19-PM1-04: ST6.3 - The Management of Uncertainties in Innovation Activities
Managing Radical innovation (RI) in large mature companies requires addressing high levels of uncertainties, not only risks. The literature recognizes that firms should build special capabilities to manage RI initiatives (O’Connor et al. 2008, 2018; Bagno et al 2017; Salerno et al 2015). Although grounded theoretical and empirical frameworks exist in the literature for understanding the dynamics of RI’s (e.g. O’Connor&Rice 2013), firms still struggle to deal with the complex uncertainties landscape that RI’s present (Brasil et al. 2018, Eggers&Kaul 2018). Recent studies call for a more systematic approach for confronting uncertainties. For example, new project management processes have been proposed (Rice et. al. 2008). At a systems level, Gomes et al. (2018) proposed a framework for managing uncertainties in the ecosystem; O’Connor et al (2008) propose the emergence of an Innovation Function (IF) in mature companies; O’Connor et al (2018) discuss IF managerial roles; Salerno&Gomes (2018) propose a networked IF in companies; Lubik et al (2013) analyse value creation in university spin offs. These studies open opportunities for further research, drawing upon new theoretical and methodological perspectives.
In this track, we are interested in moving the frontier of knowledge forward by addressing, from different theoretical perspectives, how innovative firms – whether mature or knowledge intensive startups–manage uncertainties associated with radical innovations. We are especially interested in the following topics:
1. What are the types of uncertainties that RI project teams and start-ups face?
2. Which contingencies moderate the ways firms should organize for RI to best cope with uncertainties?
3. How should companies manage uncertainties within innovation ecosystems?
4. How does uncertainty affect the way firms negotiate contracts with external partners for RI projects?
5. How do firms manage the uncertainties present in RI projects at the portfolio level?
6. What firm level capabilities are required to overcome uncertainties present in RI’s?
7. What skills and capabilities are required of managers and entrepreneurs to deal with uncertainties?
8. What heuristics do entrepreneurs or managers use for perceiving and managing uncertainties?
Uncertainties as element of management of startup in light of the theory of Dynamic Capabilities
Federal University of Santa Catarina, Brazil
Entrepreneurs need to continually configure their available resources to propose innovative products and services (TEECE, 2010), resulting in a business model that can identify a client's wishes, while at the same time focusing on value proposition (Al-Debei & Avison, 2010; Chesbrough, 2010; Rajala & Westerlund, 2007).
Dynamic capabilities (DCs) is understood as a meta-competence, which transcends operational competence and is intended to analyze the sources and methods of organizations in creating value and wealth operating in dynamic and rapidly changing environments (Teece, 2007).
The main logic behind the arguments of the association between startups and DCs is that what the entrepreneur does is the synthesis of what dynamic capabilities are (Arend, 2014), being able to decompose into three perspectives , being able to glimpse an opportunity (sensing) (Neill, McKee, & Rose, 2007; Pandza & Thorpe, 2009), decision-making capacity in dynamic environments and high uncertainty (seizing) (Frank & Roessl, 2015; Yi, He, Ndofor, & Wei, 2015) and ability to continually reconfigure their resources (Li & Liu, 2014).
Although much research is done on the subject, which is one of the most researched and controversial in the area of strategy, very little is known about these DCs in startups companies. These firms are predominantly responsible for creating new markets and realizing innovative value propositions (Corner & Wu, 2012; Newey & Zahra, 2009).
What are the managerial techniques and behavior patterns that entrepreneurs use to overcome the obstacles arising from the uncertainty and lack of knowledge of their context?
This research is based on longitudinal cases study with five startups at one of the main innovation habitat of Brazil.
Data collection was performed by semi-structured interviews (Qu & Dumay, 2011), participant observation (Hancock & Algozzine, 2016) and documentary analysis (Yin, 2017), thus seeking to triangulate evidence (Barratt, Choi, & Li, 2011; Voss, 2010).
Data analysis was performed by inductive method (Barratt et al., 2011), seeking to find clusters of categories and similarities (Benbasat, Goldstein, & Mead, 1987).
This research presents results of a long research and development project carried out in one of the most important Brazilian universities, in order to support young technology-based entrepreneurs.
In addition to these activities, a longitudinal monitoring was carried out with more than 20 Startups. This article presents the initial results of the scientific analysis of the trajectory of these companies.
The first company is based on Information Technology Consulting that started for SMB and now it prospects large companies and find themselves in challenge to scale their business.
