Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Session Overview
19-PM1-05: ST10.3 - Monitoring Technology Dynamics and Convergence
Wednesday, 19/Jun/2019:
1:00pm - 2:30pm

Session Chair: Stefanie Broering, Uni Bonn
Session Chair: Martin G. Moehrle, University of Bremen, Germany
Location: Room PC 21

Session Abstract

Technologies play a major role for the creation of product as well as process innovations and are able to establish a competitive advantage. For this reason, R&D managers should monitor their dynamics, in particular if driven by competitors or external research institutes. Furthermore, technologies might move towards each other; technology convergence might occur and change the business models of companies. On the other side, technologies might conquer new niches and form technology speciation.

All three tasks, monitoring technology dynamics, convergence as well as speciation, can be supported by advanced analyses. Time has come to consider all the possible methods and techniques to assess technology dynamics, convergence and speciation. Participants may consider – but not be limited to – the following:

Indicators for assessing technology dynamics, convergence and speciation

o patent- or publication-based

o blog or social network-based

o applying bibliometric or semantic approaches

o using network or indicator-based methods,

o applying advanced machine learning methods

Insights on processes leading to technology dynamics, convergence and speciation

o micro-foundations

o the importance of the time dimension

o life-cycle perspective

Insights on the determinants of technology dynamics, convergence and speciation

o specialization and diversification strategies

o search practices

o collaboration practices

o knowledge recombination

o network practices

Insights on the outcomes of technology dynamics, convergence and speciation

o emergence of general purpose technologies

o emergence of niche technologies

o emergence of radical or incremental innovations

o generation of new markets and industries

o market disruptions


Technological Drivers of Urban Innovation. A T-DNA Analysis Based on US Patent Data

Ngoc Uyen Phuong Nguyen, Martin G. Moehrle

University of Bremen, Germany, Germany


The world faces fast development of urbanization. It is necessary to solve related problems in a new way: urban innovation (Naphade et al. 2011). It focusses on connecting and integrating important infrastructures of cities (including ‘city governance, transportation, energy and water, healthcare, information and communication technologies, education and public safety’).


Several researches emphasize the importance of urban innovation. Recent papers are summarized with the main issues.

Meijer and Thaens (2018): ‘Social technical structure’ and the cooperation of many parts in the society (governments, companies, universities, institutes and people) in urban innovation

Han and Hawken (2018): The significance of data and ‘informational ecosystems’ in the development of technology and special cultures of the city as well as the enhancement of life quality in the urban innovation process

Caragliu and Del Bo (2018): Using patent counts in high-tech, ICT and particular smart city technologies to look for the influence of smart city policies on urban innovation and economic growth

Praharaj, Han and Hawken (2018): The integration of technologies and policy structure for the success of urban innovation

Colucci (2018): The adaptation of ‘societal transition models’ (changes in ‘culture, structure and behaviors of societal systems’) to urban innovation and climate change mitigation

Literature Gap

Many authors have already looked at different parts of urban innovation, such as sustainability or connectivity of infrastructures and socio-technological systems. Still the questions arise (in a quantitative way) what the technological drivers of urban innovation are and in which relationship they stand to each other?

Research Questions

We sharpen the questions raised in the literature gap. Based on a reliable source of data, can we find out what the technologies as drivers of urban innovation are and in which relationship they stand to each other?


To answer the research questions we suggest the use of a Technology-DNA (T-DNA) approach based on patent data. T-DNA is developed by analogy with ‘the DNA-sequence’ of creatures. A system structure needs to be defined, comprising the system, subsystem, super system and associated system. For instance, we interpret buildings constituting the system level; embedding environments such as energy supply, infrastructure for transportation or communication technologies as elements of the super system. Moreover, we assign patents to those system levels. We identify a series of dominant system levels over years and sketch some of their features (Roepke and Moehrle, 2014).

Empirical Material

Our study is based on the analysis of granted US patents from the period between 1976 and 2018.


