Conference Agenda

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Session Overview
20-AM-07: ST10.4 - Transfer From Research to Industry and Society: The Role of the Fuzzy Front End in Scientific
Thursday, 20/June/2019:
8:30am - 10:00am

Session Chair: Luise Fischer, Fraunhofer Center for International Management and Knowledge Economy, Leipig
Session Chair: Anna Pohle, Fraunhofer IMW
Location: Amphi Lagarrigue

Session Abstract

Despite the extensive research activities in industrial countries, exploitation of research results remains unsatisfactory. Yet, scientific commercialisation or innovation is an important – but often underused – force for the respective national economies as well as the global market. Studies have shown that particularly the early phase of innovation – the fuzzy front end – is key in managing successful innovations in companies. Yet, this transition phase from science into innovation is understudied in the field of academic research. We therefore offer a paper track that places focus on understanding the fuzzy front end and the related mechanisms that trigger or hinder the transfer of scientific knowledge into innovation both on the level of individuals and organisations within and across countries.

Research organisations still struggle to commercialize their technologies. The transition between (basic) research and the commercialization of research often fails. This phenomenon is called the ‘Valley of death’ and is known as the phase where potential inventions and discoveries go to die. In the context of research organisations it is also called the ‘research valley of death’ or the ‘translation gap’ and focusses on challenges of research organisations in this phase. In contrast to the valley of death (VoD) within the company context, universities face the aggravating factor that the transition of knowledge and technologies happens between different organisations: from research to industry. The different perspectives and intentions of research organisations and companies hamper the transition. Strategies to bridge the research VoD become increasingly important. We argue that research organisation would benefit from approaches which intervene during the idea creation – the so called fuzzy front end (FFE) of innovation. The FFE is defined as the early phase of innovation that concerned with finding ideas and teams. The successful management of the FFE can bridge the VoD – also for research institutions.

We thus propose that universities and research (technology) organisations would benefit from a better understanding and management of the FFE. We suggest that studying the characteristics, role and potentials of scientific transfer mechanisms would benefit the exploitation of scientific research results. More particularly, the track welcomes papers that examine new tools, methods and strategies that ease the transition from research into society. We would like to invite contributions that inquire the following areas:

(1) What new forms of R&D management practices and/or transfer mechanisms are needed in research organisations to help researchers apply their knowledge to real-life situations and to support them enter into commercialisation agreements for their findings? How can the fuzzy front end contribute?

(2) What role should universities and academic research play in order to bridge the areas of research, industry and society?

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The Relevance of Academic Engagement for Knowledge Transfer: a Longitudinal Study.

Cristina Marullo1, Andrea Piccaluga1, Fabrizio Cesaroni2

1Sant'Anna School of Advanced Studies, Italy; 2University of Messina, Italy


Creating the conditions for the successful transfer of scientific knowledge to the market has achieved crucial importance in the last decade. A fundamental step is to improve the current understanding of the synergies between relational forms of university-industry interactions (academic engagement), knowledge commercialisation (patenting and licensing activities) and academic entrepreneurship.


A considerable body of literature grounded in the paradigm of entrepreneurial universities (Etzkowitz et al., 2000) has focused on knowledge commercialisation through patenting and licensing and spin-off creation as clear examples of academic impact (Markman et al., 2008).

Collaborative research, contract research and consulting represent other - “informal”- channels of university-industry knowledge transfer (Link et al., 2007). Such activities, motivating the majority of university-industry interactions (D’Este and Patel, 2007), epitomise academic engagement (Perkmann et al., 2013) as the “fuzzy front end” of knowledge transfer.

The concept of academic engagement goes beyond commercialisation, being characterised by a high relational involvement and bi-directional knowledge flows between university and industry rather focusing on the exploitation of academic knowledge to the market (Perkmann and Walsh, 2007). Although different in nature, the two concepts are strictly related: university engagement facilitates knowledge absorption by firms and encourages the production of scientific knowledge of commercial value.

Literature Gap

While literature has widely acknowledged the importance of universities’ quality and organisational context for successful commercialisation, a gap of knowledge persists on the role that other informal channels through which universities engage with industry can assume in facilitating knowledge transfer, and on the possible complementarities/differences between them.

Research Questions

We empirically investigate the relationship between different forms of academic engagement with industry and knowledge commercialisation activities.

