Conference Agenda

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Session Overview
Session
20-PM1-04: ST4.2 - Orchestrating Platforms Ecosystems
Time:
Thursday, 20/Jun/2019:
1:00pm - 2:30pm

Session Chair: Mark DE REUVER, Delft University of Technology (Netherlands)
Session Chair: Thierry ISCKIA, Institut Mines-Telecom Business School (France)
Location: Amphi Poisson

Session Abstract

Creating a digital platform to access and leverage the potential of an ecosystem is recognized as an effective strategy to achieve market success. Platforms help ecosystems reach new potential and create the conditions for the development of catalytic reactions between various partners that, because they interact with each other, affect the value a platform can create for each of them. The more innovative these partners are, the more vibrant the ecosystem will be, subject to facilitating the transformation of new ideas into new products or services that can be brought to market. Platform ecosystems are characterized by a flexible and scalable architecture of cooperation designed to leverage collective intelligence (Nambisan & Sawhney, 2007; Prandelli et al, 2008), illustrating the ability to reach objectives that go beyond what could be possibly achieved by a single company. This network-centric innovation approach (Nambisan & Sawhney, 2011) focuses on cultivating innovation and creativity, on making, connecting and experimenting with the aim of turning shared knowledge into new products and/or services. In the recent years, platform-based ecosystems (Ceccagnoli et al, 2012; Isckia & Lescop, 2013; Cennamo & Santaló, 2013; Isckia, 2011, Isckia & Lescop, 2015, Tiwana, 2014, Choudary et al, 2016; McIntyre & Srinivasan, 2017) which are a subset of business ecosystems (Koenig, 2013) became a « recurrent pattern of behaviour » (Allen, 1983) in terms of innovation.

These architectures of collaboration rely on an original approach of value creation which is based on multiple processes sequentially and/or simultaneously involving different actors, communities, activities and resources. As such, platform ecosystems implement a set of rules and governance mechanisms designed to facilitate the enrollment of independent actors in the pursuit of distributed, collaborative, and cumulative innovation. These institutional mechanisms (Sharapov, Thomas & Autio 2013, Thomas & Autio, 2014) are critical to the understanding of how platform-based ecosystems coordinate innovation efforts. The aim of this track is to go a step further in platform ecosystems analysis, focusing on orchestration processes i.e. how the keystone organization or platform leader ensures everyone's contribution throughout the platform's life cycle.


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Presentations

Engaging with startups in platform-based business models to enable explorative and exploitative learning

Ellen Enkel, Veronika Sagmeister

Zeppelin University, Friedrichshafen, Germany

Context

Collaboration with startups in platform-based business models as a mean for companies operating in moderately changing markets to pursue exploration and thus to archive organizational ambidexterity.

Literature

Research addressing platforms within firms and how organizational capabilities can be used to reorient a firm’s competitive scope through capability creation, combination, re-orientation and deployment began in the 1990s. Only recently, Teece (2017) broadened his research on dynamic capabilities and connected it with platform lifecycle research. Helfat and Raubitschek (2018) built upon the research of Teece (2018) and proposed three types of dynamic capabilities that are critical for platform leaders.

Literature Gap

However, only a few studies have focused on engagement with startups in platform-based business models or collaboration with startups to enable exploration and exploitation.

Research Questions

Therefore, we extend these studies by addressing the following questions: How does engagement with startups in platform-based business models support explorative and exploitative learning in high-velocity and moderately changing markets?

Methodology

We follow a case study approach, which provides two main advantages. First, immersion in the abundance of detailed case data and the use of various sources that describe the phenomenon from different perspectives allow us a deep understanding of companies’ strategic engagement with startups in platform-based business models. Second, case studies enable us to examine phenomena in their context without clearly defined boundaries between context and phenomenon. This helps us understand why these changes occur and how the mechanism underlying them works.

Empirical Material

11 case companies are included in the study.

Results

Our empirical investigation of 11 startup collaboration platforms reveal that companies participating in markets with different velocities acquire substantially different dynamic capabilities when integrating startups in their platforms, which results in either explorative or exploitative innovation. The first surprising result our analysis reveals is that startup platforms in moderately changing markets strive for exploration while those in high-velocity markets aim for exploitation. Consequently, there are differences between the groups in terms of the dynamic capabilities needed to reach their aims.

