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
Session
19-PM1-12: ST8.5 - Disclosure and Exclusion: The Challenges of Collaboration in Open Innovation
Time:
Wednesday, 19/Jun/2019:
1:00pm - 2:30pm

Session Chair: Janet Bercovitz, University of Colorado, Leeds School of Business
Session Chair: Martin Hetu, HEC Paris
Session Chair: Denisa Mindruta, HEC Paris
Location: Room PC 23

Session Abstract

Over the last decades, innovators have increasingly relied on external collaboration for knowledge insourcing (Chesbrough, 2003). This shift in the innovation process from inbound knowledge to ongoing collaboration requires organizations to manage the knowledge flows across their boundaries (Chesbrough and Bogers, 2014) and to decide the right mix of knowledge sharing and protection against knowledge spillovers (Arora, Athreye, and Huang, 2016).

Some collaborations occur under the shadow of intellectual property rights enforcement. They involve exclusive dealings and the use of a range of governance mechanisms to prevent knowledge spillovers to external partners. While the locus of innovation shifts from inside to outside knowledge sources (Laursen and Salter, 2006), focal actors still maintain a tight control on proprietary knowledge.

Other collaborations adhere to norms of knowledge dissemination and disclosure of discoveries (David, 1998). “Openness” in such situations implies not only reliance on external collaborators but also purposeful knowledge sharing and non-enforcement of intellectual property rights (IPR). The collaborative models underlining knowledge-production activities span a wide range of practices, including firm participation in patent pools (Vakili, 2016), contribution to open source software (Nagle, 2018; Wen et al, 2016), and multisectoral collaboration for translational drug and therapeutic R&D (Bubela et al, 2012).

These contrasting trends raise numerous questions about the use of disclosure and exclusion strategies in Open Innovation and the effect of these choices on value creation and value capture by innovators. Accordingly, the proposed special track aims at stimulating a wider discussion on why and when firms use one strategy versus the other, and why and when they combine disclosure and exclusion and to what effect.

Our interests on these topics are far ranging, extend to different levels of analysis and broadly cover the research, development and commercialisation stages of innovation. The following is an indicative (but not exhaustive list) of topics to guide potential participants :

1. What are the various determinants of selective revealing of knowledge (Dahlander and Gann, 2010; Alexy et al., 2013; Henkel et al., 2014) and how does selective revealing affect innovation performance and value capture by firms?

2. How do firms organize internally to combine knowledge sharing and protection against valuable knowledge leakages? How do incumbent and startup firm differ in their ability to implement and benefit from knowledge sharing/protection strategies?

3. How do firms choose and incentivize partners to share or protect knowledge in innovation collaboration? How does the balance of cooperation and competition between partners change under various degrees of knowledge protection chosen by participating actors?

4. How does firms’ practices of knowledge sharing and exclusion vary across different institutional and legal contexts and technological regimes? What is the impact of these practices on the large business ecosystems?

5. What are some reputation, status, and competitive effects of protection and disclosure in open innovation?


Show help for 'Increase or decrease the abstract text size'
Presentations

Open Innovation practices of firms within an emerging personalized medicine innovation ecosystem

Andrew Park1, Elicia Maine2

1Simon Fraser University, Canada; 2Simon Fraser University, Canada

Context

Personalized medicine is a rapidly growing subsector spanning medicine, biotechnology, and information technology, which is forecast to transform medicine, bringing benefits to patients and medical professionals and reducing overall system costs. Little is known in the innovation management or innovation policy literatures about the emergence of personalized medicine ecosystems.

Literature

We draw on and contribute to the Innovation Ecosystem, Open Innovation and Science Entrepreneurship bodies of literature. Whereas innovation ecosystems have been widely studied, far less has been studied on the emergence of science-based innovation ecosystems. Science-based clusters can be seeded by the formation of ventures by star scientists co-located with their university or research hospital (Zucker & Darby, 1998; Maine et al, 2014). Science-based innovation ecosystems rely heavily on open innovation: the science ventures seeding these ecosystems frequently need to access complementary assets, finance, and may need to contribute to the formation of new regulations and policies. It is uncertain how science-based ventures differ in managing spillovers during collaboration (Arora et al., 2016). If firms are successful in contributing to a growing innovation ecosystem using open innovation strategies (Chesbrough, 2006), there can be broader societal and public policy implications in encouraging further open innovation (Chesbrough & Bogers, 2014).

