20-AM-05: ST4.1 - the Design of New Industrial Ecosystems
Recent works on business model innovation (Schneckenberg and Velamuri 2018) (Spieth et al. 2014; Demil and Lecocq 2010) and ecosystems (Jacobides et al. 2018) have underlined the variety of configurations, roles and strategic positioning in the contemporary industry. They raised the critical issue of designing these ecosystems: is it possible to design a technological core to become a platform leader? who are the actors capable of designing an ecosystem? And also: what are the required managerial competencies? What kind of leaders? What kind of work division and value division? What kind of collaborations?
On the other hand, the study of design regimes and innovation dynamics has shed lights on new actors and new forms of interactions supporting intensive innovation dynamics at ecosystem level (Lange et al. 2013; Le Masson et al. 2012; Ollila and Yström 2016). These works underline that new ecosystem dynamics call for new forms of relationships between economic actors, based on the capacity to collectively explore the unknown. In particular it can lead to new forms of relationships between industrial ecosystem, society and scientific research. These works also relied on recent advances in design methods, design theory and even design cognition (Agogué et al. 2012) to improve the analytical framework and experiment with new methods and organizational forms.
This track will study these actors in charge of new ecosystem dynamics. Papers can be based on the empirical study of the actors; the track also welcomes theoretical papers that could help rediscuss the nature of the relationship in this process of ecosystem design, a relationship that probably goes far beyond the usual economics transaction. The track also welcomes methodological papers that propose new instruments and new techniques to help study ecosystems design.
- research / industry relationship for double impact;
- managing collective innovation for industry 4.0
- platform emergence and platform overthrown
- design regimes
- prescription and ecosystem dynamics
- cognitive approach of ecosystem dynamics
Systematic Literature Review of Disruptive Technologies in Business Ecosystems
Graz Univeristy of Technology, Austria
Disruptive technologies (DTs) increasingly demand external sourcing of new competencies and resources. This is achieved through forming alliances, causing changes in companies’ business ecosystems (BEs). This article systematically investigates literature addressing the influence of DTs on BEs’ structures. Relevant articles are analysed to form a framework for DTs in BEs.
DTs are defined as any enhanced or entirely new technology that replaces an existing one by providing new functionality. Furthermore, DTs are powerful means for enlarging markets, eventually leading to changes in what’s commonly described as a companies’ BE. (Utterback, 2005) A BE is a co-evolving and dynamic community, consisting of diverse actors involved in the creation of a product or service for a customer. (Moore, 1996)
So far, research results in the area of DTs in BEs present a rather diverse image. For example, Asgari et al. (2017) show how firms reconfigure alliance portfolios in response to DT. Ray and Ray (2011) found out that collaborative partnerships support the process of innovation by offering advantages such as decreasing risks and costs regarding the development of novel technologies. Nieuwenhuis et al. (2018) elaborate changes in the role of BE’s stakeholders due to DT.
Although a broad body of literature combines DT with aspects of the BE, to the authors best knowledge no systematic literature review addressing the influence of DTs and BEs has been published yet. This underlines the need to investigate the literature on DTs in BEs (Fettke, 2006).
(1) What literature is relevant when investigating the relationship between DTs and BEs?
(2) What relevant issues can be identified from an analysis of the existing literature identified in (1)?
The literature review at hand combines qualitative and quantitative aspects to assess descriptive and content specific aspects. Doing so, a process model proposed by Cooper (1984) is applied, containing following steps: 1) Problem formulation 2) Data collection 3) Data evaluation and 4) Analysis and interpretation. In step 1), research questions were formulated. In step 2), comprehensive and explicit criteria for inclusion and exclusion were applied. In step 3), abstracts of 359 articles were checked for their thematic relevance, reducing the results to 141 articles. Step 4) included a content analysis of the 141 full papers.
To narrow down the empirical material for the area of investigation, comprehensive and explicit criteria for inclusion and exclusion were applied. The criteria include a limitation to peer-reviewed scientific journal articles, a limitation to articles including the topics “disruptive technology” and “business ecosystems” as well as a limitation to articles in English language. Research material from the databases “Web of Science” (WoS) and “Scopus” was collected, looking solely at titles, abstracts and keywords. A wide range of search terms covering the two main topics was used and combined with an “OR” logic in order to cover a broader range of possible results. Search results from the two main topics (DT and BE) were combined with an “AND” logic. After downloading and combining the preliminary results from WoS and Scopus, and removing duplicate articles, the overall search resulted in 359 papers. The abstracts of the 359 articles were checked for their relevance according defined criteria, reducing the results to 141 articles. A content analysis of the 141 full papers was conducted. For the analysis, a process described by Mayring (2010) together with considerations by Gioia et al. (2012) forms the guideline for category selection and material evaluation.
