Conference Agenda

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Session Overview
21-PM1-03: ST11.1 - Managing the New Mobility Transition
Friday, 21/Jun/2019:
1:00pm - 2:30pm

Session Chair: Rémi Maniak, Ecole Polytechnique
Location: Amphi Becquerel

Session Abstract

The automobile industry is implementing a major shift from its long established dominant design of internal combustion engine cars. "The future car will be electrical, communicative and autonomous" is the new shared horizon for all the global players of the car industry.

Such a paradigm change leads to a triple transition. (i) A technology transition from an internal combustion engine dominant design technologies to electric motorization, connectivity and artificial intelligence. (ii) A business model transition from a B to C product centric to a B to B to C mobility service business model. The profitability of the shift for car manufacturers is also far from evident: development of shared mobility is forecasted to diminish quite effectively the number of cars sold in mature urban markets; not to mention the redeployment of internal combustion engine based industrial footprint to electric cars production units. (iii) An ecosystem transition from an established internal combustion engine value chain to a new, heterogeneous and nescient ecosystem involving beyond the traditional auto suppliers new technology suppliers, energy and data providers, service providers and public authorities that will be key complementors in the new mobility scenarios.

Managing such breakthrough transition call for new approach in term of R&D organization, project management, open innovation processes... This track will welcome contributions which address such innovation management issues both from a theoretical and empirical side.

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Modelling electric vehicle market diffusion: A system dynamics model based on the Norwegian case and Recommendations for other Countries

Eya Meddeb, David Ziegler, Nizar Abdelkafi, Cyrine Tangour

Fraunhofer Center for International Management and Knowledge Economy, Business Models: Engineering and Innovation, Neumarkt 9-19, 04109 Leipzig, Germany


To understand the hidden factors that hinder the adoption of EVs, the authors develop a system dynamics model focusing on the EV market, while incorporating many influencing factors that drive the EV market diffusion. The generic model enables a better understanding of the E-mobility system in Germany and in Norway.


The diffusion of electric vehicles is a complex issue. Several authors used or developed theories and methods to estimate the future adoption of electric vehicles. For instance;

Benvenutti et al. (2017) and Yu et al. (2018) develop a system dynamics model. Adepetu et al. (2016) and Yang et al. (2018) use agent-based modeling, whereas Egnér and Trosvik (2018) and Javid and Nejat 2017.resort to discrete choice modeling. Other authors such as Yang et al. (2015) combine several approaches.

It should be noted, however, that most of the previous studies have focused on one specific region.

Literature Gap

We analyze several scientific research papers to identify the factors that influence the EV market diffusion. Moreover, we develop a comprehensive EV market diffusion model based on a system dynamics approach and apply this model to the case of Germany and Norway. This enables us to compare between both cases.

Research Questions

What are the existing models of EV market diffusion?

What are the relevant factors in the EV market diffusion?

What are the differences between Germany and Norway regarding EV market diffusion?

What are the policy recommendations that can be transferred from the case of Norway to Germany?


To develop the EV market diffusion model, we combine a system dynamics approach with the Bass model. System dynamics is a method that reveals how different variables of a system relate to each other (Xiang et al. 2017). The Bass model calculates the total adoption rate as the sum of adoptions that result from word of mouth and advertising and any other external effect (Sterman 2000)

Empirical Material

we do not have Empirical Material


Inspired by the Bass diagram, we develop a causal loop diagram that includes internal factors such as word-of-mouth and external factors such as government incentives (subsidies and restrictions), EV attributes (Driving-range, time-of-charge), charging station distribution, and car-manufacturer’s influence. The causal-loop-diagram is applied to the Norwegian and then to the German case.

Our analysis shows that the German government should focus on decreasing the purchase price of EVs compared to combustion engine vehicles (CVs) and should introduce restrictions regarding the use of CVs such as emission taxes, or driving restrictions. Moreover, these incentives should go hand in hand with the development of a strong network of charging stations.

This is in line with previous studies, in particular the work by Sierzchula et al. (2014) that show that strong financial incentives and charging infrastructures are positively correlated with national EV market shares, but there is definitely no evidence for causal relationships.

Furthermore, our work shows that the fast increase in EVs market share, especially at the beginning, will push car manufacturers to invest much more in E-mobility in order not to lag behind competitors.