Contribution to Scholarship

First, regarding ambidexterity (March, 1991), we find evidence that contrary to existing theory, platforms in high-velocity markets pursue exploitative goals by engaging with startups while platforms in moderately changing markets strive for explorative results through their engagement with startups. Consequently, our second contribution reveals that dynamic capabilities differ based on whether a company is pursuing explorative or exploitative learning in high-velocity and moderately changing markets. These differences play a key role in seizing and reconfiguring capabilities and a minor role in sensing capability. Third, combining dynamic capabilities and integrating startups’ knowledge in platforms, we find that contrary to Teece (2007) decision errors regarding the selection of startups are not as damaging in high-velocity markets as they are in moderately dynamic markets, since companies in high-velocity markets that engage with startups in their platform-based business models have already established a company-wide culture of failure, with failure considered a calculated risk.

Contribution to Practice

If firms pursue the objective of exploiting the corporate’s technology in new product-market areas to strengthen platform leadership and to increase platform’s value, those firms should engage with a higher number of startups, that either deploy the corporate’s technology in their solution, or develop complementary applications for the platform.

If firms pursue the objective of exploring new opportunities to foster innovation through platform-based business models, firms should engage with startups with which they either can jointly develop the platform in the first place, jointly build innovations for the platform or integrate startup’s innovation in the platform.

Fitness

Collaboration with startups to pursue platform-based business models and enable exploration and exploitaiton is relevant for open innovation topic as well as for the special track on ecosystems.

Bibliography

Helfat, C. E. & Raubitschek, R. S. (2018). 'Dynamic and integrative capabilities for profiting from innovation in digital platform-based ecosystems'. Research Policy.

March, J. G. (1991). 'Exploration and exploitation in organizational learning'. Organization Science, 2, 71-87.

Teece, D. J. (2007). 'Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance'. Strategic Management Journal, 28, 1319-1350.

Teece, D. J. (2017). 'Dynamic capabilities and (digital) platform lifecycles'. In Entrepreneurship, Innovation, and Platforms: Emerald Publishing Limited, 211-225.

Teece, D. J. (2018). 'Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world'. Research Policy.



Capabilities development to attract complementation in ecosystems based on digital platforms

Ana Lucia Figueiredo Facin1,2, Leonardo Augusto de Vasconcelos Gomes1, Mauro de Mesquita Spinola1, Mario Sergio Salerno1

1Universidade de São Paulo, Brazil; 2UNESP - Univ Estadual Paulista, Brazil

Context

Ecosystems based on digital platforms are considered as a source of dynamism and innovation for many technologies, products and services. To deal with multilateral relationships and attract complementation, platform leaders are required to develop platform capabilities that enable these companies both to create and to capture value in the ecosystem.

Literature

Most successful companies in leading platform-based ecosystems are those that can open their platforms to collaborate with external developers and maintain coordination and control of the whole system (Boudreau, 2017). Parker, Van Alstyne and Jiang (2017) point out that those companies have chosen to innovate through external relationship, and that value creation is changing from the inside out. Teece (2018) argues that creating and capturing value requires from platform leader a mix of openness to attract the complementors and a degree of control to keep users satisfied. Gawer and Cusumano (2013) highlighted the need of capabilities to explore the innovative ideas of external companies, which are not necessarily part of their supply chain. Isckia and Lescop (2015) add that managing a platform-based ecosystem is not only about building a network of partners but also building coordination processes to engage key partners that will influence the participation of other ecosystem partners.

Literature Gap

Recent researches highlight the importance of deepening studies for detailing the specific types of capabilities required of the platform leaders and how these capabilities can enable those companies open their platforms to both create and capture value in ecosystems based in digital platforms (Teece, 2018; Helfat and Raubitschek, 2018).

Research Questions

The research question is: How do platform leaders develop capabilities to attract complementation allowing the evolution of platforms from internal to external to create the so-called ecosystems based on digital platforms? The research focus is the discussion of the capabilities related to the relationship between platform leaders and complement providers.

Methodology

The research used a qualitative approach. A multiple case study analysis was conducted to develop the research problem. Empirical studies were conducted on leaders of digital platforms and companies that develop products and/or services that complement the platforms (complement providers).