This paper yields two kinds of results related to the research questions stated above. (1) Over the last 39 years, the amount of relevant publications covering the topics of DTs and BEs has exponentially increased. Interestingly, results indicate that a large part of the scientific discussion takes place in a rather small number of scientific journals. The identified 141 relevant articles are spread across 97 journals. Of all considered articles, 30 % were published in ten journals. (2) Through qualitative content analysis of 141 articles, this paper sheds light on overarching themes in literature regarding DTs in the context of BEs. Findings highlight the DT induced changes in BEs’ structures as well as changes in interactions and capabilities of individual actors in BEs. Each identified aspect is analyzed and discussed in detail. Subsequently, findings are put into relation to form a general framework for DTs in BEs.
Contribution to Scholarship
The work contributes to theory of DTs in the context of BEs in three ways. First, it provides an overview of relevant literature. Second, the current state of literature is analysed with regard to recurring themes. Third, identified themes are aggregated to provide an overview of research streams. Using the BE as a theoretical lens, this paper aims to categorize themes in literature by establishing a general framework of DTs in the context of BEs. The obtained overview of the investigated intersection of DTs and BEs can be used as a guideline for further research.
Contribution to Practice
Literature is rich in case studies with regard to DTs in BEs. However, these studies mainly address branch-specific issues. The generalizability of how DTs change BEs’ structures is often limited. Therefore, a framework is proposed, functioning as a guidepost for applying findings from practical cases. That way, this paper elucidates on the applicability of findings from literature. Therefore, this article supports practitioners in responding to DTs within a BE.
The paper is of great importance for the ecosystems theme in order to provide managers and researchers a guidepost on how DTs affect BEs and how to respond. Our study advances the literature of BEs, showing that DTs have significant impact on BEs’ structures and activities.
Asgari, N., Singh, K., and Mitchell, W. “Alliance portfolio reconfiguration following a technological discontinuity.” Strategic Management Journal 38 (2017): 1063.
Cooper, H. M. The integrative research review: a systematic approach. Applied social research methods series (Vol. 2). Beverly Hills: Sage, 1984.
Fettke, Peter. "State-of-the-Art des State-of-the-Art - Eine Untersuchung der Forschungsmethode `Review´ innerhalb der Wirtschaftsinformatik." Wirtschaftsinformatik 48 (2006): 257-266.
Gioia, Dennis A., Corley, Kevin G. and Hamilton, Aimee L. “Seeking Qualitative Rigor in Inductive Research: Notes on the Gioia Methodology.” Organizational Research Methods 16 (2012): 15–31.
Moore, J. F. The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems. New York: HarperCollins, 1996.
Nieuwenhuis, B., Ehrenhard, M., and Prause, L. “The shift to Cloud Computing: The impact of disruptive technology on the enterprise software business ecosystem.” Technological Forecasting and Social Change 129 (2018): 308-313.
Ray, S. and Ray, P. K. “Product innovation for the people's car in an emerging economy.” Technovation 31 (2011): 219.
Utterback, James M. and Acee, Happy J. “Disruptive Technologies: An Expanded View.” International Journal of Innovation Management 9 (2005): 1-17.
Exploring practices in University-Industry collaborations: the case of collaborative doctoral program in France
1Centre de Gestion Scientifique (CGS), Mines Paristech; 2Institut National de la Propriété Industrielle (INPI)
University – Industry (U-I) collaborative Ph.D. is one channel amongst a wide range of methods for firms to access academic knowledge. Often presented as a mean for firms to hire Ph.D. candidates or address problem-solving issues, U-I collaborative Ph.D. constitute an interesting proxy to explore U-I collaboration forms and strategies.
Collaborative Ph.D. is a quite common form of University – Industry interaction (Borell-Damian, 2015) with dedicated programme in many European countries.
Previous studies on this topic focused either on (1) U-I collaborative Ph.D. students’ research works quality and employment trajectories; (2) descriptive analyses of Ph.D. students’ roles in the knowledge transfer process and (3) success factors of the collaboration.