Finally, policies can push customers to buy electric vehicles, driving car-manufacturers to making even more investments in E-mobility.

Contribution to Scholarship

This work provides a comprehensive review of existing models that focus on the factors affecting the adoption diffusion of EVs.

We contribute to literature by developing a comprehensive System dynamics model, more specifically a causal loop diagram that provides insights into the most important key factors affecting the diffusion of e-mobility. The model can be adapted to different regions.

Based on the developed model, an empirical study can be pursued in the future by implementing stock and flow diagrams, the second step in system dynamics, while using data from Norway and Germany. The models can be used to simulate different scenarios of policy recommendations.

Contribution to Practice

The qualitative analysis leads to valuable insights into both e-mobility systems of Norway and Germany. The model enables us to better understand the whole system behavior, which is the result of the interrelationships between influencing factors. This is of great interest to practitioners from industry and, policy makers that want to push the transition to an environmentally friendly and sustainable future.


E-mobility represents a systemic innovation that can only flourish, if different stakeholders contribute to it, in particular research-industry-society and policy makers. The EV diffusion model that we develop in the paper addresses the innovation challenge of e-mobility diffusion and makes explicit where contributions from society, academia and practice are required.


Publication bibliography

Adepetu, Adedamola; Keshav, Srinivasan; Arya, Vijay (2016): An agent-based electric vehicle ecosystem model. San Francisco case study. In Transport Policy 46, pp. 109–122. DOI: 10.1016/j.tranpol.2015.11.012.

Benvenutti, Lívia Moraes Marques; Ribeiro, Arthur Boeing; Uriona, Mauricio (2017): Long term diffusion dynamics of alternative fuel vehicles in Brazil. In Journal of Cleaner Production 164, pp. 1571–1585. DOI: 10.1016/j.jclepro.2017.07.051.

Egnér, Filippa; Trosvik, Lina (2018): Electric vehicle adoption in Sweden and the impact of local policy instruments. In Energy Policy 121, pp. 584–596. DOI: 10.1016/j.enpol.2018.06.040.

Federal Republic of Germany (2009): German Federal Government’s National Electromobility Development Plan. Berlin. Available online at

International energy agency-iea (2018): Nordic EV Outlook 2018: OECD.

Javid, Roxana J.; Nejat, Ali (2017): A comprehensive model of regional electric vehicle adoption and penetration. In Transport Policy 54, pp. 30–42. DOI: 10.1016/j.tranpol.2016.11.003.

Statista (2019): Anzahl der Elektroautos in Deutschland von 2006 bis 2018. Available online at

Sterman, John (2000): Business dynamics. Systems thinking and modeling for a complex world. Boston: Irwin/McGraw-Hill.

Transport & Environment (2018): CO2 emissions from cars: The facts. Edited by William Todts. European Federation for Transport and Environment AISBL. Available online at, checked on 1/30/2019.

United Nations (Ed.) (2015): ADOPTION OF THE PARIS AGREEMENT. Paris. Available online at

Xiang, Yue; Zhou, Hao; Yang, Wei; Liu, Junyong; Niu, Yi; Guo, Jiahui (2017): Scale Evolution of Electric Vehicles. A System Dynamics Approach. In IEEE Access 5, pp. 8859–8868. DOI: 10.1109/ACCESS.2017.2699318.

Yang, Wei; Xiang, Yue; Liu, Junyong; Gu, Chenghong (2018): Agent Based Modeling for Scale Evolution of Plug-in Electric Vehicles and Charging Demand. In IEEE Trans. Power Syst. 33 (2), pp. 1915–1925. DOI: 10.1109/TPWRS.2017.2739113.

Yang, Wei; Zhou, Hao; Liu, Junyong; Dai, Songling; Ma, Zhao; Liu, Youbo (2015): Market evolution modeling for electric vehicles based on system dynamics and multi-agents. In : 2015 International Symposium on Smart Electric Distribution Systems and Technologies (EDST). 2015 International Symposium on Smart Electric Distribution Systems and Technologies (EDST). Vienna, Austria, 8/9/2015 - 11/9/2015: IEEE, pp. 133–138.