Empirical Material

The case study was conducted through interviews and information gathering in 6 platform leaders, and at least two companies that develop add-ons for the platform (total of 16 complement providers). Some interviews were conducted in person and others were conducted by telephone or audio conference, for a total of 42 interviews (approximately 50 hours). The websites of the companies were consulted before and after the interviews to complement the data collection, as well as the material provided or indicated by the respondents, during the interviews, was analyzed. All the interviews carried out in each case were transcribed and analyzed using Nvivo software.

Results

A tentative theoretical framework was proposed, indicating that, to open their platforms (in the supply chain or to create platform-based ecosystems - those considered as different degrees of overture and control of the platform by Gawer (2014)), the platform leaders develop a set of capabilities linked to the relationship with complement providers according to the degree of overture and control of the platform. In this context, the study also suggests that complement providers acquire different capabilities depending on the type of platform they adopt.

Contribution to Scholarship

As a scientific contribution we highlight the proposition of a tentative theoretical framework to extend the theory proposed by Teece (2018), regarding the development of some specific platform capabilities in the context of the ecosystems based on digital platforms by the platform leaders and additionally by the complement providers, identified as different from those that are developed when the platform is opened for complementation just in the supply chain.

Contribution to Practice

Preliminary findings indicate that different degrees of overture and control of platforms require different sets of capabilities, not only for the platform leader, but also for complementors who innovate the platform. Knowing this, managers can establish the most appropriate set of capabilities to orchestrate their complementors and proactively manage the platform by creating compelling business models, making it clear how the platform can create value and how companies would be able to capture value from it.

Fitness

Our research is aligned with the general theme as it discusses issues related to ecosystems based on digital platforms and how to enhance open innovation. As well as discusses capabilities developed by platform leaders, as one of the perspectives to deal with the theme of orchestration of platform ecosystems.

Bibliography

BOUDREAU, K. J. (2017) Platform boundary choices & governance: Opening-up while still coordinating and orchestrating. In: FURMAN, J.; GAWER, A.; SILVERMAN, B. S.; STERN, S. (Eds.). Entrepreneurship, Innovation, and Platforms (Advances in Strategic Management, Volume 37), Emerald Publishing Limited, p. 227 – 297.

GAWER. A. (2014). Bridging differing perspectives on technological platforms: Toward an integrative framework. Research Policy, 43(7), 1239-1249.

GAWER. A.; CUSUMANO, M.A. (2013). Platforms and Innovation. In: DODGSON, M.; GANN, D. M.; PHILLIPS, N. (Eds.). The Oxford handbook of innovation management. OUP Oxford.

HELFAT, C. E.; RAUBITSCHEK, R. S. (2018). Dynamic and integrative capabilities for profiting from innovation in digital platform-based ecosystems. Research Policy, 47(8), 1391-1399.

ISCKIA, T.; LESCOP, D. (2015). Strategizing in Platform-based ecosystems: Leveraging Core Processes for Continuous Innovation. Communications & Strategies, 1(99), 91-111.

PARKER, G.; VAN ALSTYNE, M.; JIANG, X. (2016). Platform ecosystems: How developers invert the firm. Boston University Questrom School of Business Research Paper No. 2861574. Available in: http://dx.doi.org/10.2139/ssrn.2861574.

TEECE, D. J. (2018). Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world. Research Policy, 47(8), 1367-1387



Platform strategies under the Artificial Intelligence prism

François Acquatella, Valérie Fernandez, Thomas Houy

Télécom ParisTech, France

Context

Our proposal is to analyze the technological dimension of the platforms, more particularly through artificial intelligence (AI) technologies. we analyze AI as a variable of differentiation of platforms types and as lever of specific strategic dynamics.

Literature

Argal, A., Gupta, S., Modi, A., Pandey, P., Shim, S., & Choo, C. (2018). «Intelligent travel chatbot for predictive recommendation in echo platform». In Computing and Communication Workshop and Conference (CCWC).

Blanquet, M. F. (2016). « Intelligence artificielle et système d'information: le langage nature »l. FeniXX.