Our approach sheds light on the drivers for urban innovation and the relationships between them. In detail, we find that the core system of buildings, the subsystem regarding parts of buildings and the associated system have only limited influence on the development of urban innovation. It is the super system, which drives dominantly. In particular, technologies in electricity and communication as well as technologies related to climate change and environment protection have had a major increase in terms of granted patents between 1976 and 2018 by factor ten (compared to other technologies with a factor around two or three). As those technologies had the same growth rate, we assume a possible relationship between them.

Contribution to Scholarship

Our study provides a classification of technologies regarding urban innovation in the framework of the T-DNA. The classification is based upon the Cooperative Patent Classification (CPC), which is a follow-up of the International Patent Classification (IPC). Other researchers could rely on this classification in all countries, in which one of these patent classifications (CPC or IPC) is used in the patenting system. For instance, they might compare the results from the USA with results from other developed countries, such as Canada, France, or the UK, and from emerging countries, such as China, India, or Brazil, to find out similarities and differences in the development of urban innovation.

Our results can lead researchers to a focus on such drivers of urban innovation that had major influence in the past and still have major influence currently in the present and in the future because many patents are still valid and alive.

Contribution to Practice

Our research may help managers in companies as well as politicians in urban areas. Managers can analyse the drivers of urban innovation based on the T-DNA structure, use it as technology monitoring system, and integrate major drivers in their business. Politicians can check if their decisions regarding urban innovation take account of all relevant elements of the four system levels. According to theses assessments, they can adapt to the new environmental situations and technological opportunities.


Urban innovation is a prominent field, in which bridges between research and industry as well as bridges to society are not only needed, but also responsible for successful innovations. Without new ideas from research, without new products and services from industry, and without acceptance and contributions from the citizens, no development is possible.


Caragliu, A. and Del Bo, C. F. (2018) ‘Smart innovative cities: The impact of Smart City policies on urban innovation’, Technological Forecasting and Social Change, pp. 1–11.

Colucci, A. (2018) ‘The Transition Approach in Urban Innovations: Local Responses to Climate Change’, in Smart, Resilient and Transition Cities. ed. by Galderisi, A. and Colucci, A. Elsevier Inc., pp. 19–28. Available from:

Han, H. and Hawken, S. (2018) ‘Introduction: Innovation and identity in next-generation smart cities’, City, Culture and Society, 12, pp. 1–4.

Han, J. et al. (2012) ‘Innovation for sustainability: toward a sustainable urban future in industrialized cities’, Sustainability Science, 7(1), pp. 91–100.

Meijer, A. and Thaens, M. (2018) ‘Urban Technological Innovation: Developing and Testing a Sociotechnical Framework for Studying Smart City Projects’, Urban Affairs Review, 54(2), pp. 363–387.

Nam, T. and Pardo, T. A. (2011) ‘Smart City as Urban Innovation: Focusing on Management, Policy, and Context’, in Proceedings of the 5th international conference on theory and practice of electronic governance, ACM, pp. 185–194.

Naphade, M. et al. (2011) ‘Smarter cities and their innovation challenges’, Computer, 6, pp. 32–39.

Praharaj, S., Han, J. H. and Hawken, S. (2018) ‘Urban innovation through policy integration: Critical perspectives from 100 smart cities mission in India’, City, Culture and Society, 12, pp. 35–43.

Roepke, S. and Moehrle, M. G. (2014) ‘Sequencing the evolution of technologies in a system-oriented way: The concept of technology-DNA’, Journal of Engineering and Technology Management, 32, pp. 110–128.

State-of-the-Art in measuring convergence at different levels

Stefanie Bröring, Michael Wustmans

Univeristy of Bonn, Germany


Technology dynamics is an upcoming research stream focusing on changes either caused by a certain technology or by changes occurring within a technological field. Measurement and anticipation of such changes is key for firms in order to react and adapt in a timely manner.