The second startup works with laser cutting and engraving product that is already in the second generation of products and is also in the challenge of scaling its business model.
The third company is an incubated startup that has a solution to support scientific writing and they achieved a capillarity in its first product version but still needs to scale its business.
The fourth company has a solution for integrated battery management in order to reuse batteries and they are still exploring its market for possible opportunities.
The fifth company studied is a startup that has a building information management for construction and its challenge is lack of growth.
One of the relevant findings is the conscientiousness of the entrepreneur in the face of uncertainty. In spite of initially the entrepreneurs recognize that the context has characteristics of unavailability of knowledge, decision makers' efforts are focused on the development of the technical solution and the uncertainties are not decision criteria in the company's daily routine. The troubleshooting is still widely observed during the design of the business model.
It was observed in the case studies that the lack of knowledge about the business model are still seen as only a weakness, not a natural condition of startup to explore.
It was also noted that the rationalist view of decision, typical of structured problems, becomes prevalent in systems management, but it should have innovation processes appropriate to dynamic contexts (Bagno, Salerno, & da Silva, 2017).
The main uncertainties cataloged from the case studies revealed the uncertainty as (i) how to explore a target market and (ii) how to set pivot points (go or kill conditions).
Contribution to Scholarship
The potential of the contribution of the dynamic capabilities concept to the study of the creation of new companies and their contribution to the area of knowledge of entrepreneurship are recognized by the scientific community (Corner & Wu, 2012).
The results demonstrate evidence to the community about the still descriptive state of the field of knowledge of dynamic capabilities in startup environments, rather than prescriptive state. This assertion stems from the theoretical advancement of this field of knowledge, but there are still few studies on how this theory can disruptively contribute to the modus operandi of the treatment of uncertainties of a startup.
This research presents typical uncertainties and startups, and the potential management tools to be able to identify opportunities from uncertainties and to reorganize continuously with the knowledge acquired during the elaboration of a business model.
Contribution to Practice
This research presents a discussion, analysis and propositions so that entrepreneurs who manage startups can benefit in a practical way from the DCs theory.
Firstly, a proposition is presented to recognize uncertainty as a management element that can support decisions and not only as a fragility to be concealed or accepted by the entrepreneurs.
The research also presents categories of uncertainties typical of startup environments and what the characteristics that a management system can acquire for the proper management of these uncertainties, supporting plans or pivot points, similar to O'Connor and Rice (2013) did for mature businesses.
The research also presents categories of uncertainties typical of startup environments and what the characteristics that a management system can acquire for the proper management of these uncertainties, supporting plans or pivot points.
Al-Debei, M. M., & Avison, D. (2010). Developing a unified framework of the business model concept. European Journal of Information Systems, 19(3), 359-376.
Arend, R. J. (2014). Entrepreneurship and dynamic capabilities: how firm age and size affect the ‘capability enhancement–SME performance’relationship. Small Business Economics, 42(1), 33-57.
Bagno, R. B., Salerno, M. S., & da Silva, D. O. (2017). Models with graphical representation for innovation management: a literature review. r&D Management, 47(4), 637-653.
Barratt, M., Choi, T. Y., & Li, M. (2011). Qualitative case studies in operations management: Trends, research outcomes, and future research implications. Journal of Operations Management, 29(4), 329-342.
Benbasat, I., Goldstein, D. K., & Mead, M. (1987). The case research strategy in studies of information systems. MIS quarterly, 369-386.
Chesbrough, H. (2010). Business model innovation: opportunities and barriers. Long Range Planning, 43(2-3), 354-363.
Corner, P. D., & Wu, S. (2012). Dynamic capability emergence in the venture creation process. International Small Business Journal, 30(2), 138-160.
Frank, H., & Roessl, D. (2015). Problematization and conceptualization of “entrepreneurial SME Management” as a field of research: overcoming the size-based approach. Review of Managerial Science, 9(2), 225-240.
Hancock, D. R., & Algozzine, B. (2016). Doing case study research: A practical guide for beginning researchers: Teachers College Press.
Li, D.-y., & Liu, J. (2014). Dynamic capabilities, environmental dynamism, and competitive advantage: Evidence from China. Journal of Business Research, 67(1), 2793-2799.
Neill, S., McKee, D., & Rose, G. M. (2007). Developing the organization's sensemaking capability: Precursor to an adaptive strategic marketing response. Industrial Marketing Management, 36(6), 731-744.