Evans, D. S., & Schmalensee, R. (2016). « Matchmakers : the new economics of multisided platforms ». Harvard Business Review Press.

Parker, G. G., Van Alstyne, M., Choudary, S. P., & Foster, J. (2016). «Platform revolution: How networked markets are transforming the economy and how to make them work for you » (p. 352). New York: WW Norton.

Tatsumoto, H., Ogawa, K., & Shintaku, J. (2011). « Strategic Standardization ». Annals of Business Administrative Science, 10, 13-26.

Literature Gap

Most research on platform economics analyzes them in light of the new market mechanisms that they characterize.

The question of the technological infrastructure that underlies them is little discussed, while it is "organizing" their ecosystems and associated markets.

Research Questions

Our research project aims to better understand the strategic dynamics of platforms, in particular by analyzing the role and impacts of AI technologies. How markets are structured around platform technology infrastructure and how is AI the differentiating variable?

Methodology

Online Desk Research - we mobilized two approaches for digging out the relevant information from internet. One by directly browsing the specific information from platform websites and extracting the information out of these sites. Secondly, by using the various search engines like google search/scholar , for modulated searching.

Empirical Material

not relevant

Results

For technological platforms, interoperability and AI allow the platform to combine different data from different technologically synchronized sources. Through its technological architecture, the platform controls the direction of technological innovation by controlling the data generated by its "satellite actors". the platform designs and tests technological offers and products, imposing on its ecosystem, a dynamic of innovation in a form "path dependency"

Aggregation platforms are characterized by a form of disintermediation-re-intermediation of markets; This approach is based on capturing part of the value chain at the expense of traditional intermediaries. Controlling the flow of information generated by their different partners is essential to coordinate their ecosystem around federative and collective strategies. Algorithmic content is required to develop the performance of the recommendation tools, especially to streamline distributed information and synchronize the various platform partners.

The coordination platforms propose a new form of intermediation. these palteforms mobilize assets (cars, houses, etc.), under-exploited, which, evaluated in a new way, create and coordinate a market by creating a new demand.

These platforms reinvent business models by changing the way users consume and the type of service / product they consume. They use machine learning to generate new value propositions.

Contribution to Scholarship

If the generic platform model presented in the literature presents a holistic approach necessary for framing and understanding the economic phenomenon of the platforms, it does not envisage certain attributes and specificities of the different business models by types of platforms. Methods of creating and developing value networks and value propositions are indeed presented in a generic and not very specific way.

Our research project aims to better understand the strategic dynamics of platforms, particularly by analyzing the role and impacts of AI technologies. As well, The AI as a variable differentiating strategic dynamics seems little mobilized in the literature related to platforms.

Contribution to Practice

The choice to build your own platform or join an existing platform must be consistent with its positioning, the expectations of users and its ability to innovate "vis-à-vis" the competition. Concretely, this requires a lot of trade-offs, to choose partners, to develop an economic model, to measure costs and to distinguish the levers of growth.The proposed conceptual framework can be read as an appropriate basis for internal business strategy discussions surrounding Big Data and AI investments, explaining how companies can create value through the harmonization of their different AI technology approaches.

Fitness

The life of a platform ecosystem is based on their ability to continuously articulate a cycle of innovation. The analysis of strategic decisions is closely linked in that they allow the platform to multiply synergies in a perspective of tenfold increase in value but also survival against competing ecosystems.

Bibliography

Acquatella, F., Fernandez, V., & Houy, T. (2018). « Le cas Coursera ou la préfiguration des changements en cours sur les plateformes d’apprentissage en ligne », Distances et médiations des savoirs [En ligne], 24 | 2018

Argal, A., Gupta, S., Modi, A., Pandey, P., Shim, S., & Choo, C. (2018). «Intelligent travel chatbot for predictive recommendation in echo platform». In Computing and Communication Workshop and Conference (CCWC).

Benavent, C. (2016). « Plateformes. Sites collaboratifs, marketplaces, réseaux Sociaux. Comment ils influencent nos choix », FYP editions.

Bishop, C. M. (2006). « Pattern recognition and machine learning ». springer.

Blanquet, M. F. (2016). « Intelligence artificielle et système d'information: le langage nature »l. FeniXX.