Literature Gap

Dahlander & Gann (2010) note that most open innovation work focuses on observations from American software technology companies such as Microsoft, Intel, and the Linux Foundation. The authors encourage future work to explore other contexts to improve external validity.

Research Questions

We build on selective revealing to enhance value creation and capture (Dahlender & Gann, 2010). “What are the various determinants of selective revealing of knowledge (Dahlander & Gann, 2010; Alexy et al., 2013; Henkel et al., 2014) and how does selective revealing affect innovation performance and value capture by firms?

Methodology

To address this research question, we investigate the emergence of a personalized medicine innovation ecosystem in BC, analysing the open innovation mechanisms employed by science-based ventures and the value outputs of these firms. To do so, we first identified firms with personalized medicine capabilities. We then gathered further firm level data including revenue, employees, VC financing, and relevant patents. Value outputs of each firm were calculated by normalizing annual revenue to the age of the firm to rank commercial value created by the firm in the personalized medicine ecosystem.

Empirical Material

Firms were then classified by status (active or defunct), spinout institution, degree of selective revealing (relevant US patent granted), whether intellectual property was strategically timed (having a US patent that was granted within 5 years of founding), level of technological uncertainty.

Results

We identified 95 Personalized Medicine firms founded in B.C., 65 of which are currently active. 45% of these active firms were in therapeutics, 8% in medical devices, 31% in diagnostics, and 17% in digital health. Digital health firms were mostly founded after 2010, whereas therapeutics and diagnostics firms were founded more evenly in the last several decades.

Selective revealing and strategic timing appear to play a role in commercialization success. For example, 74% of high and medium value creating therapeutics firms exhibit selective revealing, compared to 30% for low value creators. Moreover, we find evidence of the importance of strategic timing and of venture capital financing to value creation by personalized medicine ventures. A matched comparison of high and low value creators by founding year shows that 71% of high value creators exhibit strategic timing of patents vs. only 14% for low value creators. 70% of the top 20 firms ranked by Value Output reported venture capital financing.

Contribution to Scholarship

We contribute to the “selective revealing” body of literature by addressing the seemingly contradictory positions of Henkel et al. (2014), who argue early selective revealing positively effects firms’ competitiveness and West (2003) who states firms prefer proprietary strategies “whenever possible”. Our results show selective revealing in personalized medicine firms tends to lead to higher value outputs moderated by uncertainty of the environment. Strategic timing of patents also appears to play a role. This suggests an open innovation framework can be helpful to a firm’s commercialization, but a firm must also consider the breadth and timing of its intellectual property protection (Maine & Thomas, 2017). Dahlender & Gann (2010) encourage future work to explore other contexts to improve external validity. Our study focuses on the emerging personalized medicine industry, which exists in a more uncertain environment.

Contribution to Practice

We contribute to practice by providing initial guidelines and insights to both individual firms and public policy makers to encourage the growth of the personalized medicine ecosystem in their jurisdictions. Given the long timelines to commercialization, particularly for personalized medicine therapeutics companies (Maine & Seegopaul, 2016), and the risks and benefits involved in openness and selective revealing, firms must strategically navigate not only their own technological capability development but also their relationships with surrounding firms, universities and other public entities.

Fitness

Our study is directly relevant to the main conference theme of R&D Management 2019 “The Innovation Challenge”. Our study examines science-based ventures which bridge academic research and industry with technologies which promise to have an enormous positive impact on society. We also contribute to the theme of track 8.5.

Bibliography

Alexy, O., George, G., Salter, A., 2013. Cui bono? The selective revealing of knowledge and its implications for innovative activity. Academy of Management Review 38(2), 270–291.

Arora, A. Athreye S, Huang, C. 2016. The paradox of openness revisited: collaborative

innovation and patenting by UK innovators. Research Policy 45: 1352-1361

Bubela, Tania, Garret A. FitzGerald & E. Richard Gold. 2012. Recalibrating Intellectual

Property Rights to Enhance Translational Research Collaborations. Science Translational Medicine 4(122): 122cm3.

Chesbrough, H. W. (2006). Open innovation: The new imperative for creating and profiting from technology. Harvard Business Press.

Chesbrough, H., & Bogers, M. (2014). Explicating open innovation: Clarifying an emerging paradigm for understanding innovation. New Frontiers in Open Innovation. Oxford: Oxford University Press, Forthcoming, 3-28.

Dahlander, L., Gann, D.M., 2010. How open is innovation? Research Policy 39 (6),699–709.

Henkel, J., Schöberl, S., & Alexy, O. (2014). The emergence of openness: How and why firms adopt selective revealing in open innovation. Research Policy, 43(5), 879-890.