Newey, L. R., & Zahra, S. A. (2009). The evolving firm: how dynamic and operating capabilities interact to enable entrepreneurship. British Journal of Management, 20, S81-S100.
O'Connor, G. C., & Rice, M. P. (2013). A comprehensive model of uncertainty associated with radical innovation. Journal of Product Innovation Management, 30, 2-18.
Pandza, K., & Thorpe, R. (2009). Creative search and strategic sense‐making: missing dimensions in the concept of dynamic capabilities. British Journal of Management, 20, S118-S131.
Qu, S. Q., & Dumay, J. (2011). The qualitative research interview. Qualitative research in accounting & management, 8(3), 238-264.
Rajala, R., & Westerlund, M. (2007). Business models–a new perspective on firms' assets and capabilities: observations from the Finnish software industry. The International Journal of Entrepreneurship and Innovation, 8(2), 115-125.
Teece, D. J. (2007). Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic management journal, 28(13), 1319-1350.
Voss, C. (2010). Case research in operations management Researching operations management (pp. 176-209): Routledge.
Yi, Y., He, X., Ndofor, H., & Wei, Z. (2015). Dynamic capabilities and the speed of strategic change: Evidence from China. IEEE Transactions on Engineering Management, 62(1), 18-28.
Yin, R. K. (2017). Case study research and applications: Design and methods: Sage publications.
Mechanisms for the integration between Research and Development: a literature review
1Production Engineering Department, Polytechnic School, University of São Paulo; 2Business Department, Faculty of Economics, Management and Accounting, University of São Paulo
This study was motivated by observing a real problem faced by an organization where new technologies provided by the Research department were not fully implemented in the new products carried out by the Development department. This inter-departmental integration capability is a critical step in the implementation of radical innovations.
The studies on Research and Development integration provide some models to deal with the challenges that emerge between these areas interface. These models are mostly based on a few qualitative studies, what limits their generalization. In addition, they were developed through different levels of analysis, what limits their comparison. Nevertheless, they bring important contribution to build a consolidated framework. White (1977) studying a medical device company, emphasized the importance of having an overlap phase between technology development and its application in new products. Cohen, Keller and Streeter (1979) listed primary (e.g. understanding of technology growth potential) and secondary factors (e.g. involvement of leadership) in the integration of 18 projects at IBM. Iansiti (1995) studying mainframe industry relates the importance of a dedicated group for the intra-firm technology transfer. Nobelius (2004) highlights three elements in his study in an automobile company: Strategic and operational synchronization, Transfer scope and Transfer scope.
The models reported bring an important aspect of the integration, but none presents a broader view with a consolidated list of the mechanisms. To cover this gap, we conducted a systematic review of the literature and compared the models to present a framework with these mechanisms.
Which are the mechanisms of the integration between Research and Development that could reduce uncertainties in the implementation of new technologies and radical innovations?
How could these mechanisms be organized to provide a framework for integration between Research and Development that are detailed and concise at the same time?
A systematic literature review was conducted in May/2018 updated in January/2019, using Scopus® database. The terms used in the search were refined until we achieve a number of articles feasible to be managed by our research group. The next step was to read title-keywords-abstract and eliminate those without fit with our aim. Then, we proceed to full paper reading and other papers from backward searching (snowball technique) were identified. Other papers already known by the group were added in the set of references. Through content analysis was possible to list and categorize the mechanisms.
At the end of the search part of the literature review process, 236 papers were selected and after title-keywords-abstract reading, 150 papers were considered to full paper reading, selecting 86 papers with fit to our aim. Also, through backward searching (snowball technique) these papers, were possible to add 23 references. As a final step, 9 papers known b our research group not identified in the previous steps were added, resulting in 118 papers that had their content analyzed and categorized.
Even being the focus on internal integration (intra-firm), in this process of literature review, the integration mechanisms for external relationship (e.g. University-Company, Supplier-Client) were also considered with the intention to find mechanisms not studied/reported in one type of interface (internal or external) be helpful to the other.
These study consolidated three groups of mechanisms for integrating Research and Development, which are: technology, organizational design, and individual.
In total, 27 mechanisms were identified. Twenty-three that were relevant for both internal and external interfaces were classified in these three groups, as detailed bellow:
• The technology group: equivocality; market and consumer orientation; scope, specifications and prototypes; timing synchronization; and technology fit.