These changes can be caused by the phenomenon of (technology) convergence, hence, the blurring of hitherto different technological paradigms as one can witness in Nano-Bio-Info convergence with new fields such as “nano sensors” where different areas of technology form novel substructures complementing the extant ones. The ability to sense those dynamics, in particular those relating to substitutive convergence, where the old technologies are fading out, becomes an ever increasing meta-competence for actors involved. Convergence as such is defined as ‘the blurring of technical and regu-latory boundaries between sectors of the economy’ (OECD, 1992, p. 13). Thereby, the process of convergence is often described as a sequential process with four steps, namely science, technology, market and industry convergence (Curran et al. 2010). These steps are characterized by a decreasing distances resulting in (structural) chang-es in the respective industry (Sick et al., 2018).

Literature Gap

These steps can be measured and anticipated by using different data sources, i.e. secondary data sources (publications, patents, trends, M&A, product launches); or primary data sources (expert surveys, case studies), as well as by using different (quantita-tive or qualitative) research methods (Bornkessel et al., 2016; Sick et al., 2018).

Research Questions

However, a systematic literature review of these different possibilities along with their similarities and differences is still missing.


In order to fill this gap, we conduct a systematic literature review on how convergence is measured on different levels. Our method is based on Niemann et al. (2017) who use the so-called lane diagrams to identify patterns within a technological field using patents as a data source. For the purpose of a systematic literature review, we adapt this approach by using publications as a data source in order to identify different research streams and approaches for operationalization the assessment of convergence on different levels.

Empirical Material

More precisely, we extracted publications from Web of Science using the following search string for the fields “Title” and “Topic”: (“knowledge converg*“ OR “technol-ogy converg*“ OR “market converg*“ OR “industry converg*“ OR “converging knowledge“ OR “converging technologies“ OR “converging markets“ OR “converging industries“) AND (measur* OR analy* OR model* OR anticipat* OR investig* OR assess*). In total, we identified 517 publications, released until 09.11.2018, which were tagged within the indices WOS, KJD, MEDLINE, RSCI, and SCIELO. After ex-clusion of search results with either no or a non-English title and abstract, we obtained 500 publications. Following Niemann et al. (2017), we conduct semantic analyses in order to develop what we call a publication lane diagram. The underlying semantic approach relies on the comparison of sequences of n terms (n-grams) from the titles and abstracts of the publications. Therefore, we use the software PatVisor following the suggestions by Moehrle (2010), Moehrle and Gerken (2012), and Walter et al. (2017). Accordingly, we apply stop word and synonym filters, and a lemmatization for manipulating the textual input data. For the subsequent similarity measurements, we extract bi-grams in a window of two and apply a Jaccard similarity coefficient, fol-lowing suggestions by Moehrle (2010).


Based on the derived publication lanes, we are able to identify the most frequent measuring techniques and deduce similarities and differences within and across the publication lanes. Thereby, we focus on different dimensions to characterize the re-spective approaches. These include the convergence level (knowledge, technology, market, or industry), data source (primary or secondary data), and research methods (quantitative or qualitative).

Contribution to Scholarship

Our results reveal that adapting the lane diagram approach by Niemann et al. (2017) for a systematic literature review is feasible since we identified different research are-as and similar research methods for measuring convergence on different levels. Fur-thermore, based on our results, pathways for future research can be derived leading to suggestions which different data sources and research methods might be combined to anticipate (and measure) convergence as an indicator for change to better understand dynamics caused by or within a respective technology.

Contribution to Practice

Additionally, firms are able to get an overview of different measurement and anticipa-tion techniques of technological changes in order to react and adapt in a timely man-ner.


This research will give an overview of different methods for anticipating technological change caused by convergence. This is relevant for both, researcher and practitioners, and lead to bridging research methods with practical/industrial challenges.


Bornkessel, S., Bröring, S., & Omta, S. O. (2016). Crossing industrial boundaries at the pharma-nutrition interface in probiotics: A life cycle perspective. PharmaNutri-tion, 4(1), 29-37.