The structuration of an innovation-driven industry: the effects of coopetive strategies on the value networks creation

Gulsemin Altundas

CEREFIGE (Research center), University of Lorraine, France


In our study, we focus on the creation and especially the structuration of the autonomous vehicle (AV) industry at the macro level. Industry level phases of structuration around the different value networks created and the coopetitive strategies that sustain them.


We combine two complementary frameworks: value network and coopetition. Existing studies on value network discuss the impacts of disruptive innovations on incumbents and new entrants in the context of an emergent industry. Tendency is to focus on either the disruptors or the incumbents (Ansari et al., 2016). Existing literature implement academic context regarding actions that can be taken by firms, whether they are the incumbents or new entrants. However, in this stream of literature, taking into account in parallel these two types of actor has been largely omitted as the macroeconomic study of the industry structuration introduced by disruptive technologies (Christensen, 1997).

Value networks structuration through coopetitive strategies

Introduced by Brandenburger and Nalebuff (1996), coopetition has been referred to as the junction of two paradoxical phenomenon that occur between competitors. . Our study encompasses vertical (Lechner, Soppe, & Dowling, 2016) and horizontal (Gnyawali, He, & Madhavan, 2006) coopetitive relationships.

Literature Gap

We choose the confluence of coopetition and value network literatures in order to pinpoint how coopetitive strategies enable to structure value in an emerging industry composed of value networks. We thus study value structuration at networks level (macro) rather than firm level.

Research Questions

How do coopetitive strategies enable the emergence of value networks around application?

Are poles of applications structure the AV industry?


To observe the structuration of this industry around key value networks and coopetitive strategies, a quantitative methodology has been adopted. We gathered secondary data and put into two related databases:

- « Market Players » : sector, industry and country of origin, the role, the type (traditional, incumbent or startup)

- « Market Moves 2016-2019 » coopetitive actions 2016-2019

Identification of five applications : Artificial Intelligence, Connectivity, Mobility Services, Data Processing and AV Designing

To analyze our data, we have coded and entered them into the UCINET (Borgatti et al., 2002) in order to generate mappings according to two segmentations: chronological ans technological

Empirical Material

In total 493 articles regarding all strategic moves made by firms such as mergers, acquisitions, partnerships, and collaborations have been collected from 2016 to February 2019. The articles were released by IHS Markit, a market intelligence company which works with global automotive industry players and gathers strategical information.


First, we correlate the number of firms with their country and industry of origin. Our results underline that R&D intensive countries have the largest number of firms acting for the development and deployment of the AV industry.

Concerning the origin industry, we note a clustering effect around two main industries: IT (46.9 and the consumer discretionary (composed of automobile and auto component manufacturers) (34.5%).

Second, we observe the creation of new key applications areas introduced by new entrants. This led us to the second finding, which considers application areas such as connectivity and artificial intelligence as, first, reasons for firms to opt for coopetition, and second as emerging value networks.

Third, our findings highlight the nature of firms, which contributes to the structuration of the AV industry. Data show that for the application area of connectivity, incumbents from Automobiles & Components industry undertake coopetitive strategic actions, whilst new entrants almost equally populate the application area of artificial intelligence (AI). Classification of importance has been enabled through companies’ centrality degree according to Freeman’s Centrality Degree on UCINET (Borgatti et al., 2002). This third finding underline that new players enable to solve incumbents’ growth needs whilst also widening their contact portfolio.

Contribution to Scholarship

These preliminary findings contribute to the literature on several ways. Indeed, research on value networks studied how isolation is not the way to go for firms willing to create value.

Insights of our study confirmed and enlarged prior findings on coopetition. Coopetition strategies are a way to create value within a value network composed by incumbents, new entrants, and startups. However, as data underlined it, they also are a way to create the value network itself, which is not systematically around a focal firm but rather around an application area. We focused on how firms contribute through coopetitive strategies to build a value network around a key application area. Over years, the constellation of firms around specific key application areas leads to the structuration of an entire industry. Yet, the study of industry structuration industry composed of application area value networks still has potential to be harnessed in further works.

Contribution to Practice



This study goes along with the key themes of the R&D Management conference as it is at the center of the industry structuration. Focus is given to market pleayers, wheter they are traditional, new entrant, or startups


Aldrich, H., & Ruef, M. (2006). Organizations Evolving. London: Sage Publications.