• The organizational design group: cross-functionality; definition of roles, responsibilities and targets/objectives; geographical distance; processes and governance; technology transfer group; technology and strategic planning; allocation and job rotation; project management; culture; funding; rewards and incentives; knowledge management; integrative role.
• The individual group: communication; networking; senior leadership commitment; motivation; prevention of “not invented here syndrome”.
A visual model will be provided in the full paper to illustrate these groups and mechanisms.
The other four mechanisms were considered just relevant for the external interface. They are: provision of technical services or consultancy; partnership formats (e.g. joint-ventures); contracts establishment; intellectual property agreements and policies.
Contribution to Scholarship
We achieved a consolidated view of the integration mechanism between Research and Development that was synthetized in an illustrative model. This view combined previous models’ perspectives that were compared to result in the group of mechanisms proposed, an unprecedented consolidated view about this problem of integration. Future researches could be produced utilizing this model, for instance, identify which group is more relevant depending on the characteristics/contingencies of each new technology to be applied in new products and radical innovations.
Contribution to Practice
Proposing a model that presents a set of mechanisms divide into groups, managers in the industry could use this model to evaluate their in-house practices and change their procedures and processes, or even their organizational design, to improve the application new technologies into new products. Also, considering the application to external interfaces, changes in the relationship of universities-companies, supplier-client, etc, could also be improved.
This research contributes to bridging the gap between the players in the field of new technologies and radical innovation implementation. The focus was to cover the intra-firm gap between Research and Development with a broader model of integration mechanisms, however, the model proposed also could be applied to external interfaces.
Cohen, H., Keller, S., & Streeter, D. (1979). Transfer of Technology from Research to Development. Research Management, 22(3), 11-17.
Iansiti, M. (1995). Technology development and integration: an empirical study of the interaction between applied science and product development. IEEE Transactions on Engineering Management, 42(3), 259-269.
Nobelius, D. (2004). Linking product development to applied research: transfer experiences from an automotive company. Technovation, 24(4), 321-334.
White, W. (1977). Effective transfer of technology from research to development. Research Management, 20(3), 30-34.
Where do radical innovations come from? The case of nanotech
1Department of Economics, University of Messina, Italy; 2Research Center of the École de Management Léonard de Vinci in Paris La Défense, France
Over the past 30 years, patents have become one of the most common means of measuring the degree to which an innovation is radical or incremental. Patents have become an important metric in the innovation literature due to an easy and open trail of patent citations.
Several empirical studies have supported the positive relationship between the increase of technology diversity in a firm’s patent portfolio and the growth of R&D expenditures, and firm value (Gambardella and Torrisi, 1998). Other studies prove the large positive effect of the recombination across domains of technological knowledge on the value of innovation (Gosh et al. 2009; Moaniba et al. 2018).
Different authors have proposed method for determining the radicalness of an innovation, using the number of times a patent is cited by other patents through the forward citations (Katila, 2000) and the degree to which the patent classes of cited patents differ from those of the focal patent through the backward citations (Rosenkopf and Nerkar, 2001).
The Dahlin and Behrens model (Dahlin and Behrens, 2005) identifies radical inventions as inventions that heavily influence and affect the future content of patent families, in terms of patent structure and future citations.
Studies prove that forward citations are related with the value and impact of an invention, but it is not clear how to choose the number of years to be considered after the publication of the patent and how qualifying an invention as radical.
The purpose of this research is to determine a relationship between the diversity of knowledge used in building up a patent (and therefore the ability to combine scientific and technological sources together) and the likelihood of generating radical innovations.
We have used econometric tools for the determination of an index of diversification of every analyzed patent. In particular, the study will evaluate the diversity of used technological and scientific knowledge with the Shannon-Wiener diversity index.
For each patent, the Shannon-Wiener diversity index (Magurran and McGill 2011) has been computed according to the method proposed by Appio et al. (2016). Such an index shows the capability of combining technological and scientific knowledge and the diversity of knowledge bases on which the patent has been built. This index value will be compared with the value of radicalness of the patent.
We have considered the technological landscape of silica aerogel technology, a nanomaterial with excellent thermal insulating properties, high temperature stability, very low dielectric constant and high surface area properties. Silica aerogel tech has seen an incredible increase in number of published patents in recent years.
All patents in this field of nanotechnology from 2006 until now (from QPat database) have been considered.