Bottou, L., Curtis, F. E., & Nocedal, J. (2018). » Optimization methods for large-scale machine learning ». Siam Review, 60(2), 223-311.

Christensen, Clayton M. (1997). «The Innovator’s Dilemma: When New Technologies

Cause Great Firms to Fail. Boston», Harvard Business School Press.

Chung, M., Joung, H., & Ko, E. (2017). « The role of luxury brand’s conversational agents : comparaison between face-to-face and chatbot ». Global Fashion Management Conference at Vienna (pp. 540-540).

Daumal, S. (2015). « Design d'expérience utilisateur : Principes et méthodes UX » . Editions Eyrolles.

Cardon, D. (2015). « À quoi rêvent les algorithmes, Nos vies à l’heure des big data». Paris, Le Seuil, 2015, 108 p.

Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). « From game design elements to gamefulness: defining gamification ». In Proceedings of the 15th international academic MindTrek conference: Envisioning future media environments (pp. 9-15). ACM.

Evans, D. S., & Schmalensee, R. (2016). « Matchmakers : the new economics of multisided platforms ». Harvard Business Review Press.

Fogg, B. J. (2009, April). « A behavior model for persuasive design ». In Proceedings of the 4th international Conference on Persuasive Technology (p. 40). ACM.

Gawer, A. (2011). Platforms, Markets and Innovation. Edward Elgar Publishing Ltd.

Hagiu, A., & Wright, J. (2015). « Multi-sided platforms ». International Journal of Industrial Organization, 43, 162-174.

Hartmann, ph., Zaki, M., Feldmann, N. et Neely, A. (2014) « Big Data for Big Business? A Taxonomy of Data-driven Business Models used by Start-up Firms », Cambridge Service Alliance.eng.cam.ac.uk.

Huotari, K., & Hamari, J. (2012, October). « Defining gamification : a service marketing perspective ». In Proceeding of the 16th international academic MindTrek conference (pp. 17-22). ACM.

Kaptein, M., Markopoulos, P., De Ruyter, B., & Aarts, E. (2015). « Personalizing persuasive technologies : Explicit and implicit personalization using persuasion profiles ». International Journal of Human-Computer Studies, 77, 38-51.

Kim, W. C., & Mauborgne, R. (2005). « Blue ocean strategy : from theory to practice », California management review, 47(3), 105-121.

Landau, I. D., & Landau, V. (2016). « Data Mining et Machine Learning dans les Big Data Une tentative de demystification » Conference- GIPSA –LAB, Grenoble.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). « Deep learning ». Nature, 521(7553), 436-444.

Petrova, E., Pauwels, P., Svidt, K., & Jensen, R. L. (2019). « In search of sustainable design patterns: Combining data mining and semantic data modelling on disparate building data. In Advances in Informatics and Computing » Civil and Construction Engineering pp. 19-26.

Marty, F. (2017). « Algorithmes de prix, intelligence artificielle et équilibres collusifs ». Revue internationale de droit économique, 83-116.

Mazzei, M. J., & Noble, D. (2017). «Big data dreams: A framework for corporate strategy ». Business Horizons, 60(3), 405-414.

Oinas-Kukkonen, H., & Harjumaa, M. (2009). « Persuasive systems design: Key issues, process model, and system features ». Communications of the Association for Information Systems, 24(1), 28.

Pang, B., & Lee, L. (2008). «Opinion mining and sentiment analysis». Foundations and Trends® in Information Retrieval, 2(1–2), 1-135.

Parker, G. G., Van Alstyne, M., Choudary, S. P., & Foster, J. (2016). «Platform revolution: How networked markets are transforming the economy and how to make them work for you » (p. 352). New York: WW Norton.

Sam Ransbotham, David Kiron et Pamela Kirk Prentice, (2016) « Beyond the Hype : The Hard Work behind Analytics Success », ibid; id.

Sarasvathy, S. D. (2001). « Causation and effectuation: Toward a theoretical shift from economic inevitability to entrepreneurial contingency ». Academy of management Review, 26(2), 243-263.

Schmidhuber, J. (2015). « Deep learning in neural networks : An overview ». Neural networks, 61, 85-117.