Our research questions are:

RQ1: Is academic engagement with industry relevant for successful knowledge commercialisation and academic entrepreneurship?

RQ2: Are different forms of academic engagement (and their interactions) relevant for different commercialisation outcomes?


We adopt an analytical framework distinguishing different forms of academic engagement with industry in terms of types of relational involvement and underlying motivations (Perkmann and Walsh, 2007). Specifically, we investigate the relevance of research partnerships (“open science” forms of collaboration), and research services (“industry-pull” forms of collaboration) on knowledge commercialisation and academic entrepreneurship. To analyse these relationships we adopt a quantitative approach using longitudinal data (11 years) on a panel of intalian universities. By acknowledging the longitudinal dimension, we are able to test whether different forms or engagement (and their complementarities) are related to different commercialisation patterns in each period.

Empirical Material

We compiled a longitudinal dataset combining large-scale survey data collected on 52 Italian universities from 2005 to 2016 (Source: NETVAL Association) with secondary data obtained from other sources (Web of Science, EU Commission, Italian Ministry of Instruction & Universities).

Survey data were used to account for the frequency of university-industry interactions (outcomes from collaborative research, contract research and consulting), knowledge commercialisation activities (number of patents granted in year, number of exclusive licenses and transfer agreements concluded in year) academic entrepreneurship (number of spin-off companies founded in year) and characteristics of universities’ technology transfer offices, while secondary sources were used to obtain relevant information on universities’ size, scope (variety of disciplines) and productivity.

We analysed the panel through 6 negative binomial models in Generalised Estimated Equations (GEE) to evaluate how different forms of academic engagement with industry (research partnerships and research services) are related to different types of outcomes (number of patents, number of licenses number of transfer agreements and number of spin-off companies) in each period. 3 models were estimated to evaluate the main effects and 3 models were estimated to evaluate the interaction effects of the two forms of academic engagement on each of the outcome variables.


Taken together, our results show that research partnerships, as “open science” forms of academic engagement, have a positive effect on patenting activities. This form of university-industry interaction fosters the exchange of scientific knowledge and mutual learning, bringing the outputs of academic research into application. Research services (contract research and consulting), as “industry-pull” forms of engagement, positively influence licensing activities and transfer agreements, acting as a stimulus for universities to pursue closer-to-market research. Universities engaged in both research partnerships and research services with industry get positive outcomes in terms of spin-off creation, showing that a high relational involvement with industry enables the production of new knowledge of commercial value. We also successfully test the existence of complementarities between different forms of academic engagement: their interaction has a positive and significant effect on all the outcome variables. Our results clearly show that, although different forms of academic engagement with industry are relevant for different outcomes they mutually reinforce each other.

Summary of relevant findings:

Dependent variables

(n.patents / n.licenses / n.Spin-offs in year)

Independent variables (sig.)

Research_Partnerships (+++ / n.s. / +++)

Research_Services (n.s. / + / +++)

Research_partnerships*Research_services (+++ / +++ / +++)

Scientific_productivity (+ / n.s. / n.s)

TTO_age (+++ / + / n.s.)

TTO_size (n.s. / ++ / n.s.)

Research_budget (+++ / +++ / +)

Contribution to Scholarship

We contribute to scholarship on university-industry knowledge transfer by exploring the relationship between different forms of university engagement with industry, knowledge commercialisation and academic entrepreneurship, and by testing the existence of complementarities between different engagement activities. Drawing on longitudinal data on a panel of 52 Italian universities for 11 years, we offer a methodological improvement to research on academic engagement and provide robust empirical support to prior research (Perkmann and Walsh, 2007; Perkmann et al., 2013) by underlining: a) the existence of temporal relationships between university engagement with industry and knowledge commercialisation/academic entrepreneurship; b) the distinctive relevance of different forms of academic engagement (research partnerships/research services) on different commercialisation outcomes (patenting, licensing, spin-off creation); c) the existence of complementarities between different types of engagement activities in supporting market acceptance of the outputs of academic research.

Contribution to Practice

Exploring empirically the relevance of university engagement with industry on academic impact is of undoubted interest to university managers and policy makers. Knowing more about the relationship between academic engagement and commercialisation would inform universities’ managers by clarifying whether initiatives aimed at stimulating academic engagement also support commercialisation, and by guiding organisational initiatives towards a more focused approach. Our results could also inform the policy debate on university-industry knowledge transfer and, more in general, on the impact of universities on local development.