Curran, C. S., Bröring, S., & Leker, J. (2010). Anticipating converging industries using publicly available data. Technological Forecasting and Social Change, 77(3), 385-395.Moehrle, M. G. (2010). Measures for textual patent similarities: a guid-ed way to select appropriate approaches. Scientometrics, 85(1), 95-109.

Moehrle, M. G., & Gerken, J. M. (2012). Measuring textual patent similarity on the basis of combined concepts: design decisions and their consequences. Scien-tometrics, 91(3), 805-826.

Niemann, Helen; Moehrle, Martin G.; Frischkorn, Jonas (2017). Use of a new patent text-mining and visualization method for identifying patenting patterns over time. Concept, method and test application. Technological Forecasting and So-cial Change 115, pp. 210–220. DOI: 10.1016/j.techfore.2016.10.004.

OECD (1992). Telecommunications and Broadcasting: Convergence or Collision? No. 29, OECD Digital Economy Papers, No. 5, OECD Publishing, Paris.

Rosenberg, N. (1963). Technological change in the machine tool industry, 1840–1910. The Journal of Economic History, 23(4), 414-443.

Sick, N., Preschitschek, N., Leker, J., & Bröring, S. (2018). A new framework to assess industry convergence in high technology environments. Technovation (in press).

Walter, L., Radauer, A., & Moehrle, M. G. (2017). The beauty of brimstone butterfly: novelty of patents identified by near environment analysis based on text mining. Sci-entometrics, 111(1), 103-115.

Analysing scientific dynamics – Does machine learning help to predict scientific convergence based on bibliographic data?

Tetyana Melnychuk1, Lukas Galke1,2, Eva Seidlmayer3, Michael Wustmans4, Klaus Tochtermann1,2, Konrad Förstner3, Stefanie Bröring4, Carsten Schultz1

1Kiel University; 2Leibniz Information Centre for Economics (ZBW); 3German National Library of Medicine (ZB MED) - Information Centre for Life Sciences; 4University of Bonn


Scientific and technology convergence blur the boundaries between scientific fields and technological applications. They represent the basis for industry convergence characterized by a combination of technological capabilities and customer needs from different industry sectors (Bröring et al., 2006; Curran et al., 2010).


Scientific convergence is particularly difficult to predict for companies, since in the phase of basic research, markets or even application fields are unknown (Curran and Leker, 2009). However, active involvement into scientific convergence at the early stage of converging processes may create unique competitive advantages for firms. Therefore, it is crucial for firms to analyse the signs of scientific convergence at the embryonic stage. Nevertheless, the current literature in R&D management does not provide validated instruments that may help to predict the future dynamics of scientific and technology fields (Kim and Lee, 2017). Scientific convergence has its origin in scientific communities and networks. Therefore, scientific publications may provide relevant indications to detect scientific development early. Scientific convergence highly depends on knowledge diffusion in scientific communities and has been analysed from the network perspective with scientometric methods (Jeong et al., 2016; Jeong et al., 2018).

Literature Gap

However, some of widely used topic modelling approaches based on unsupervised learning methodology which are used for investigation of technology convergence (Venugopalan and Rai, 2015) have a limitation in terms of overly common and unspecific terminology which is supposed to represent distinct topics, thus, providing little technological content.

Research Questions

To address the issues of handling large amount of bibliographic data and using an appropriate clustering method, we focus in the present study on analysis and forecast of dynamics and convergence of scientific areas and networks by applying a novel machine learning approach based on graph convolutional and attention networks.


We exert the proposed method on a case of cholesterol-lowering agents to quantitatively demonstrate scientific convergence of drug-based therapies and disease prevention approaches from the perspective of functional foods.