Particularly relevants (according to Appio et al. 2016) are the cases in which the Shannon-Wiener diversity index score is higher than 3.5. Results show that the owners of patents with an index higher than 3.5 are the top player firms in the field (L’Oreal, Aspen Aerogels) and some research institutes of technology and Universities (MIT, Lawrence Livermore National Security, Institute Superior Tecnico). This demonstrates that also big research centers possess technological and scientific knowledge in nanotech fields and the ability of recombining them for obtaining radically innovative products.
Contribution to Scholarship
Clearly, being able to identify ex-ante a radical innovation is important for evaluating the possible economic value generated ex-post by the new product. This contributes in learning how firms could manage uncertainties and risks present in radical innovation projects at the patent portfolio’s level and what firm level skills and capabilities could be required to overcome these uncertainties.
Contribution to Practice
This research contributes for the subsequent empirical works that measures radicality of innovation performance through patents. The advantage of using backwards citations to compute the Shannon-Wiener diversity index is that it is richer in information content than forward citations. The main drawback is the patent classes are determined by the patent office and are reclassified or added or altered with varying frequency, making cross-technology comparisons difficult.
The aim is to provide guidance, mainly to firms, on how to manage knowledge bases and how to organize the patent portfolio in order to develop radical innovations.
R. Adams, J. Bessant, R. Phelps, 2006. Innovation management measurement: a review. Int. J. Manag. Rev., 8 (1), pp. 21-47.
S. Arts, F. P. Appio, B. Van Looy, 2013. Inventions shaping technological trajectories: do existing patent indicators provide a comprehensive picture? Scientometrics. DOI: 10.1007/s11192-013-1045-1
S. Arts, L. Fleming, 2018. Paradise of Novelty - Or Loss of Human Capital? Exploring New Fields and Inventive Output. Organization Science. Articles in Advance, pp. 1-19.
S. Arts, R. Veugelers, 2014. Technology familiarity, recombinant novelty, and breakthrough invention. Industrial and Corporate Change, pp. 1-32. DOI: 10.1093/icc/dtu029
F. P. Appio, D. Baglieri, F. Cesaroni, A. Martini, 2016. Knowledge recombination and the development of radical innovations: preliminary evidence from nanotech. R&D Management Conference 2016 “From Science to Society: Innovation and Value Creation” 3-6 July 2016, Cambridge, UK.
J. Callaert, B. Van Looy, A. Verbeek, K. Debackere, B. Thijs, 2006. Traces of prior art: An analysis of nonpatent references found in patent documents. Scientometrics 69(1): 3-20.
K. Dahlin, D. Behrens, 2005. When is an invention really radical? Defining and operationalizing technological radicalness. Research Policy 34(5):717-737.
M. Dziallas, K. Blind, 2018 (in press). Innovation indicators throughout the innovation process: An extensive literature analysis, Technoinnovation.
Ghosh, X. Martin, J. M Pennings, F. C. Wezel, 2000. Recombination Experience: A Study of Organizational Learning and Its Innovation Impact. SMS 29th Annual Conference Submission.
M. Kalthaus, 2016. Knowledge recombination along the technology life cycle. Jena Economic Research Papers.
R. Katila, 2000. Measuring innovation performance. International Journal of Business Performance Measurement, 2: 180-193.
A.E. Magurran, B.J. McGill, 2011. Biological diversity: Frontiers in measurement and assessment. New York: Oxford University Press.
M. Moaniba, H. Su, P. Lee, 2018. Knowledge recombination and technological innovation: the important role of cross-disciplinary knowledge. Innovation, Organization & Management, 20 (4), pp. 326-352.
L. Rosenkopf, A. Nerkar, 2001. Beyond local search: Boundary-spanning, exploration, and impact in the optical disk industry. Strategic Management Journal, 22: 287-306.
S. Shane, 2001. Technological Opportunities and New Firm Creation. Management Science 47(2), pp. 205-220.
S. Torrisi, A. Gambardella, P. Giuri, D. Harhoff, K. Hoisl, M. Mariani, 2016. Used, blocking and sleeping patents: empirical evidence from a large-scale inventor survey. Research Policy, 45 (7), pp. 1374-1385
D. Verhoeven, J. Bakker, R. Veugelers, 2016. Measuring technological novelty with patent-based indicators. Research Policy, 45, pp. 707-723.