Literature is scarce in terms of those projects’ contribution to the innovation process. Harryson & al. (2007) showed in a qualitative case study how U-I collaborative Ph.D. students were a leverage to perform exploratory projects in the audio industry by co-developing knowledge with academic partners. Thune & Børing (2015) suggested that those projects were mainly dedicated to develop firm’s absorptive capacity. Nevertheless, Grimm (2018) and Malfroy (2011) showed that those projects were more oriented towards product development and that scientific outcomes were limited.
Scholars acknowledged that U-I collaborative Ph.D. is a topic of little systematic research (eg. Wit-de-Vries & al., 2018). It seems that there is a wide range of U-I interactions’ types through collaborative Ph.D. Their roles in terms of unknown exploration and new knowledge development is an area for further research.
What would be the different archetypes of U-I collaborative Ph.D. in terms of R&D contribution? In what extent firms and universities contribute to new knowledge co-development and unknown exploration through U-I collaborative Ph.D. programmes?
Our methodology is both conceptual and quantitative. Based on extensive literature review on U-I collaborative Ph.D. we built a new framework to classify those research projects in terms of collaboration modalities and research strategies. We applied this framework to classify an original dataset of collaborative Ph.D. projects. The latter was comprising of collaborative Ph.D.’s contract agreements and associated detailed research projects. Each contract was double-coded in order to be classified according to our framework. We then performed statistical descriptive analyses to discuss project archetypes identification and magnitude, in particular with the distinction between large company and SMEs.
Our empirical material was collected through a collaboration with the ‘National Association of Research and Technologies’ (ANRT) in France. ANRT is the body responsible for managing the main U-I Collaborative Ph.D. scheme in France called ‘Industrial Convention of Formation by Research’ (CIFRE).
The CIFRE’s scheme includes: (1) a Ph.D. student; (2) a research laboratory and (3) a company. The parties are contracting out a 3-year long research project. The company is hiring the Ph.D. student through a 3-year employment contract. The company and the research laboratory are linked by a collaboration contract specifying the design of the project (eg. time spend by the Ph.D. student in each party facilities, intellectual property issues, financial transfers, etc.). Each collaboration contract also includes an appendix with the detailed research project undertaken by the parties.
In this exploratory study, we had access to 89 collaboration contracts and associated appendixes, of which 78 were complete to perform our analyses. This constitute a confidential and original set of data. To our knowledge other studies on the topic are mainly based on qualitative interviews. The study is considered at an exploratory stage due to the limited size of the current data set (under improvement).
The framework developed to analyse U-I collaborative Ph.D. was comprising of three interaction modes: prescriptive science (ie. transfer and absorption to the company’s context of scientific findings); prescriptive industry (ie. problem solving for the company / outsourcing); co-development (ie. each party is equally contributing to the research project). We also distinguished between three types of research strategies: product or service-oriented, disciplinary-oriented (ie. solving an already identify disciplinary gap) and exploration-oriented (transdisciplinary project and/or new disciplines).
Based on this framework and double-coding process to classify each research project, we conducted statistical descriptive analyses. We showed that co-development is practiced in half of projects in our sample. Surprisingly regarding the literature review, it is also the first mode of interaction for SMEs. Regarding research strategy, disciplinary-oriented is the most common one but exploration-oriented strategies are highly represented for large firms (c. 40%).
We then defined main collaboration archetypes. Two were related to the development & transfer of knowledge between the parties (prescriptive industry / product or services-oriented strategy; prescriptive science / discipline-oriented strategy). Two other archetypes were related to both industrial and scientific impact (co-development / discipline-oriented strategy; co-development / exploration). In our sample, double-impact projects were over-representing transfer projects.
Contribution to Scholarship
We contributed to the literature on U-I collaborative Ph.D. by developing an original framework to better understand firms and academics motives in terms of R&D contribution. Archetypes definition also provided a comprehensive vision of the literature on U-I collaborative Ph.D. projects that were appearing fragmented. Indeed, as most of the studies were qualitative ones, our framework both deal with very exploratory and collaborative projects such as those described in Harrysson & al. (2007) and more applied ones as those desbribed by Grimm (2018).