Maine, E., & Seegopaul, P. (2016). Accelerating advanced-materials commercialization. Nature materials, 15(5), 487.

Maine, E. M., Shapiro, D. M., & Vining, A. R. (2010). The role of clustering in the growth of new technology-based firms. Small Business Economics, 34(2), 127-146.

Maine, E., & Thomas, V. J. (2017). Raising financing through strategic timing. Nature

nanotechnology, 12(2), 93.

Maine, E., Thomas, V. J., Bliemel, M., Murira, A., & Utterback, J. (2014). The emergence of the nanobiotechnology industry. Nature nanotechnology, 9(1), 2.

Pisano, G. P. (2010). The evolution of science-based business: innovating how we

innovate. Industrial and corporate change, 19(2), 465-482.

West, J. (2003). How open is open enough?: Melding proprietary and open source platform strategies. Research policy, 32(7), 1259-1285.

Zucker, L. G., & Darby, M. R. (1996). Star scientists and institutional transformation: Patterns of invention and innovation in the formation of the biotechnology industry. Proceedings of the National Academy of Sciences, 93(23), 12709-12716.



Value capture in open innovation systems: a longitudinal analysis of collaborative processes with radical circles

claudio dell'era2, alberto di minin1, giulio ferrigno1, federico frattini2, paolo landoni3, roberto verganti2

1Scuola Superiore Sant'Anna, Italy; 2Politecnico di Milano; 3Politecnico di Torino

Context

Open innovation research, value creation and value capture in open innovation processes, the involvement of radical circles in open innovation processes.

Literature

This paper reviews what has been written on value creation and value capture in open innovation research. Moreover, the paper introduces radical circles as actors increasingly involved in open innovation systems. Prior research has traditionally focused on value creation and has offered astounding insights into how firms can create value along the innovation process by interacting with outside actors in an open way (West and Bogers, 2014). Notwithstanding the valuable contributions of these studies, our understanding of open innovation processes remains limited. In particular, prior research has neglected the importance of value capture process in open innovation (West and Bogers, 2014). Notably, some scholars advocate that a comprehensive understanding of open innovation requires a balanced consideration of both value creation and value capture (Chesbrough and Appleyard, 2007).

Literature Gap

prior research has neglected the importance of value capture process in open innovation (West and Bogers, 2014). Notably, some scholars advocate that a comprehensive understanding of open innovation requires a balanced consideration of both value creation and value capture (Chesbrough and Appleyard, 2007).

Research Questions

What are the mechanisms through which firms capture value from the collaboration with radical circles?

Methodology

We used an inductive, exploratory approach in our empirical analysis. In particular, we adopted the approach for theory building suggested by Eisenhardt’s (1989) as well as the guidelines proposed by Yin (2003) and Klein and Myers (1999). These studies helped us to explore and build theory from three longitudinal case studies (i.e., Slow Food, Memphis and Free Software Foundation). The data collection methods relied on multiple sources: 1) primary data (17 hours of semi-structured interviews with key informants of our cases studies, conducted from March 2014 to March 2016); 2) secondary data (web interviews, speeches, books, and various web sources).

Empirical Material

Slow Food: we studied the collaboration with Coop for the Presidia project, and the collaboration with Barilla to draw up the Milan Protocol, which led to the development of the project Safety 4 Food.

Memphis: we examined the collaboration between Olivetti Synthesis and two members of the circle (Sottsass and De Lucchi), and the collaboration between the Swiss firm SMH Swatch and a former member of Memphis, Matteo Thun.

Free Software Foundation: we analyzed the collaboration with Red Hat and the Mozilla project.

Results

By drawing on a comparison of the similarities and the differences among the cases and our current understanding of open innovation value creation and value capture processes (Eisenhardt and Graebner, 2007), we found that the collaboration with radical circles was extremely beneficial for the firms involved in the open innovation processes. Indeed, we discovered that after collaborating with the radical circles these firms developed specific assets to capture value: 1) reputational assets (Red Hat); 2) organizational assets (Coop –Presidia Project);3) intellectual and human assets (Barilla Safety 4 Food, Olivetti Synthesis –Icarus Project, and Swatch); and 4) technological assets (Netscape-Mozilla project).

Contribution to Scholarship

First, it focuses on a relevant, although under-researched, actor which is increasingly involved in collaborative innovation processes, i.e. radical circles (Verganti, 2009; Verganti and Shani, 2016). Our paper complements existing research by unveiling the collaborative value capture processes in open innovation systems where firms interact with hybrid organizations such as radical circles.