R. Veugelers, J. Wang, (in press). Scientific novelty and technological impact. Research Policy.
Coping with uncertainty for innovative concept maturation: exploring Concept Maturity Levels in MedTech innovation
1Université Paris Est Créteil, IRG (EA2354), France; 2Altran Research Department, France; 3Forum des Living Labs en Santé et Autonomie, France
The research emerged in the context of a partnership between the French Forum of Living Labs in Health & Autonomy (LLSA) and the INSERM CIC-IT Network. Forum LLSA is a non-profit organization federating more than 30 Living Labs. CIC-IT Network includes Research Centers specialized in clinical research for MedTech projects.
Past research on radical innovation (O’Connor 2008; McDermott & O’Connor 2002) and open innovation (Chesbrough 2003; West et al. 2014) have paid attention to the complex organizational forms of innovation, their strategies, processes and business models. An important aspect of recent literature concerns the more upstream phases of exploration, including the maturation of innovative concepts (Markovitch et al. 2017). During these maturation activities, it no longer consists of evaluating and selecting ideas, but also of structuring complete management systems intended to “mature” concepts and the organizations which sustain them to transform the starting intention into a value proposition implemented into a new ecosystem of uses (Hooge, Béjean et al. 2016).
Despite their strategic importance, the maturation activities needed for innovative concept early development are still insufficiently understood.This paper aims to fill this gap by studying a novel integrative approach developed by NASA which uses “Concept Maturity Levels” (CMLs) (Ziemer, Ervin, and Lang 2013) for early innovation activities.
What types of challenge do emerge in the Medtech sector when coping with uncertainty during the concept maturation activities? How could CMLs help better formalize and organize new upstream processes for coping with these uncertainties? What could be the benefits of such processes for the whole ecosystem?
This paper is based on an on-going multiple case studies research (Eisenhardt 2007; Eisenhardt 1989) led in France where CMLs are being tested as a methodology for structuring Medtech Innovation complexity. CMLs are a new metric inspired by Technology Readiness Levels (TRLs) developed by NASA in the 80s. CMLs extend TRLs by both adding divergent phases to TRLs convergent orientation and integrating needs (value proposition, end-users…) as well as organizational aspects (costs, organization…) to the technological ones.
The paper presents the exploratory phase of the research, consisting of 17 interviews carried out between June and July 2018 in 5 different Living labs and CIC-IT.
Based on exploratory results, this paper provides an emerging framework showing what could be CMLs for Medtech Innovation. Named “CML-FS” (“CML-Forum Santé”), this framework defines 6 progressive levels, which describe the increasing maturity of a healthcare innovative concept with associated types of uncertainties: A design process model is also presented. It takes the form of a “diamond” illustrating the diverging and converging phases of this approach.
Contribution to Scholarship
This paper contributes to the literature on radical innovation management. It provides an integrative framework to cope with uncertainty in the early phases of concept maturation and development including internal and external stakeholders of the concerned firms or organizations.
Contribution to Practice
The paper presents a generic model that defines the maturity of an innovative concept in the sector of medical devices, as well as a generic process to transform an initial concept idea into a functional proven concept. It also provides insights of why and how they could be implemented as a solid basis to stimulate more formative and adaptive design and evaluation methods in the MedTech sector.
This paper is directly linked to this year’s conference theme as it explicitly brings into play Research (living labs, CIC-IT, laboratories…), industry (start-ups, firms) and society (patients, healthcare professionals…).
Chesbrough, H.W., 2003. Open innovation: the new imperative for creating and profiting from technology, Harvard Business School Press.
Eisenhardt, K.M., 1989. Building theories from case study research. Academy of Management Review, 14(4), pp.532–550.
Eisenhardt, K.M., 2007. Theory building from cases: opportunities and challenges. Academy of Management Journal, 50(1), pp.25–32.
Hooge, S., Béjean, M. & Arnoux, F., 2016. Organising for radical innovation : the benefits of the interplay between cognitive and organisational processes in KCP workshops. International Journal of Innovation Management, 20(04), p.1640004.
Markovitch, D.G., O’Connor, G.C. & Harper, P.J., 2017. Beyond invention: the additive impact of incubation capabilities to firm value. R&D Management, 47(3), pp.352–367.
McDermott, C.M. & O’Connor, G.C., 2002. Managing radical innovation: an overview of emergent strategy issues. Journal of Product Innovation Management, 19(6), pp.424–438.
O’Connor, G.C., 2008. Major Innovation as a Dynamic Capability: A Systems Approach. Journal of Product Innovation Management, 25(4), pp.313–330.
West, J. et al., 2014. Open innovation: The next decade. Research Policy, 43(5), pp.805–811.