Schneider, D., Lins, S., Grupp, T., Benlian, A., & Sunyaev, A. (2017). « Nudging Users into Online Verification: The Case of Carsharing Platforms ». Associations for information systems.

Szczodrak, M., & Czyżewski, A. (2016). « Face detection algorithms evaluation for the bank client verification ». In Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2016 (pp. 186-190).

Tatsumoto, H., Ogawa, K., & Shintaku, J. (2011). « Strategic Standardization ». Annals of Business Administrative Science, 10, 13-26.



Entering the Black Box of Platform Orchestration: A metaphoric co-evolutionary framework for platform-based ecosystems

Xavier Francis Etienne Pierre PARISOT1, Thierry ISCKIA2, Pierre VIALLE2

1The Institute for Knowledge and Innovation, Bangkok University, Thailand; 2Institut Mines-Télécom Business School

Context

Since the first empirical definition of business ecosystems (BEs), its central orchestration dynamic has been defined as co-evolutive. If the co-evolutionary nature of inter-organizational innovation processes has been largely characterized, the associated generative mechanisms and triggering factors are still debated.

Literature

In platform-based ecosystems (PBEs), platform leaders attract and aggregate third-party players that increase the platform value proposition. Niche players are connected to the platform through shared or open-source technologies (Gawer & Cusumano, 2014). The platform is an artifact designed to ensure the coupling of interorganizational innovation and business development ecosystemic core processes (Isckia, 2018).

Orchestrating their platforms, keystones organizations ensure value creation, knowledge mobility, innovation appropriability, and network stability by applying mutualism mechanisms (Dhanaraj & Parkhe, 2006). If the platform provides architectural scalability and enhances collective intelligence, only the triggering of coevolutionary mechanisms leads to the development of a BE (Moore, 1996). Peltoniemi suggests 4 preconditions that need to be fulfilled in order to trigger co-evolution. Loilier & Malherbe reported 3 types of ecosystemic capabilities required for BE emergence. To initialize a coevolutive sequence and activate these different mechanisms, ecosystem’s members need to develop and co-evolve their dynamic capabilities (Teece, 2017).

Literature Gap

Following Jacobides et al. (2016), if the literature better characterizes PBEs, “there is little explanation of how firms mutually adapt” in BEs. Moreover, platform orchestration co-evolutionary mechanisms (CMs) remain poorly understood and mobilize analogically different and sometimes contradictory biological concepts (Parisot et al., 2018).

Research Questions

Only a switch from analogy to metaphor allows the needed semantic, conceptual and theoretical alignments within the context of strategic management (Indurkhya, 1991). Achieving these alignments implies to answer one central question: How to metaphorically define co-evolutive phenomenon in strategic management?

Methodology

Cornelissen (2005) developed the domains-interaction model (DIM) to clarify the steps of purely metaphorical imports in organizational sciences. The application of the DIM allows:

1) the needed semantic and structural alignment to occur at the conceptual and theoretical levels

2) to better benefit from the understanding of the CMs involved in biological complex adaptive systems

3) to overcome all the analogical weaknesses generated by the paradigmatic fragmentation of the CMs in Biology

4) to solve most of the current ontological, semantic, epistemological, and structural issues generated by the current analogical transpositions (Parisot et al., 2018)

Empirical Material

Most of the growing number of coevolution empirical descriptions in strategic management forgets to define it (McKelvey, 1997). Existing accounts of ecosystem dynamics are quite scarce in the academic literature and the few available descriptions of CMs in PBEs provide limited empirical support (Dhanaraj & Parkhe, 2006; Pellinen et al., 2012; Loilier & Malherbe, 2012). From this point of view, platform orchestration is still a black box that needs to be illuminated.

Results

CMs have been already identified in different paradigms dominant in Genetics (3) and Ecology (4) and positioned in their respective theoretical structures (Parisot et al., 2018). This previous analysis allows the consideration of the logical structure connecting the concepts to each other in the source domain of Biology.