Second, the paper enriches the embryonic research on value capture processes by suggesting specific mechanisms (i.e., reputational, organizational, intellectual and human, and technological assets) that lead firms to capture value when they collaborate with radical circles.

Third, the paper examines open innovation processes from a longitudinal perspective (Appleyard and Chesbrough, 2017), which represents a relevant departure from the most common static and cross-sectional standpoint adopted in existing open innovation research.

Contribution to Practice

First, results suggest that firms need to be aware of how they capture value from open collaborative processes. Capturing value is indeed the ultimate goal for those who innovate, and quite likely the greatest challenge they face, especially in an open innovation era.

Second, results show that firms’ managers willing to capture value through open collaborative processes with radical circles ought to understand that the development of certain internal assets (i.e. reputational assets, organizational assets, intellectual and human assets, and technological assets) is of paramount importance.

Fitness

The paper analyzes the challenges that are relevant for a particular typology of innovation (Open Innovation, Theme 8, Track 8.5).

Bibliography

Appleyard, M. M. and H. W. Chesbrough. 2017. “The dynamics of open strategy: from adoption to reversion”. Long Range Planning 50(3): 310−321.

Chesbrough, H. and M. M. Appleyard. 2007. “Open innovation and strategy”. California Management Review 50(1): 57−76.

Eisenhardt, K. M. 1989. “Building theories from case study research”. Academy of Management Review 14(4): 532−550.

Eisenhardt, K. M. and M. E. Graebner. 2007. “Theory building from cases: Opportunities and challenges”. Academy of Management Journal 50(1): 25−32.

Jick, T. D. 1979. “Mixing qualitative and quantitative methods: Triangulation in action”. Administrative Science Quarterly 24(4): 602−611.

Klein, H. K. and M. D. Myers. 1999. “A set of principles for conducting and evaluating interpretive field studies in information systems”. MIS Quarterly, 67−93.

Verganti, R. 2009. Design driven innovation: changing the rules of competition by radically innovating what things mean. Boston: Harvard Business Press.

Verganti, R. and A. B. R. Shani. 2016. “Vision transformation through radical circles”. Organizational Dynamics 2(45): 104−113.

West, J. and M. Bogers. 2014. “Leveraging external sources of innovation: a review of research on open innovation”. Journal of Product Innovation Management 31(4): 814−831.

Yin, R. K. 2003. Case study research: design and methods. Thousand Oaks, CA: Sage Publications.



Exaptation and collaboration. The missing link in European Smart Specialization Strategy

Andrea Ganzaroli1, Ivan De Noni2, Luciano Pilotti1

1University of Milan, Italy; 2University of Padova, Italy

Context

Smart specialization strategy is cornerstone in European innovation policy within the framework of the regional cohesion program since 2011. It aims at stimulating innovation building on existing regional specialization. However, little attention has been devoted to exaptation as contingent form of radical creativity leveraging on available know-how.

Literature

Many discoveries in the history of science and innovation were not the result of the adaptive recombination of existing knowledge, but of the exploitation of an already existing body of knowledge into new and emerging markets (exaptation). Exaptation has been proven useful to deepen our understanding of technological speciation and punctuation (Cattani, 2005; 2008; Dew, 2007). It has been recognized as major innovation driver in the rising of the fiber optics (Cattani, 2005; 2008) and new market niches in the pharmaceutical industry (Andriani et al., 2017). Furthermore, the exaptive copacity has been addressed as key source of competitive advantage (Cattani, 2008). Knowledge complexity, inventors’ analogical capacity, modularity, and patent scope have been already highlighted as possible (micro-)determinants of exaptation (Mastrogiorgio and Gilsing, 2016; Andriani and Carignani, 2014).

Literature Gap

However, little attention has been devoted to organizational and contextual factors enhancing the capacity of firms to exapt their know-how into radically new market niches. This paper focus on region as specific loci of collective innovation and look at specialization and collaboration as main contextual and organizational drivers.

Research Questions

How does reginal specialization affect exaptive invention in regions?

How does collaboration (intraregional, interregional and international) affects exaptive inventions in regions?

How does regional technological diversity affect exaptive invention in regions?