Bridging Research, Industry & Society is the 2019 R&D management conference theme. Our in-depth empirical analysis of university engagement as an “informal” mechanism triggering the exploitation of scientific knowledge offers an original perspective of the transition between research and commercialisation, shedding light on the “fuzzy front end” of technology transfer.


D’Este, P. and Patel, P. (2007) ‘University-industry linkages in the UK: What are the factors underlying the variety of interactions with industry?’, Research Policy, 36(9), pp. 1295–1313.

Etzkowitz, H., Webster, A., Gebhardt, C., & Terra, B. R. C. (2000). The future of the university and the university of the future: evolution of ivory tower to entrepreneurial paradigm. Research Policy, 29(2), 313-330.

Markman, G. D., Siegel, D. S. and Wright, M. (2008) ‘Research and Technology Commercialization’, Journal of Management Studies, 45(8), pp. 1401–1423.

Perkmann, M., Tartari, V., McKelvey, M., Autio, E., Broström, A., D’Este, P., Fini, R., Genua, A., Grimaldi, R., Hughes, A., Krabel, S., Kitson, M., Llerena, P., Lissoni, F., Salter, A. & Sobrero, M. (2013). Academic engagement and commercialisation: A review of the literature on university–industry relations. Research policy, 42(2), 423-442.

Perkmann, M. and Walsh, K. (2007) ‘University-industry relationships and open innovation: Towards a research agenda’, International Journal of Management Reviews, 9(4), pp. 259–280.

A clustering study of European Knowledge Transfer Offices based on their Commercialisation DNA

Laura Kreiling1, John Scanlan2

1Univ. Paris-Sud, Université Paris-Saclay, France; 2Maynooth University, Ireland


The European Knowledge Transfer (KT) landscape is diverse and current normalised metric comparison does not enable university Knowledge Transfer Offices (KTOs) to finding peer KTOs for benchmarking and mutual learning. This paper seeks to offer a novel approach to clustering KTOs by capturing their fundamental characteristics based on reported metrics.


Current research that compares KTOs in Europe has been working with national datasets and few variables. Common metrics in KT performance studies are research contracts, IP licensing and spin-offs (Cartaxo & Godinho, 2017), invention disclosures (Hülsbeck et al., 2013) and metrics on knowledge exchange, patenting and commercialisation (Stankevičienė et al., 2017). Respective studies were conducted in Portuguese, German and Lithuanian national contexts.

KTO cluster analyses in Europe have up to now been within the confines of national borders, focussing on a narrow range of KT activities and neglecting confounding factors. To address this, either the creation of new metrics, such as composite indicators (Rossi and Rosli 2015) or broader impact measures (Russo et al 2016) are proposed, or improved ways of using existing metrics (Kreiling 2018, Scanlan 2018).

Literature Gap

Reference groups enable performance benchmarking and peer-learning (Mol and Birkinshaw 2009) which is standard practice, e.g. public transportation industry (Ryus, 2010) but this potential has remained unexploited in the context of European KTOs due to the lack of approaches for the formation of peer groups.

Research Questions

This study aims to create groups of similar European KTOs. To achieve this, the authors considered:

1. What are characteristics based on which to determine KTO similarity?

2. How to create peer groups from an existing transnational KTO database?


The research was conducted in two-stages, in line with its objectives: first, a conceptual framework (so-called ‘KT Commercialisation DNA’) was created considering Scanlan’s (2018) approach for the use of ratio-variables to determine a set of indicators which drive KT activities. Second, KTO peer groups were formed with a clustering method, tested for its suitability with KTO data (Kreiling 2018), based on their KT Commercialisation DNA. A within-cluster analysis was also performed, given the heterogeneous cluster sizes in the initial clustering run. All results were validated with practitioners from Belgium, Ireland and the Netherlands.

Empirical Material

For the empirical testing of the conceptual framework, a study database was used which consisted of data from the ASTP FY2016 annual survey with 118 KTOs from 22 European countries. From the database, 37 datasets with KTOs from 15 countries were used, given the prerequisite of complete datasets for all 11 quantitative variables for the computation of indicators in line with the conceptual framework as well as for cluster analysis.