Empirical Material

Our dataset comprises 28,2 scientific publications from the life sciences domain (PubMed with extensions from the ZB MED Knowledge Environment). To identify the patterns of scientific convergence, we group the data according to the controlled vocabulary of medical subject headings (MeSH terms) (Guo et al., 2011; Leydesdorff et al., 2012; Rotolo et al., 2015). By exploiting MeSH terms and bibliographic data of MEDLINE database, Leydesdorff et al. (2012) investigate the evolution of medical innovations arising from “translations and interactions” of “three main branches” (i.e. diseases, drugs and chemicals, and techniques and equipment) and demonstrate knowledge diffusion dynamics of three scientific areas over time (Leydesdorff et al., 2012). To validate our method, we compare our results with those of a scientific and technology convergence case on phytosterols. This case has been extensively studied (Curran et al., 2010) with the help of investigation of co-authorship and patent classification performed by a frequency-based analysis and visualization software (Curran and Leker, 2009; Curran and Leker, 2011).


Typical topic clustering methods which rest on similar co-citations (Upham et al., 2010) leave many publications, those citations are very diverse, outside of topic clusters. Though, such publications are decisive for scientific convergence. However, promising new techniques that apply neural networks to graph-structured data have recently emerged. Those include graph convolutional networks (Kipf and Welling, 2016, 2017) and graph attention networks (Velickovic et al., 2018). We apply these techniques to derive a quantitative similarity measure as indicator for scientific convergence and impose a graph structure on our dataset. A graph is denoted as a set of nodes and edges. Apart from the publications themselves, we consider authors and keywords as nodes. The edges are inserted according to each papers’ authors and keywords annotations. The resulting graph consists of 43,7 million nodes and 104,4 million edges. On this data, we train a graph convolutional network for link prediction that refers to predicting the probability of an edge between two nodes. The model learns a latent (vector) representation of each node that is useful to correctly predict links. We use this latent representation to derive a similarity measure among the nodes (authors, papers, keywords) to analyze research dynamics such as scientific convergence.

Contribution to Scholarship

The results indicate that our machine learning method may enable researchers and R&D managers to forecast changes within a distinct scientific field. Hence, we contribute to the increasing literature on research dynamics and convergence processes by demonstrating a novel approach that uses the vast data provided by scientific publications for the analysis of scientific convergence that constitutes the fundament for technology, market, and industry convergence processes. Future research may use our approach to investigate the knowledge diffusion within and between distinct scientific and technology areas and to analyze how these dynamics create the basis for technological innovations which may later lead to development of new product and services.

Contribution to Practice

We discuss a possible future development of a software tool, that uses the demonstrated machine learning algorithm and may give R&D managers a helpful instrument for technology foresight. As a first step in this direction, we will publish the source code along with our paper, such that our methods can be reused.


The investigated scientific knowledge diffusion from distinct research areas into a new converged field demonstrates that such scientific outcome may create the foundation for technology innovations which may lead to emergence of a new industry segment.


Bröring S, Cloutier LM and Leker J. (2006) The front end of innovation in an era of industry convergence: evidence from nutraceuticals and functional foods. R&D Management 36: 487-498.

Curran C-S, Bröring S and Leker J. (2010) Anticipating converging industries using publicly available data. Technological Forecasting and Social Change 77: 385-395.

Curran C-S and Leker J. (2009) Employing STN AnaVist to forecast converging industries. International Journal of Innovation Management 13: 637-664.

Curran C-S and Leker J. (2011) Patent indicators for monitoring convergence – examples from NFF and ICT. Technological Forecasting and Social Change 78: 256-273.

Guo H, Weingart S and Börner K. (2011) Mixed-indicators model for identifying emerging research areas. Scientometrics 89: 421-435.

Jeong D-h, Cho K, Park S, et al. (2016) Effects of knowledge diffusion on international joint research and science convergence: Multiple case studies in the fields of lithium-ion battery, fuel cell and wind power. Technological Forecasting and Social Change 108: 15-27.

Jeong D, Lee K and Cho K. (2018) Relationships among international joint research, knowledge diffusion, and science convergence: the case of secondary batteries and fuel cells. Asian Journal of Technology Innovation 26: 246-268.

Kim J and Lee S. (2017) Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020. Scientometrics 111: 47-65.