We also contributed to Univeristy – Industry R&D management literature by giving further insights regarding the high share of ‘double-impact’ projects compartivetly to ‘transfer projects’ in university – industry interactions. We also acknowledged that SMEs are also involved in that kind of ‘double-impact’ projects.
Further analyses will focus on extending project’s sample and adressing impact measurement (eg. patents, academic publications, etc.) for each archetype.
Contribution to Practice
The framework regarding research strategies and collaboration modes could help firms and universities to assess their U-I collaborative Ph.D. portfolio. Indeed, it can give assistance to insure alignment with R&D goals in terms of exploration / exploitation balance.
At a micro-level, the framework can also constitute an interesting tool in the project design phase to favour alignment between academics and firms partners.
The Framework and associated descriptive statistics can help policy-makers to design dedicated innovation-policies and incentives through a better understanding of those programme’s utilisation by firms and universities.
Our contribution appears relevant to the R&D Management conference track on the design of new industrial ecosystems. Indeed, we highlighted the different forms of research strategies and university - industry relationships through a particular collaboration scheme. We also provided insight regarding the share of transfer projects and double-impact projects.
Borrel-Damian, L., Morais, R. and Smith, J.H. (2015), Collaborative Doctoral Education in Europe: Research Partnerships and Employability for Researchers.
Grimm, K. (2018), “Assessin the industrial PhD: Stakeholder insights”, Journal of Technoloy and Science Education, Vol. 8 No. 4, pp. 214–230.
Harryson, S., Kliknaite, S. and Dudkowski, R. (2007), “Making innovative use of academic knowledge to enhance corporate technology innovation impact”, International Journal of Technology Management, Vol. 39 No. 1–2, pp. 131–157.
Malfroy, J. (2011), “The impact of university-industry research on doctoral programs and practices”, Studies in Higher Education, Vol. 36 No. 5, pp. 571–584.
Thune, T. and Børing, P. (2015), “Industry PhD schemes: Developing innovation competencies in firms?”, Journal of the Knowledge Economy, Vol. 6 No. 2, pp. 385–401.
de Wit-de Vries, E., Dolfsma, W.A., van der Windt, H.J. and Gerkema, M.P. (2018), “Knowledge transfer in university–industry research partnerships: a review”, Journal of Technology Transfer, Springer US, pp. 1–20.
Designing knowledge ecosystems through cognitive, behavioral, material and moral structures: A study of microfoundations
1Chalmers University of Technology, Sweden; 2Chalmers University of Technology, Sweden
The context of the study is an ecosystem in the transportation industry in Western Sweden. The paper explores the microfoundations of ecosystems by high-lighting dimensions of social structures in order to understand how individual level factors aggregate to the collective level in ecosystems.
Organizations engage in various relationships such as alliances, networks, communities, consortia, platforms and ecosystems for complex innovation (Adner and Kapoor, 2010; Chesbrough and Bogers, 2014; Dahlander and Frederiksen, 2012). This triggers a need to reconsider organization design in light of joint value creation (Adner and Kapoor, 2010). Clearly, in an ecosystem, it is not enough to consider how one organization can organize and manage internal innovation processes to create and capture value - all of the other partners need to do the same. Hence, ecosystems are dynamic in nature and there is no pre-existing template, rather it develops and changes over time, embodying “design” as a verb rather than a noun (Yoo et al., 2006; Boland Jr and Collopy, 2004). In understanding the microfoundations of ecosystems, the notion of social aggregation is central for linking micro and macro and to understand what “designing” an ecosystem entail.
Individuals’ beliefs, preferences, and interests provide a fruitful starting point for building theories of how social structures, such as ecosystems, originate and evolve. Still, in innovation management research, the understanding of how relationships and collaboration are designing ecosystems, i.e. the microfoundations (Barney and Felin, 2013) of ecosystems, remains relatively limited.
The RQ is: How can the social structures emanating from the relationships and collaboration that constitute the microfoundations of ecosystems be understood?
This paper is based on a 5-year qualitative case study (Eisenhardt, 1989; Yin, 2009) centred on an institutional multi-actor collaboration, called SAFER, which was created to address the innovation challenge of eliminating traffic fatalities and serious injuries in a Swedish context. As such, SAFER came to include key actors involved in the ecosystem working with these critical societal issues, also adding and changing partners over time.