The ‘KT Commercialisation DNA’ consists of four dimensions, which together then are based on ten indicators. Each dimension represents a key KTO characteristic which captures the key factors that determine the level of KT activity. The four dimensions are: (a) available budget to support KT, (b) internal KT culture, (c) external KT ecosystem, (d) KTO structural characteristics. Indicators in the framework consist of 8 ratio-variables in dimensions (a), (b) and (c) and two metrics, age and type in dimension (d). The dimensions represent what the authors consider to be key drivers for KT activity, while the indicators are selected by the authors to best capture these dimensions, given the restriction imposed by available data.

The initial clustering resulted in the creation of five groups - three consisting of a single KTO, one with seven and one with 27 KTOs. A further within-cluster analysis of the largest group resulted in seven KTO peer groups with one, three, eight and ten KTOs.

We found that variables related to the internal KT culture primarily drove cluster creation, followed by external KT ecosystem and those related to KT budget. KT structural characteristics proved useful in some clusters for further grouping of KTOs.

Contribution to Scholarship

KTOs are a form of innovation intermediary (Howells 2006) and the reference group is a set of comparator organisations that are created to benchmark performance and facilitate mutual learning from practices and behaviours (Greve 1998). This research advances both fields by improving the understanding of similarities in innovation intermediaries and the use of these insights to create KTO peer groups.

So far, reference group literature has been used to study the conditions for management innovation (Mol and Birkinshaw 2009) and European KTO cluster analyses have used national datasets (Cartaxo & Godinho, 2017, Fernandez-Alles et al., 2018). This study brings together the methodological proof-of-concept for transnational KTO clustering (Kreiling 2018) with the practical ratio-based approach (Scanlan 2018). In doing so, the study of KTOs, their similarity and performance metrics, are advanced conceptually and empirically.

Contribution to Practice

This clustering study is the first of its kind which not only develops a conceptual approach but also uses it to generate initial empirical findings using transnational KTO database. In doing so, European peers were identified for most KTOs in the database and insights are gained on which KT dimensions determine their similarities.

The potential as well as the current challenges associated with the collection and use of transnational KTO data are highlighted. Aspects for the improvement of the collection and treatment of transnational KT metrics are raised.


KTOs are bridging the ‘managerial gap’ between academia and industry and are thus an important actor in innovation systems (Bessant and Rush 1995). This research suits with Track 10.4 as creating enabling mechanisms for KTOs to finding their peers is an understudied aspect in the FFE of innovation.


Bessant, J., & Rush, H. (1995). Building bridges for innovation: the role of consultants in technology transfer. Research Policy, 24(1), 97–114.

Cartaxo, R. M., & Godinho, M. M. (2017). How institutional nature and available resources determine the performance of technology transfer offices. Industry and Innovation, 24(7), 713–734.

Fernandez-Alles, M., Diánez-González, J. P., Rodríguez-González, T., & Villanueva-Flores, M. (2018). TTO characteristics and university entrepreneurship: a cluster analysis. Journal of Science and Technology Policy Management.

Greve, H. R. (1998). Performance, aspirations and risky organizational change. Administrative Science Quarterly, 43(1).

Howells, J. (2006). Intermediation and the role of intermediaries in innovation. Research Policy, 35(5), 715–728.

Hülsbeck, M., Lehmann, E., & Starnecker, A. (2013). Performance of technology transfer offices in Germany. Journal of Technology Transfer, 38(3), 199–215.

Kreiling, L. (2018). “Finding your peers – towards the comparison of European knowledge transfer organisations”. WOIC (World Open Innovation Conference), Berkeley, CA.

Mol, M. J., & Birkinshaw, J. (2009). The sources of management innovation: When firms introduce new management practices. Journal of Business Research, 62(12), 1269–1280.

Rossi, F., & Rosli, A. (2015). Indicators of university–industry knowledge transfer performance and their implications for universities: evidence from the United Kingdom. Studies in Higher Education, 40(10), 1970–1991.

Russo, M., Caloffi, A., Rossi, F., & Righi, R. (2016). Designing performance-based incentives for innovation intermediaries: evidence from regional innovation poles. Working Paper, Birckbeck College, University of London(London, UK).

Ryus, P. (2010). A methodology for performance measurement and peer comparison in the public transportation industry. Washington: National Academy of Science.

Scanlan, J. (2018). A capability maturity framework for knowledge transfer. Industry and Higher Education, 32(4), 235–244.

