projects within a single research institution. In our study, we therefore address interactions of spin-off projects with several factors within one university, based on spin-off projects from 2007 to 2013. We inductively derive four types of spin-off projects that interact differently with the different factors and the university. By concentrating on the specific needs of each type, we can provide a framework allowing to identify spin-off needs and to implement target-oriented support mechanisms.

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Notes

  1. For our analysis, we first identified spin-offs and spin-off projects in the period from 2004 (due to the changes in the legal regulations and the fact that several transitional regulations existed for a year after 2002—for example, universities were still able to provide all rights to the inventor) to 2013. We then decided to exclude projects occurring before 2007 to guarantee a similar university environment. However, only one spin-off project and one legally established company from the field of natural and life sciences from 2004 to 2006 had to be excluded due to this. This further supports the decision to start our analysis in 2007 since it provides evidence that the representativeness of the data is unaffected by this choice.

  2. For example, we used the same questions for the interview with the expert in the advisory network, but the questions were formulated broader.

  3. Detailed information on the transcription process is available in Table 5 of the Appendix.

  4. The coding system, the full model of analysis and a discussion on the method are provided in Tables 6 and 7 of the Appendix.

  5. Protocols and the anonymized database are available from the authors on reasonable request.

  6. Four projects are legally established companies, one project will soon legally establish a company and six spin-off projects no longer engage in business development. Therefore, we were able to include projects from all developmental stages.

  7. We also took into account other possible interaction points with the university, e.g., the influence of colleagues, but did not find significant differences. In all groups, the attitudes of colleagues were similar, ranging from neutral to interested, but they were always stated as not directly influencing project development.

  8. We included team aspects in our data analysis because they are relevant in the development of a spin-off project (Vanaelst et al. 2006). We find that teams which have internal problems never developed sufficiently far.

  9. In their model of spin-off policies, based on the work of Roberts and Malone (1996), Degroof and Roberts (2004) summarize four archetypes, which they evaluate with regard to the level of selectivity and the level of support from the academic institution. They partly find absence of any proactive spin-off policy and thus show the possibility of minimum support to and low selectivity of projects. Furthermore, they identify occasions of intermediate support activity and middle selectivity in choosing which project to support (or not). Finally, they provide evidence for high support for the spin-off projects selected. They assume that spin-off policies should be aligned with the surrounding, in which the university exists. In weak entrepreneurial surroundings, regarding culture or infrastructure, a policies of high support and high selectivity are advisable but require significant resources. In regions with a highly developed entrepreneurial culture, a low support and low selectivity policy can be adopted by academic institutions.

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Authors and Affiliations

  1. Service Centre Research and Technology Transfer, University of Wuerzburg, Sanderring 2, 97070, Würzburg, Germany

    Cornelia Kolb

  2. Chair of Management, Innovation and International Business, Faculty of Business and Economics, University of Augsburg, 86135, Augsburg, Germany

    Marcus Wagner

  3. Bureau d’Economie Théorique et Appliquée, Université de Strasbourg, 61 Avenue de la Fôret Noire, 67000, Strasbourg, France

    Marcus Wagner

Authors
  1. Cornelia Kolb
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Correspondence to Cornelia Kolb.

Appendix

Appendix

See Tables 5, 6 and 7.

Table 5 Rules of transcription
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Table 6 Code system in MaxQDA11 [633]
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Table 7 Full model of analysis (own illustration based on Mayring 2010)
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Qualitative content analysis is well established and enables a theory-based assessment while still being able to adjust to the data collected. Due to its systematic approach and clear regulations, qualitative content analysis allows for intersubjective confirmability (Mayring 2010).

Data analysis and data collection was done in parallel and recursively (Eisenhardt 1989). This allowed us to structure our information based on the specific topics which we were interested in. The assessment process can be divided in three basic forms, which are aggregation, structuring and explication and can be mixed depending on the adequacy for the analysis. We tested the codes on two interviews to confirm their applicability (Kuckartz et al. 2008). The next step was a first paraphrasing of the categories’ content using the abstraction level of a single interview to select important issues and to clarify equivalences. A second reduction process on a higher abstraction level was done after re-checking the categories in the original sample and by combining the information across interviews. This was possible due to the application of a guideline. After restructuring and summarizing the information in the data, we evaluated the findings based on prior research results and theoretical assumptions (Mayring 2010; Stigler and Reicher 2005; Meuser and Nagel 1991).

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Kolb, C., Wagner, M. How university spin-offs differ in composition and interaction: a qualitative approach. J Technol Transf 43, 734–759 (2018). https://doi.org/10.1007/s10961-017-9629-1

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