The extensive qualitative material underlying our analysis consists of 120 interviews conducted over 5 years with stakeholders and participants involved in the collaboration. Archival data and document analysis of essential information about the collaboration informed our study. Additionally, the authors periodically attended and participated in activities and meetings at SAFER during 2009–2013. This allowed us to closely follow and document the organizing of the collaboration.
We identify three dimensions of social structure that appear essential for the endurance and evolution of the ecosystem:
1) The cognitive structure represents how actors and individuals exchange provisional understandings and try to reach a consensus about interpretations and course of action. This is established through dialoguing and providing space for informal meetings, which allows for sensemaking and utilization of the different perspectives needed to deal with the complex matters that unites the actors in the ecosystem.
2) The behavioral structure implies the creation of habits and routines that emerge as actors in the ecosystem need to find ways and methods of working together, and creating physical and mental space to meet. Through positive feedback, routines and processes which worked well and seemingly brought value to the ecosystem were reinforced and turned into a behavioral structure.
3) The moral structure represents a continuous discussion concerning values, norms, rights and duties of the actors and individuals that are part of the ecosystem. This is done e.g. through discussions about who is inside/outside of the ecosystem, how to relate to conflicting interests (individual actors vs the ecosystem), as well as clarifying expectations on what being part of the ecosystem means.
Contribution to Scholarship
By unpacking the social structures of ecosystems and how how relationships and collaboration are designing the ecosystems, this paper forms an important contribution to the understanding of the microfoundations of ecosystems in innovation management research (see e.g. Adner and Kapoor, 2010). Our paper adds to an emerging research stream considering how a design perspective can be useful to better understand what to consider when attempting to design and manage an emergent phenomenon such as an ecosystem.
Contribution to Practice
Our findings have implications primarily for two types of actors – those attempting to coordinate activities in ecosystems and actors that are part of ecosystems. For coordinating actors, there is a necessity to create space for social interaction, as it is through such interaction that all three structures are created and re-created over time based on positive feedback concerning what is conducive to the endurance and evolution of the ecosystem. For participating actors, active participation in the ecosystem is required to influence the structures that are created, and learn to balance potentially conflicting interest between actors and the ecosystem.
As the paper addresses the designing of ecosystems and draws upon a case study of a collaboration for innovation between academia, industry & society , it fits well with the general conference theme and the specific track of “The design of new industrial ecosystems” in particular.
Adner R and Kapoor R. (2010) Value creation in innovation ecosystems: How the structure of technological interdependence affects firm performance in new technology generations. Strategic Management Journal 31(3): 306-333.
Barney J and Felin T. (2013) What are microfoundations? Academy of Management Perspectives 27(2): 138-155.
Boland Jr RJ and Collopy F. (2004) Managing as designing: Stanford University Press.
Chesbrough H and Bogers M. (2014) Explicating open innovation: Clarifying an emerging paradigm for understanding innovation. In: Chesbrough H, Vanhaverbeke W and West J (eds) New Frontiers in Open Innovation. Oxford: Oxford University Press, 3-28.
Dahlander L and Frederiksen L. (2012) The core and cosmopolitans: A relational view of innovation in user communities. Organization Science 23(4): 988-1007.
Eisenhardt KM. (1989) Building theories from case study research. Academy of Management Review 14(4): 532-550.
Yin RK. (2009) Case Study Research: Design and Methods, Thousand Oaks, CA: Sage Publications.
Yoo Y, Richard J. Boland J and Lyytinen K. (2006) From Organization Design to Organization Designing. Organization Science 17(2): 215-229.
Managing interdependences in ecosystems through the design of alignment structures : an integrative framework of key levers and processes.
The management of interdependencies between innovators is a critical issue. Indeed, the structure of complementarities in assets or products, drives both value creation mechanisms (Adner, 2017) and value capture mechanisms (Teece, 2018) for innovative firms, resulting in an increasingly importance of ecosystem structuring.
The ecosystem construct describes interorganizational arrangements, half-way between networks and communities (Moore, 1993). Several research streams watered the ecosystem literature, such as research on platforms (Gawer & Cusumano, 2014), open innovation (Chesbrough, 2003), industry architecture (Jacobides et al., 2006), modularity (Brusoni & Prencipe, 2013) and coopetition (Brandenburger & Nalebuff, 1995), resulting in helpful but somehow disjointed insights. Though promising, this dynamic led to diverging visions and to a lake of clarity about what ecosystems are and what they bring to strategic management. In recent years important contributions proposed to refocus the ecosystem construct around the question of interdependencies (Jacobides et al., 2018). In this vein, Adner (2017) conceptualized ecosystems through a structuralist approach. The resulting «Ecosystem-as-structure» lens proposes the «alignment structure» -defined as the mutualy agreed repartition of roles and flows among the participants- as the core constitutive element of an ecosystem, defining the structure of interdependencies between firms.