Methodology

To assess the effect of collaboration and specialization on the production of exaptive invention we implement an unbalanced panel data model with individual fixed effect. The unit of analysis are EPO patent with at least one inventor based in Europe. Exaptive patents are identified as those integrating both a high level of radicalness and a low level of originality. Collaboration is defined as the percentage of co-patenting at intraregional, interregional and international level. Technological variety is defined as entropy across IPC classes within a region. Specialization defined as employment concentration at region-industry level.

Empirical Material

A panel database, regarding 265 NUTS2-regions in 29 countries within a 16 years time-window window (2008-2013), is organized by originally merging three different data sources.

1. OECD RegPat database (release version of March 2018)

2. OECD Patent Quality Indicators database (release version of February 2019) includes a number of patent indicators such as originality and radicalness degree.

3. Eurostat database provides data on industrial structure such as the number of employees per industry as well as a number of other control information at regional level such as population density and employment rate. Regions are defined at NUTS-2 level while industries are based on NACE rev.2 classification at two-digit level. In order to support the merge of these databases, patents’ technological classes based on International Patent Classification (IPC) ver.8 at four-digit level are converted to Eurostat industrial classification by using the IPC-NACE v.2 concordance table provided by Eurostat (Van Looy et al., 2014).

Results

Our analysis shows that the exaptive capacity of regions does not depend too much on the technological variety embedded on the regional system, but on specialization and the capacity of those regions to sustain collaboration at interregional and international level. This is a significant result for two main reasons. First, it emphasizes, in concordance with the smart specialization strategy, the significance of specialization as main driver of regional innovation. However, it points out exaptation as specific output of this innovation strategy, which call for specific policy tools. Second, it highlights collaboration, especially at interregional and international level, as main driver of expative innovation. Therefore, it shows how collaboration is important in order to envisage alternative and technologically unrelated usage of available know-how. Furthermore, it also shows how collaboration is a fundamental strategy to be able to exploit that know-know into unrelated technological fields. Finally, the emphasis on interregional and international collaboration, it confirms that the access to unrelated technological needs requires to get access to cognitive distant regional-technological context.

Contribution to Scholarship

This paper contributes to scientific development in three ways. First, it contributes to the literature on exaptation by highlighting the effect of regional organizational and contextual factors and by introducing an innovative metric to assess exaptive inventions. Second, it contributes to the smart specialization literature by highlighting exaptive invention as specific output of this strategy, which deserve further investigation. It contributes at open innovation literature by highlighting the specific link between collaboration and exaptive invention. Large part of this literature still focuses on collaboration as way to stimulate the adaptive combination of knowledge across firms. However, the open innovation may breed the generation of exaptive invention is a significant issue deserving further investigation.

Contribution to Practice

This paper drives also significant implications in terms of policy making. First, it highlights how exaptation, as interpretative construct, may contribute to enhances the smart specialization strategy of regions. More specifically, it suggests the complementarity between specialization and collaboration as key pillar of exaptation. Therefore, regions should invest on the development of specific collaboration mechanisms to sustain the exaptive capacity of their local firms.

Fitness

Exaptation is increasingly recognized as alternative source of creativity and innovation. Furthermore, exaptation, as in the spirt of the open innovation strategy, aims at strengthening collective efficiency in exploiting the distributed base of available knowledge across both firms and regions. Collaboration is recognized as key driver of the exaptation strategy.

Bibliography

Andriani, P., Ali, A., & Mastrogiorgio, M. (2017). Measuring exaptation and its impact on innovation, search, and problem solving. Organization Science, 28(2), 320-338.

Andriani, P., & Carignani, G. (2014). Modular exaptation: A missing link in the synthesis of artificial form. Research Policy, 43(9), 1608-1620.

Andriani, P., & Cattani, G. (2016). Exaptation as source of creativity, innovation, and diversity: Introduction to the special section. Industrial and Corporate Change, 25(1), 115-131.

Balland, P. A., Boschma, R., Crespo, J., & Rigby, D. L. (2018). Smart specialization policy in the European Union: relatedness, knowledge complexity and regional diversification. Regional Studies, 1-17.

Brunswicker, S., & Vanhaverbeke, W. (2015). Open innovation in small and medium‐sized enterprises (SMEs): External knowledge sourcing strategies and internal organizational facilitators. Journal of Small Business Management, 53(4), 1241-1263.

Boschma, R., Balland, P. A., & Kogler, D. F. (2014). Relatedness and technological change in cities: the rise and fall of technological knowledge in US metropolitan areas from 1981 to 2010. Industrial and corporate change, 24(1), 223-250.

Cattani, G. (2005). Preadaptation, firm heterogeneity, and technological performance: A study on the evolution of fiber optics, 1970–1995. Organization Science, 16(6), 563-580.