The metaphorical transposition of co-evolution and its associated concepts enables:

1) the clarification of its defining attributes and therefore the completion of its ontogenesis in strategic management

2) the metaphorical transposition of the associated concepts (mutation, migration, selection, genetic drift, pressure, genotype, phenotype, inheritance) and simultaneously their semantic and structural alignment

3) the empowerment of the conceptual connections between co-evolution and its associated concepts mobilized in the Evolutionary Theory of the Firm (Nelson & Winter, 1982)

4) the distinction between 2 evolutionary mechanisms and 3 forms of coevolutive mutualism

5) a better specification of the CMs encountered in PBEs

This metaphorical theorization contributes to a more fine-grained understanding of platform-based ecosystem coordination processes and, paves the way for further empirical characterization of yet unidentified generative mechanisms and their associated triggering factors.

Contribution to Scholarship

Moreover, this transposition allows the specification of the pre-requisites needed to pursue the empirical identification of CMs in PBEs:

1) Since CMs are hidden generative mechanisms, critical realism constitutes the optimal epistemology to apply

2) CMs should be considered in a diachronic perspective (McKelvey, 1997)

3) CMs arise only between different populations of organizations and/or communities

4) Intra and inter-organizational scales must be both simultaneously considered since CMs operate within and across organizational scales

Finally, by clarifying the differences between imitation, co-adaptation and, 3 forms of coevolutive mutualisms, this metaphorical theorization allows to better distinguish evolutionary mechanisms from CMs in PBEs and more largely in strategic management.

This distinction is of the utmost importance regarding the countless confusions existing in the current literature (Parisot et al., 2018).

Contribution to Practice

This metaphorical theorization contributes to a more fine-grained understanding of platform-based ecosystem coordination processes and, paves the way for further empirical characterization of yet unidentified generative mechanisms and their associated triggering factors.

Fitness

In knowledge-intensive industries, business is increasingly led by BEs and PBEs. To help organizations build or join these ecosystems, strategic management must move from description to prediction and, produce theoretical models able to support ecosystemic generative mechanisms identification through empirical validation. This article proposes this type of model.

Bibliography

1-Cornelissen, J. P. (2005). Beyond compare: Metaphor in organization theory. Academy of Management Review, Vol.30, n°4, p.751-764.

2-Dhanaraj, C., & Parkhe, A. (2006). Orchestrating innovation networks. Academy of management review, Vol.31, n°3, p.659-669.

3-Gawer, A., & Cusumano, M. A. (2014). Industry platforms and ecosystem innovation. Journal of Product Innovation Management, 31(3), 417-433.

4-Indurkhya, B. (1991). Modes of metaphor. Metaphor and Symbol, Vol.6, n°1, p.1-27.

5-Isckia, T., De Reuver, M., & Lescop, D. (2018). Toward an evolutionary framework for platform-based ecosystem analysis (No. hal-01992440).

6-Jacobides, M. G., Cennamo, C., & Gawer, A. (2018). Towards a theory of ecosystems. Strategic Management Journal, Vol.39, n°8, p.2255-2276.

7-Loilier, T., & Malherbe, M. (2012). Le développement des compétences écosystémiques. Revue française de gestion, Vol.3, p.89-105.

8-McKelvey, B. (1997). Perspective—Quasi-natural organization science. Organization science, Vol.8, n°4, p.351-380.

9-Moore, J. F. (1996). The death of competition: leadership and strategy in the age of business ecosystems, New York: Harper Business.

10-Nelson, R., & Winter, S. (1982). The Evolutionary Theory of the Firm.

11-Parisot, X., Isckia, T., Vialle, P., & Throngvid, H. (2018). Comment définir les phénomènes de coévolution en management stratégique ? in Neuvièmes journées du Groupe Thématique Innovation de l'AIMS." Communautés, écosystèmes et innovation" (p. 1-32), 18-19 octobre 2018, Montréal, Canada.

12-Pellinen, A., Ritala, P., Järvi, K., & Sainio, L. M. (2012). Taking initiative in market creation- a business ecosystem actor perspective. International Journal of Business Environment, Vol.5, n°2, p.140-158.

13-Peltoniemi, M., and Vuori, E. (2004) Business ecosystem as the new approach to complex adaptive business environments. Proceedings of eBusiness research forum. Vol. 2.

14-Teece, D. J. (2017). Dynamic capabilities and (digital) platform lifecycles. in Entrepreneurship, Innovation, and Platforms (p.211-225). Emerald Publishing Limited.



 
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