Stankevičienė, J., Kraujalienė, L., & Vaiciukevičiūtė, A. (2017). Assessment of technology transfer office performance for value creation in higher education institutions. Journal of Business Economics and Management, 18(6), 1063–1081.

ROBOTT-NET: How to design a European technology transfer network for robot technologies?

Christoph Hellmann1, Thilo Zimmermann1, Kate Gniewosz2

1Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Germany; 2Manufacturing Technology Center, UK


Within ROBOTT-NET we focus on developing a European wide network for technology transfer in robotics with the goal of improving technology and knowledge transfer. ROBOTT-NET consists of a consortium of four RTOs, the Manufacturing Technology Center (MTC), the Danish Technological Institute (DTI), Tecnalia (TEC) and Fraunhofer IPA (IPA).


Many models of the innovation process have been proposed. In our research we focus on open innovation and the quadruple helix. The principles and advantages of open innovation have been described by Chesbrough (Chesbrough, 2010). Etzkowitz and Leydesdorff (Etzkowitz and Leydesdorff, 2000) describe the triple helix innovation model. This is extended by the quadruple helix which was introduced by Carayannis and Campbell (Carayannis and Campbell, 2009). The quadruple helix expands the focus of open innovation to a broader group of stakeholders. The fuzzy front end of innovation has been defined by Koen et al. (Koen et al., 2001). The role of RTOs in a society and in terms of value creation has been discussed in EARTO (Arnold et al., 2010).

Literature Gap

Few information is available on creating regional technology transfer cluster with international relations which are based on modern innovation principles defined by above mentioned literature. This is especially true for the sector of robotics and the involvement of research and technology organizations (RTOs) as most work focusses on universities.

Research Questions

How can a sustainable technology transfer network be designed for the robotics sector leveraging the capabilities of research and organizations?


First, we derive a practical approach to technology transfer networks from scientific concepts such as open innovation, the quadruple helix and fuzzy front end of innovation as well as the experience of research and technology organizations. Second, the approach is implemented and tested within the EU funded ROBOTT-NET project. Data about performance is gathered and the success of the approach is quantified and discussed.

Empirical Material

The ROBOTT-NET project has been running since 2016. ROBOTT-NET implements the method for creating a sustainable technology transfer network. During this period evaluate the effectiveness of the technology transfer network. Among the data we have collected during the ROBOTT-NET project are performance indicators for RTO and industry technology transfer such as the number of people reached, the number of people personally reached, number of product ideas generated or the number of multilateral development projects conducted due to ROBOTT-NET. We have also collected data about international technology transfer between RTOs and industry within the network such as the work load shared between RTOs or synergies leveraged.


The main result of our research is a validated approach for a sustainable technology transfer network. The approach enables technology transfer on a regional as well as on international level and is built around a three phased new product development process (NPD) for research-industry-end-user cooperation. The first phase consists of idea genesis and opportunity identification, the second of opportunity analysis and concept development and the third of product development. Phase one takes place during networking events such as open labs and training courses. Phase two is conducted in short term multilateral projects. Phase three consists of long term projects in a consortium. After phase one and two gates are established to filter unfit ideas or concepts respectively. In the technology transfer network a large number of NPDs are running concurrently. The technology transfer network enables communication between the NPDs and leverages synergies on a regional level as well as in between RTOs on an international level.

The approach has been successfully tested within the ROBOTT-NET. A vibrant technology network on a regional and international level has been created. Over 2000 robot enthusiast have been personally reached, more than 200 product ideas were generated, and more than 100 projects were conducted.

Contribution to Scholarship

Our main contribution is an approach for successful implementation of an international scale technology transfer network in robotics and automation. Our approach leverages state of the art innovation models such as open innovation and the quadruple helix to improve technology and knowledge transfer between RTOs and industry on a regional level as well as in-between RTOs on an international level. Another contribution is the data we gathered within our international technology transfer network which is to our knowledge unique. The approach is designed for robotics but can certainly be adapted for other domains.

Contribution to Practice

Our research can be directly applied in practice. It can be used by RTOs to organize technology transfer with industry as well as with other RTOs more effectively. We provide insides on how to capture business opportunities and ideas from industry and merge them with technologies developed at RTOs to develop new products. The implementation of our approach in RTOs has shown great potential and already created many new business opportunities.