Cattani, G. (2006). Technological pre-adaptation, speciation, and emergence of new technologies: how Corning invented and developed fiber optics. Industrial and Corporate Change, 15(2), 285-318.

Capello, R., & Kroll, H. (2016). From theory to practice in smart specialization strategy: emerging limits and possible future trajectories. European Planning Studies, 24(8), 1393-1406.

Cassiman, B., & Valentini, G. (2016). Open innovation: Are inbound and outbound knowledge flows really complementary?. Strategic Management Journal, 37(6), 1034-1046.

Castaldi, C., Frenken, K., & Los, B. (2015). Related variety, unrelated variety and technological breakthroughs: an analysis of US state-level patenting. Regional studies, 49(5), 767-781.

Dew, N., & Sarasvathy, S. D. (2016). Exaptation and niche construction: behavioral insights for an evolutionary theory. Industrial and Corporate Change, 25(1), 167-179.

Dew, N., Sarasvathy, S. D., & Venkataraman, S. (2004). The economic implications of exaptation. Journal of Evolutionary Economics, 14(1), 69-84.

De Noni, I., Ganzaroli, A., & Orsi, L. (2017). The impact of intra-and inter-regional knowledge collaboration and technological variety on the knowledge productivity of European regions. Technological Forecasting and Social Change, 117, 108-118.

De Noni, I., Orsi, L., & Belussi, F. (2018). The role of collaborative networks in supporting the innovation performances of lagging-behind European regions. Research Policy, 47(1), 1-13.

Foray, D. (2018). Smart specialization strategies as a case of mission-oriented policy—a case study on the emergence of new policy practices. Industrial and Corporate Change, 27(5), 817-832.

Foray, D., David, P. A., & Hall, B. (2009). Smart specialisation–the concept. Knowledge economists policy brief, 9(85), 100.

Garud, R., Gehman, J., & Giuliani, A. P. (2016). Technological exaptation: A narrative approach. Industrial and Corporate Change, 25(1), 149-166.

Iacobucci, D., & Guzzini, E. (2016). Relatedness and connectivity in technological domains: The missing links in S3 Design and Implementation. European Planning Studies

Jaffe, A. B., & De Rassenfosse, G. (2017). Patent citation data in social science research: Overview and best practices. Journal of the Association for Information Science and Technology, 68(6), 1360-1374.

Jung, H. J., Lee J. J. (2016). The quest for originality: A new typology of knowledge search and breakthrough inventions. Academy of Management Journal, 59(5), 1725-1753.

Kaplan, S., & Vakili, K. (2015). The double‐edged sword of recombination in breakthrough innovation. Strategic Management Journal, 36(10), 1435-1457.

Lopez-Vega, H., Tell, F., & Vanhaverbeke, W. (2016). Where and how to search? Search paths in open innovation. Research Policy, 45(1), 125-136.

Mastrogiorgio, M., & Gilsing, V. (2016). Innovation through exaptation and its determinants: The role of technological complexity, analogy making & patent scope. Research Policy, 45(7), 1419-1435.

McCann, P., & Ortega-Argilés, R. (2015). Smart specialization, regional growth and applications to European Union cohesion policy. Regional Studies, 49(8), 1291-1302.

Miguelez, E., & Moreno, R. (2017). Relatedness, external linkages and regional innovation in Europe. Regional Studies, 1-14.

Singh, J., & Fleming, L. (2010). Lone inventors as sources of breakthroughs: Myth or reality? Management science, 56(1), 41-56.

Sun, Y., (2016). The structure and dynamics of intra- and inter-regional research collabora-tive networks: the case of China (1985–2008). Technol. Forecast. Soc. Chang. 108:70–82.

Tödtling, F., & Trippl, M. (2005). One size fits all? Towards a differentiated regional innovation policy approach. Research policy, 34(8), 1203-1219.

Van Looy, B., Vereyen, C., & Schmoch, U. (2014). Patent statistics: Concordance IPC V8 – NACE REV.2, Eurostat, Euopean Commission.

Wixe, S., & Andersson, M. (2017). Which types of relatedness matter in regional growth? Industry, occupation and education. Regional studies, 51(4), 523-536.



 
Contact and Legal Notice · Contact Address:
Privacy Statement · Conference: R&D Management Conference 2019
Conference Software - ConfTool Pro 2.6.128+TC
© 2001 - 2019 by Dr. H. Weinreich, Hamburg, Germany