Adner poses alignment structure as a practicable focal point to understand the intersections between individual and collective dynamics. However he does not give much insights about levers used to align partners within ecosystems nor about the processes by which an alignment structure is defined. These are important avenues for research.
Thus our present work seeks to fill these gaps, addressing the following questions : What are the strategic levers used by firms to align multilateraly with partners in ecosystems ? How are these levers collectively triggered ?
Given its ambition, the Ecosystem-as-Structure perspective can be useful only if it can lie on prior contributions in the ecosystem literature and link them within an integrative approach of ecosystems. So, our work is conceptual and relies on an extensive review of the existing literature on ecosystems and associated fields. We strived to look at these works through the lens offered by the "ecosystem as structure" perspective to identify the key levers used by firms to shape their alignment structure.
This paper advances 3 main contributions. First we identified four types of alignment to achieve in order, for firms, to collaborate in a context of multilateral interdependencies : cognitive alignment, behavioral alignment, technological alignment and assets alignment. Second, we identified three main levers of alignment : (1) The community lever relies on the development of a shared vision, a culture of collaboration, and common practices ; (2) The architectural lever relies on the development of a technological architecture which support collaboration through some level of modularization, standardization and plateform building ; (3) The reticular lever relies on staging channels to exchange resources, competences and knowledges. Finally, we identified that alignment structures are dynamic and are likely to evolve under the influence of endogenous factors but also because of exogenous factors. We conclude this article by proposing a framework to systematically address the design of alignement structures in an integrative way, taking into account business environment, the alignment structure and its dynamic.
Contribution to Scholarship
This study contributes to the literature on ecosystems by expanding and enriching the new « structuralist approach » proposed by Adner (2017). Doing so, it contributes to the conceptualization of the ecosystem construct, deepening the ecosystem-as-structure perspective. It also contributes to build a more structured approach of ecosystems to guide scholars by proposing an integrative framework for the study of ecosystems. This framework also proposes a better view on how to position the ecosystem construct within the strategic management literature by identifiying its critical links with other streams and futur avenues for the ecosystem-as-structure perspective.
Contribution to Practice
This study contributes to practice by enlightening the main levers that firms can leverage to design alignment structure around their value proposition. Doing so, the integrative framework we propose will help practitioners to take critical decisions when it comes to manage interdependencies around their products and innovation. It will also help practitioners to design successful business models, providing tools to design efficient value creation and valure capture mechanisms.
This study fits the purpose of this year’s conference track 4.1 (the design of new industrial ecosystems) as it gives usefull insight on how firms achieve to design collectively their ecosystem and their value proposition, identifying underlying tools and process of ecosystem design.
Adner, Ron. 2017. « Ecosystem as Structure: An Actionable Construct for Strategy ». Journal of Management 43(1): 39 58.
Brandenburger, Adam, et Barry Nalebuff. 1995. « The Right Game: Use Game Theory to Shape Strategy ». Harvard Business Review (July–August 1995).
Brusoni, Stefano, et Andrea Prencipe. 2013. « The Organization of Innovation in Ecosystems: Problem Framing, Problem Solving, and Patterns of Coupling ». In Collaboration and Competition in Business Ecosystems, Advances in Strategic Management.
Chesbrough Henry W. 2003. Open innovation, Harvard Business School Press.
Gawer, Annabelle, et Michael A. Cusumano. 2014. « Industry Platforms and Ecosystem Innovation ». Journal of Product Innovation Management 31(3): 417 33.
Jacobides, Michael G., Thorbjørn Knudsen, et Mie Augier. 2006. « Benefiting from Innovation: Value Creation, Value Appropriation and the Role of Industry Architectures ». Research Policy 35(8): 1200 1221.
Moore, James F. 1993. « Predators-and-Prey-A-New-Ecology-of-Competition.pdf ». Harvard business review.
Teece, David J. 2018. « Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world ». Research Policy 47(8): 1367 87.