We describe how technology transfer can be organized. Our approach enables fostering a vibrant fuzzy front end and chosing the most promising ideas for product development and thus concurs with the theme “Transfer from research to industry and society: the role of the fuzzy front end in scientific organisations”.


Arnold, E., Clark, J., and Javorka, Z. (2010) Impacts of European RTOs. A Study of Social and Economic Impacts of Research and Technology Organisations: A Report to EARTO: Technopolis Group.

Carayannis, E.G. and Campbell, D.F. (2009) 'Mode 3' and 'Quadruple Helix'. Toward a 21st century fractal innovation ecosystem. International Journal of Technology Management, 46, 3/4, 201. doi: 10.1504/IJTM.2009.023374.

Chesbrough, H.W. (2010) Open innovation. The new imperative for creating and profiting from technology. Boston, Mass.: Harvard Business School Press.

Etzkowitz, H. and Leydesdorff, L. (2000) The dynamics of innovation. From National Systems and “Mode 2” to a Triple Helix of university–industry–government relations. Research Policy, 29, 2, 109–123. doi: 10.1016/S0048-7333(99)00055-4.

Koen, P., Ajamian, G., Burkart, R., Clamen, A., Davidson, J., D'Amore, R., Elkins, C., Herald, K., Incorvia, M., Johnson, A., Karol, R., Seibert, R., Slavejkov, A., and Wagner, K. (2001) Providing clarity and a common language to the "fuzzy front end". Research Technology Management, 44, 2, 46–55.

The Impact of the IPR Regulation on Academic Spin-offs’ Acquisitions


University of Messina, Italy


Academic Spin-Off (ASO) represents a valuable technology transfer channel and are one of the main tools for the exploitation of university research results. A fundamental step is science commercialization through academic entrepreneurship, when the inventions developed and patented inside the university are exploited with the new company creation (Mets, 2007).


The institutional context where entrepreneurs operate both constraints and facilitates the opportunities for starting and growing a business (Fini et al, 2017). However, the value creation of the scientific discoveries as well as the monetary, institutional, and organizational issues are factors that influence the commercialization of the university science (Perkmann et al., 2013). Several scholars have analysed the institutional characteristics that affects academic entrepreneurship (Huyghe & Knockaert 2015, Lehoux et al., 2017, Fini et al., 2017).Only recently, studies have adopted the institutional theory to focus on firms acquisition (Ahammad et al., 2018; Zhang & Greve, 2017; Li et al., 2017). Firms are driven by the need to acquire external technology and through the acquisition big companies encompass existing technology and the capabilities to develop new technologies (Arora et al., 1995). For some European countries acquisition may act as an important tool for fostering the economic dynamics and innovation (Xiao, 2015).

Literature Gap

The gap concerning the ASO acquisition is both theoretical and practical. First, the received few papers have grounded on institutional theory to specifically analyse quantity and quality of ASO, scientist entrepreneurial attitudes and the academic health sector. Second, much of the research have focused exclusively on USA’s companies.

Research Questions

The aim of this study is to focus on ASO acquisition as positive post-entry performance indicator. Digging deeper on legislative aspects that influence ASO growth, we focus on their acquisition and address the following research question: To what extent do institutional factors influence ASOs growth strategy and facilitate their acquisition?


This quantitative study draws on a new macro-level academic spin-offs database, based on information of Italian, Belgium and UK companies over a 10 years period (2009-2018). To test the hypotheses, we used the survival analysis in order to examine the time for a specific event to occur and the following regression has been run:

Acquisition = β0 +β1 IPR legislation+β2 Government Effectiveness+β3 GDP country+β4 R&D country+ β5 GDP region+β6 R&D region+ β7ROE+ β8 R&D firm

Dependent variable:

Acquisition (dummy variable);

Predictor variables:

IPR Legislation Changes, Government Effectiveness;

Control variables at Country, Regional and Firm Level:

GDP, R&D expenses, ROE.

Empirical Material

This study investigates differences in the institutional framework of Italy, Belgium and UK academic spin-off environment. These countries have a different structure in their science and technology national systems, and while evidence shows that UK represents a leading country at European level, cannot be said the same for Italy that represents an example of a late-follower country in this context, while Belgium stands in the middle.