Kipf T and Welling M. (2016) Variational Graph Auto-Encoders. In: 30th Conference on Neural Information Processing Systems.

Kipf T and Welling M. (2017) Semi-Supervised Classification with Graph Convolutional Networks. In: 5th International Conference on Learning Representations.

Leydesdorff L, Rotolo D and Rafols I. (2012) Bibliometric perspectives on medical innovation using the medical subject Headings of PubMed. Journal of the American Society for Information Science and Technology 63: 2239-2253.

Rotolo D, Hicks D and Martin BR. (2015) What is an emerging technology? Research Policy 44: 1827-1843.

Upham SP, Rosenkopf L and Ungar LH. (2010) Innovating knowledge communities. Scientometrics 83: 525-554.

Veličković P, Cucurull G, Casanova A, Romero A, Liò P and Bengio Y. (2018) Graph Attention Networks. In: 6th International Conference on Learning Representations.

Venugopalan S and Rai V. (2015) Topic based classification and pattern identification in patents. Technological Forecasting and Social Change 94: 236-250.

University spin-offs between soft and hard patents

Valentina Stanzione1, Alberto Nucciarelli1, Sandro Etalle2, Bert Sadowski2

1University of Trento, Italy; 2Eindhoven University of Technology


In recent years, attention has been paid to the entrepreneurial orientation of universities and their ability to exploit and transfer scientific knowledge to firms. Universities are considered an important source of new innovations and are increasingly seen as a breeding ground for innovative spin-offs.


We have looked at the literature on university spin-off to grasp its main characteristics, investigate the origins of the phenomenon and identify the main drivers. Moreover, we have analyzed the role and importance of Intellectual Property (IP) in promoting innovation and as a tool to protect competitive advantage based on knowledge.

We have also delved our attention into the literature on software patents to have a clear understanding of the particular nature of software and how it can be protected. In this respect, we paid attention to compare the European, and North American US patent legislation to understand how policy makers have regulated IP for software. as well as the criteria by which a software patent can be issued.

Literature Gap

Literature has so far neglected the validity of the protection system in the case of software patents, and the effects of this type of patents on the growth and performance of the Spin-Offs. No research exists that establishes a link between these two aspects.

Research Questions

We aim to demonstrate the existence of hard patents and soft patents whereas soft patents are relatively weak to appropriate the value of an invention related to software. This weakness displays both in terms of market value of a patent and of defensibility in case of litigation.


We employ qualitative analysis (Eisenhardt and Graebner, 2007). In particular, we use comparative case studies built on primary and secondary data from 2 Dutch and 2 Italian spin-offs. We collected primary data have with semi-structured interviews conducted between February - March 2019 upon the completion of questionnaires.

Empirical Material

We conducted 8 interviews (2 per case study) and reviewed printed material (when available) as well as web sites. We took the first round of interviews at the beginning of the project just after our contact persons filled the questionnaire. We took the second round of interviews after we compared the case studies to validate our findings.


The interviews highlighted a general lack of confidence in the effectiveness of software patents as a tool for protection. The University Spin-Offs with soft definable patents show difficulty in being able to sell their patent to existing companies with the sole proof that it works, as it is considered easy to copy without exploiting it. Necessary for the sale is a complete product, possibly with a brand, even the one protected by a patent. Trying to sell only the software patent to a company also exposes the Spin-Off to the risk of being copied. In such circumstances, a small and new business will hardly have the resources to start a dispute, especially when the competitor is a large company. There is also the likelihood that the large company may have a patent that the small business violates. Moreover, the development of a commercial software product usually requires several years of work, and therefore also considerable investments, after the Spin-Off foundation, while shorter times seem necessary for products with hard patents. As a result, making revenues is harder for software Spin-Offs and funding is essential at this stage.