Ansari, S., Garud, R., & Kumaraswamy, A. (2016). The disruptor’s dilemma: TiVo and the US television ecosystem. Strategic Management Journal, 37(9), 1829–1853.

Borgatti, S.P., Everett, M.G. and Freeman, L.C. 2002. Ucinet 6 for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies.

Brandenburger, A. M., & Nalebuff, B. J. (1996). Coopetition. New York, NY: Doubleday.

Chesbrough, H. W. (2005). Open innovation: The new imperative for creating and profiting from technology. Boston, MA: Harvard Business School Press.

Christensen, C. M. (1997). The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Boston: Harvard Business Review Press.

Danneels, E. (2004). Disruptive technology reconsidered: A critique and research agenda. Journal of Product Innovation Management, 21(4), 246–258.

Gnyawali, D. R., He, J., & Madhavan, R. (Ravi) (2006). Impact of co-opetition on firm competitive behavior: An empirical examination. Journal of Management, 32(4), 507–530.

Golnam, A., Sanchez, R., Ritala, P., & Wegmann, A. (2014). The why and the how of coopetition: Modeling the incentives and design of coopetitive value networks. In A Focused Issue on Building New Competences in Dynamic Environments (pp. 29–60). Bingley: Emerald Group Publishing Limited.

Lechner, C., Soppe, B., & Dowling, M. (2016). Vertical coopetition and the sales growth of young and small firms. Journal of Small Business Management, 54(1), 67–84.

Möller, K., & Svahn, S. (2006). Role of knowledge in value creation in business nets. Journal of Management Studies, 43(5), 985–1007.

Ritala, P., & Hurmelinna-Laukkanen, P. (2009). What’s in it for me? Creating and appropriating value in innovation-related coopetition. Technovation, 29(12), 819–828.

Sroka, W., Cygler, J., & Gajdzik, B. (2014). The transfer of knowledge in intra-organizational networks: A case study analysis. Organizacija, 47(1), 24–34.

Tushman, M. L., & Anderson, P. (1986). Technological discontinuities and organizational environments. Administrative Science Quarterly, 31(3), 439–465.

Robust disruption: Competition and collaboration dynamics in the launch of electric vehicles in the automotive industry

Jan Lepoutre

ESSEC Business School, France


We explore the emergence of EV markets as a systemic disruption for the automotive industry, and try to understand patterns of collaboration and competition between rivals in the automotive industry during this systemic disruption.


Disruption increasingly piques the interest of scholars and practitioners for their destabilizing effect on the status quo and the uncertainty in the future of established practices and actors (Christensen, 1997; Henderson & Clark, 1990; Lavie, 2006; Tushman & Anderson, 1986). Although we have made considerable progress on how disruptors compete with the disrupted, or how actors battle for their version of disruption to be successful, we are only beginning to understand the role of collaboration in the process of disruption (Cozzolino & Rothaermel, 2018; Ozcan, 2018).

Literature Gap

Whereas some authors find that, in dealing with disruption, incumbents and entrants compete in the beginning and then collaborate later on (Marx et al., 2014), others find that collaboration precedes competition (Ozcan, 2018). We aim to explore the underlying drivers of these incompatible findings;

Research Questions

Our particular focus in this paper is therefore to seek answers on the question “How do organizations balance collaboration and competition over time in their responses to systemic and institutional disruption?”


In line with similar studies of industrial disruption (Hargadon & Douglas, 2001; Sgourev, 2013), we develop this emerging EV market as a case study (Yin, 2003) and draw on a historical narrative that was developed on the basis of primary and secondary data, which enables us to trace patterns of competition and collaboration between both incumbent and startup actors in this period.

Empirical Material

We draw on data collected from newspaper clippings, interviews, books and reports that were collected about the period spanning from 1990 to 2018.


In dealing with EVs, we find that incumbents in the automotive industry did not respond to EVs with reluctance or blocking the disruption or seeking dominance, but reacted in ways that reflected a “robust action” strategy (Eccles, Nohria, & Berkley, 1992; Ferraro, Etzion, & Gehman, 2015; Leifer, 1991): keeping options open across unforeseeable futures to preempt hostile attempts of others to narrow those options. This strategy was not new to the industry, as the maturity of the industry has lead incumbent OEMs to mitigate risk and high sunk costs through various forms of interorganizational collaboration or even consolidation (Sutton, 2001). As a result, they collaborated both with startups as well as other incumbents, yet limited this collaboration when the time for doing so would slow down responsiveness of an alliance in such a way that it would shut down options or relinquish control. We discuss the implications of our findings for organizational theory, strategy and the ONE literature.