Data have been collected in several stages. The final dataset includes time-variant information of almost 2.000 spin-off companies at European, national and regional level: 160 from Flemish, 920 from Italy and 1048 from UK. Nevertheless, only the Acquisition typology have been considered. Consequently, the final dataset contains 115 companies which can be distinguished in 15 Italian, 13 Flemish and 87 UK companies.


The results indicate that the new policies implementation and formulation has an effect on firms acquisition, but this outcome seems to be more representative than substantive. Therefore, even thought the IPR legislation is positively related with the acquisition event, the number of changes is not enough to explain the phenomenon (0.0623, p<0.05). Fini, Fu, Mathisen, Rasmussen, & Wright (2017) in their national and university level analysis on spin-offs’ quantity and quality confirm this result and showed that regulations positively affect the number of spin-offs created, but negatively affect the quality of these companies as measured by their ability to attract VC financing. However, the results of this study also reveal that the quality of policy formulation and implementation has a stronger impact on the likelihood of the ASO of being acquired (6.149, p<0.001).

These findings are worthy of interest because they confirm that the patent protection enforceability plays a significant role for ASO attractiveness and, in order to be acquired, ASO companies need to protect their technologies and innovative ideas.

Contribution to Scholarship

Our research makes two important contributions to the literature on academic spin-off (ASO). First, at a theoretical level, whereas received studies linked institutional determinants with entrepreneurial attitudes or with quantity and quality of spin-off created, this study, using the survival analysis, considers the factors that influence the acquisition rate at a particular point in time. Second, as a comprehensive and organic database providing data from different national and EU level ASO records is not currently available, we collected data on European spin-off companies over a 10 years period from different sources and built an original macro-level ASO database.

Contribution to Practice

The empirical analysis on ASO acquisition impact is of interest to university managers and policy makers. Understanding how many ASOs become target in the market provides a more relevant basis for deriving policy implications for entrepreneurial universities than simply ranking the number of ASOs created. Our results also suggest that ASOs growth strategies should properly accommodate legislative enforcement for promoting their acquisition.


Transfer from Research to Industry and Society is one of the 2019 R&D management conference theme. Our empirical analysis of academic entrepreneurship suggests an original point of view on science commercialization, with a particular emphasis on the antecedents of academic spin-off creation, how they develop, grow, and perform over time.


Ahammad, M. F., Konwar, Z., Papageorgiadis, N., Wang, C., & Inbar, J. (2018). R&D capabilities, intellectual property strength and choice of equity ownership in cross-border acquisitions: evidence from BRICS acquirers in Europe. R and D Management, 48(2), 177–194.

Arora, A., Fosfuri, A., & Gambardella, A. (1995). Markets for Technology: The Economics of Innovation and Corporate Strategy.

ASTP-Proton. (2015). ASTP-Proton SURVEY REPORT Fiscal Year 2015, 48.

Baldini, N. (2010). University spin-offs and their environment. Technology Analysis and Strategic Management, 22(8), 859–876.

Berger, P. L., & Luckmann, T. (1966). The social construction of reality. Penguin Group, 249.

Bigliardi, B., Galati, F., & Verbano, C. (2013). Evaluating Performance of University Spin-Off Companies : Lessons from Italy, 8(2), 178–188.

Bruton, G. D., Ahlstrom, D., & Li, H.-L. (2010). Institutional Theory and Entrepreneurship: Where Are We Now and Where Do We Need to Move in the Future? Entrepreneurship Theory and Practice, 34(3), 421–440.

Cesaroni, F., & Piccaluga, A. (2005). Universities and intellectual property rights in Southern European countries. Technology Analysis and Strategic Management, 17(4), 497–518.

Colombo, M. G., Adda, Æ. D. D., & Piva, Æ. E. (2010). of academic start-ups : an empirical analysis, 113–140.

Cox, D. R. (1972). Regression Models and Life-Tables, 34(2), 527–541.

Dahlstrand, Å. L. (1997). Entrepreneurial spin‐off enterprises in Göteborg, Sweden. European Planning Studies, 5(5), 659–673.

Delmar, F., Davidsson, P., & Gartner, W. B. (n.d.). Arriving at the high-growth firm.

Fini, R., Fu, K., Mathisen, M. T., Rasmussen, E., & Wright, M. (2017). Institutional determinants of university spin-off quantity and quality: a longitudinal, multilevel, cross-country study. Small Business Economics, 48(2), 361–391.