Contribution to Scholarship

In case of software inventions, many changes occur that need to be taken into consideration: product life, relationship between the description and implementation of the invention, multiplicity of "subsystems" that a software system includes, and the possibility to know for all if a patent already exists. As a result, it is hard to recognise the software patent a true market value. In the software industry, a patent tends to have a relatively low value due to the ease of designing a distinct product. As a consequence, it is undeniable the importance of the software industry in the modern economy, the importance of innovation for that industry, as well as that today innovation comes increasingly from small business contexts, such as the Spin-Offs.

Contribution to Practice

Based on this evidence, this paper intends to underline the difficulty of adapting traditional intellectual property regimes to software innovation, inviting to reflect on possible new interventions that can be made to strengthen the protection of software patents. Another objective is also to further investigate the influence that this category of patents has on the Spin-Off phenomenon, with the ultimate goal of understanding how to help them prosper and thus benefit from the advantages in terms of innovation that can derive the community.


Spin-Offs represent a bridge between research and practice. By doing so, they provide us with meaningful insights for discussion during the R&D Management Conference.


Baroncelli A., Landoni M. (2017), Exploring differences in university support practices and the effects on Spin-Off companies in Boston, in International Journal of Entrepreneurship and Innovation Management

Baum J.A.C., Silverman B.S. (2004), Picking Winners or Building Them? Alliances, Patents, and Human Capital as Selection Criteria in Venture Financing of Biotechnology Startups, Journal of Business Venturing

Birley S. (2002), Universities, Academics, and Spinout Companies: Lessons from Imperial, International Journal of Entrepreneurship Education

Boh W.F., De-Haan U., Strom R. (2016) University technology transfer through entrepreneurship: faculty and students in spinoffs, in The Journal of Technology Transfer

Bolzani D., Fini R., Grimaldi R., Sobrero M. (2014) University Spin-Off and their impact: Longitudinal evidence from Italy, in Economia e politica industriale

Borges, C. & Filion, L. J. (2013). Spin-Off process and the development of academic entrepreneur’s social capital. Journal of Technology

BVCA – British Venture Capital Association (2005), Creating Success from University Spin-Outs. Last retrieved: 19th December 2009 from:

Calcagnini G., Favaretto I. (2016) Models of university technology transfer: analyses and policies, The Journal of Technology Transfer.

Cesaroni F., Moscara P., Piccaluga A. (2005), Le Imprese Spin-Off della Ricerca in Italia: Modelli di Sviluppo e Percorsi di Crescita, Piccola Impresa – Small Business

Chesbrough H., Crowther A.K. (2006), Beyond high tech: early adopters of open innovation in other industries, R&D Management

Colombo M., D’Adda D., Piva E. (2010), The contribution of university research to the growth of academic start-ups: an empirical analysis, Journal of Technology Transfer

Davila A., Foster G., Gupta M. (2003), Venture Capital Financing and the Growth of Startup Firms, Journal of Business Venturing

Di Gregorio D., Shane S. (2003), Why do some universities generate more start-ups than others?, Research Policy

Djokovic D., Souitaris V. (2004), Spinouts from Academic Institutions: A Literature Review with Suggestions for Further Research, Cass Business School City of London, Working Paper Series. Last retrieved: February 2009 from:

Dechenaux, E., Thursby J. and Thursby M. (2011), Inventor moral hazard in university licensing: The role of contracts, Research Policy

Eisenhardt, K. M., & Graebner, M. E. (2007). Theory building from cases: Opportunities and challenges. Academy of management journal, 50(1), 25-32.

Etzkowitz H., Webster A., Gebhardt C, Cantisano Terra B.R. (2000), The Future of the University and the University of the Future: Evolution of Ivory Tower to Entrepreneurial Paradigm, Research Policy

Feldman M., Feller I., Bercovitz J., Burton R. (2002), Equity and the Technology Transfer Strategies of American Research Universities, Institute for Operation Research and the Management Sciences (INFORMS)

Fini R., Grimaldi R., Santoni S., Sobrero M. (2011), Complements or substitutes? The role of universities and local context in supporting the creation of academic Spin-Off. Research Policy