Contribution to Scholarship

We contribute to scholarship in 2 ways:

- Given the inconclusive results on the role of competition and collaboration between rivals during periods of disruption, we highlight how maturity in high sunk cost industries may induce robust action strategies that trigger rivals to work together out of "fear of missing out". Rather than blocking the disruption, such collaboration and exploration actually reduces uncertainty in the industry and speeds up disruption. Rather than seeing collaboration between competitors only as a way to reduce disruption, it may actually speed up disruption and the ability of individual actors to compete subsequently.

- We introduce robust action as an important mechanism in inter-organizational collaboration.

Contribution to Practice

We highlight the beneficial role of interorganizational collaboration in times of high uncertainty and potential disruption Industries that have a habit of collaboration because of high sunk costs in a mature industry will therefore already have the capabilities and networks ready to engage in such collaboration.


Our paper is immediately relevant to theme 6.1 as it is specfically about systemic disruption and the issues faced in this process.


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Cozzolino, A., & Rothaermel, F. 2018. Discontinuities, competition, and cooperation: Coopetitive dynamics between incumbents and entrants. Strategic Management Journal, 39(12): 3053-3085.

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Hargadon, A. B., & Douglas, Y. J. A. s. q. 2001. When innovations meet institutions: Edison and the design of the electric light. 46(3): 476-501.

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Leifer, E. M. 1991. Actors and observers: a theory of skill in social relationships. New York, NY: Garland.

Ozcan, P. J. S. M. J. 2018. Growing with the market: H ow changing conditions during market growth affect formation and evolution of interfirm ties. 39(2): 295-328.

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From product to service offering: the OEM’s transition to mobility services

Theodore de Campigneulles

CRG, France


Autonomous cars, also called, self-driving cars, are currently expected by many to play an important role in the future of carmakers, helping them develop new products as well as new services. This phenomenon is called servitization. It stems from both a strategic intent and an organizational arrangement.


The evolution of carmakers through the development of services refers to the literature field of servitization. This field, which has been studied since the 1980s, has become even more popular with the rise of digital technologies. We can trace it back to the concept of servuction (Eiglier & Langeard, 1987). Autonomous vehicles are closely linked with the development of new services that will ensure their smooth operation, and bring more value to customers (Antonialli et al. 2018). Thus, OEMs need to develop new competencies that will enable them to operate these new services. The expected impact of servitization on a company’s KPI has also been an important element in this literature.

Literature Gap

Although servitization has been studied extensively, the application of this concept to the recent developments of the auto industry has not yet been the subject of detailed scrutiny.

Research Questions

This article focuses on the way a company developing new products also takes into consideration the development of new services. In this article we will study the coordination between the exploration of products and the creation of new services.


In this paper we will study case examples of ambidexterity that have allowed industrial companies to integrate the development of services. We will try to identify what assets and capabilities organizations have leveraged to develop services.

We will then conceptualize a matrix that helps identify the different product and service-oriented projects to analyze the carmakers’ strategic intent. This matrix will be founded on the Technological Readiness Level scale and key criteria in the development of AV technology that have emerged through our own research.

Empirical Material

The authors have had access to data both via semi-structured interviews, implication in several projects over the long term and, finally, via meetings and presentations about the development of AVs.


Preliminary results will be presented to assess the usefulness of the matrix, its scientific reliability and its relevance to the development of AVs in various technological and national contexts.

Contribution to Scholarship

This article linking the evolution of OEMs to servitization will give scholars a new tool to look at and study service-development projects.

Contribution to Practice

This article will give automobile analysts a useful tool to analyze the various strategic paths chosen by large companies that are developing AVs. Moreover, it will help managers visualize and communicate the different objectives of their projects.


This article focuses on how companies organize to develop AVs. As such, it delves into the evolution of the mobility paradigm and has its place in track 11.1 named Managing the New Mobility Transition.


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