Fini, R., Grimaldi, R., Marzocchi, G. L., & Sobrero, M. (2008, December 8). The Determinants of Corporate Entrepreneurial Intention within Small and Newly Established Firms.

Grilli, L., Mrkajic, B., & Latifi, G. (2018). Venture capital in Europe: social capital, formal institutions and mediation effects. Small Business Economics, 51(2), 393–410.

Huyghe, A., & Knockaert, M. (2015). The influence of organizational culture and climate on entrepreneurial intentions among research scientists. Journal of Technology Transfer, 40(1), 138–160.

Kaufmann, D., Kraay, A., & Mastruzzi, M. (2010, September 1). The Worldwide Governance Indicators: Methodology and Analytical Issues.

Kroll, H., & Liefner, I. (2008). Spin-off enterprises as a means of technology commercialisation in a transforming economy—Evidence from three universities in China. Technovation, 28, 298–313.

Lehoux, P., Daudelin, G., Denis, J., & Miller, F. A. (2017). A Concurrent Analysis of Three Institutions that Transform Health Thechnology Based Ventures: Economic Policy, Capital Investment and Market Approval. Review of Policy Research, 0(0), 1–24.

Li, J., Xia, J., & Lin, Z. (2017). Cross-border acquisitions by state-owned firms: How do legitimacy concerns affect the completion and duration of their acquisitions? Strategic Management Journal, 38(9), 1915–1934.

Li, J., Xia, J., Shapiro, D., & Lin, Z. (2018). Institutional compatibility and the internationalization of Chinese SOEs: The moderating role of home subnational institutions. Journal of World Business, 53(5), 641–652.

Lockett, A., Siegel, D., Wright, M., & Ensley, M. D. (2005). The creation of spin-off firms at public research institutions: Managerial and policy implications. Research Policy, 34(7), 981–993.

Minshall, T., & Wicksteed, B. (2005). University spin-out companies : Tim Minshall and Bill Wicksteed, (January), 83.

Moray, N., & Clarysse, B. (2005). Institutional change and resource endowments to science-based entrepreneurial firms. Research Policy, 34(7), 1010–1027.

Mowery, D. C., Nelson, R. R., Sampat, B. N., & Ziedonis, A. A. (2001). The growth of patenting and licensing by U.S. universities: an assessment of the effects of the Bayh–Dole act of 1980. Research Policy, 30(1), 99–119.

Mowery, D., Nelson, R., Sampat, B., Policy, A. Z.-R., & 1999, undefined. (n.d.). The effects of the Bayh-Dole Act on US university research and technology transfer: An analysis of data from Columbia University, the University of California. Citeseer.

Nosella, A., & Grimaldi, R. (2009). University-level mechanisms supporting the creation of new companies: an analysis of Italian academic spin-offs. Technology Analysis & Strategic Management, 21(6), 679–698.

Rogers, E. M., Takegami, S., & Yin, J. (2001). Lessons learned about technology transfer. Technovation, 21(4), 253–261.

Roy, W. G. (1997). Socializing capital : the rise of the large industrial corporation in America. Princeton University Press.

Scott, W. R. (2008). Institutions and organizations : ideas and interests.

Shane, S. (2004). Academic entrepreneurship : university spinoffs and wealth creation. Edward Elgar.

Shane, S., & Foo, M.-D. (1999). New Firm Survival: Institutional Explanations for New Franchisor Mortality. Management Science, 45(2), 142–159.

Urbano, D., & Alvarez, C. (2014). Institutional dimensions and entrepreneurial activity: an international study. Small Business Economics, 42(4), 703–716.

Wright, M., Clarysse, B., Mustar, P., & Lockett, A. (2007). Academic Entrepreneurship in Europe. Academy of Management Learning Education (Vol. 8). Edward Elgar.

Xiao, J. (2015). The effects of acquisition on the growth of new technology- based firms : Do different types of acquirers matter ? Small Business Economics, 487–504.

Yang, D., Sheng, S., Wu, S., & Zhou, K. Z. (2018). Suppressing partner opportunism in emerging markets: Contextualizing institutional forces in supply chain management. Journal of Business Research, 90(October 2017), 1–13.

Zhang, C. M. A. N., & Greve, H. R. (2017). Institutional Logics and Power Sources: Merger and Acquisition Decisions: Journal articles, journal titles and books, 60(2), 671–694.

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