Henrekson M., Rosenberg N. (2001) Designing efficient institutions for science-based entrepreneurship: lesson from the US and Sweden, Journal of Technology Transfer

Heirman A., Clarysse B. (2004), The Initial Resources and Market Strategy to Create High Growth Firms, Working Paper Steunpunt OOI

Kalanje C. M. (2017) Role of Intellectual Property in Innovation and New Product Development, WIPO

Kenney M., Patton D. (2009) Reconsidering the Bayh-Dole Act and the current university ownership model, Research Policy

Lee C., Lee K., Pennings J.M. (2001) Internal Capabilities, External Networks, and Performance: A Study of Technology-Based Ventures, Strategic Management Journal

Leifer R., McDermott C.M., O'Connor G.C., Peters L.S., Rice M., Veryzer R.V. Jr. (2002), Radical Innovation: How Mature Companies Can Outsmart Upstarts, Harvard Business School Press

Leith P. (2011) Software and patents in Europe, Cambridge intellectual property Rights and information law, Cambridge University Press

Link, A. N., Scott J. T. (2005) Opening the ivory tower’s door: An analysis of the determinants of the formation of U.S. university Spin-Off companies. Research Policy

Lockett, A., Wright, M. (2005). Resources, capabilities, risk capital and the creation of university spin-out companies. Research Policy

Macho-Stadler I., Perez-Castrillo D., Veugelers R. (2008) Designing contracts for university Spin-Off, Journal of Economics and Management Strategy

Mowery D., Nelson R., Sampat B., Ziedonis 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

Müller K. (2010), Academic Spin-Off’s transfer speed – Analyzing the time from leaving university to venture, in Research Policy

Mustar P., Renault M., Colombo M.G., Piva E., Fontes M., Lockett A., Wright M., Clarysse B., Moray N. (2006), Conceptualizing the Heterogeneity of Research-Based Spin-Off: A Multi-Dimensional Taxonomy, Research Policy

Nard C. A. (2008) The Law of Patents, Aspen Publishers, New York

Niosi J. (2006), Success Factors in Canadian Academic Spin-Off, Journal of Technology Transfer

Phan, P. H., Siegel, D. S., & Wright, M. (2005). Science parks and incubators: Observations, synthesis and future research. Journal of Business Venturing

Piccaluga A. (2001) La valorizzazione della ricerca scientifica. Come cambia la ricerca pubblica e quella industriale, Franco Angelli

Piccarozzi M. (2016) Dagli Spin-Off universitari alle start-up. Innovazione nella creazione d'impresa, CEDAM

Pirnay F., Surlemont B., Nlemvo (2003) Toward a Typology of University Spin-Off, Small Business Economics

Powers, J. B., & McDougall, P. P. (2005) University start-up formation and technology licensing with firms that go public: A resource-based view of academic entrepreneurship. Journal of Business Venturing

Rivette K., Kline D. (2000) Discovering New Value in Intellectual Property, Harvard business review

Roberts E.B. (1991), Entrepreneurs in High Technology. Lessons from MIT and Beyond. Oxford University Press. New York

Scherer F.M. (2005) Patents: economics, policy and measurement, Cheltenham; Northampton, Mass.: Elgar

Schofield T. (2013) Critical Success Factors for Knowledge Transfer Collaborations between University and Industry, working paper, Imperial College London

Shane S., (2004) Academic Entrepreneurship: University Spinoffs and Wealth Creation, Edward Elgar Publishing

Tirole J. (2017) Economics for the Common Good, Princeton University Press

Toshiko T. (2008) Patent law and theory: a handbook of contemporary research, Cheltenham; Northampton, Mass.: Elgar

Visintin F., Pittino D. (2014) Founding team composition and early performance of University – Based Spin-Off companies. Technovation

Walter M. M., Von Lewinski S. (2010) European copyright law, a commentary, Oxford University Press, Oxford.

Wright M., (2014) Academic entrepreneurship, technology transfer and society: where next? in The Journal of Technology Transfer