Contents lists available at ScienceDirect
Long Range Planning
journal homepage: www.elsevier.com/locate/lrp
Mapping, analyzing and designing innovation ecosystems: The
Ecosystem Pie Model
, Bob Walrave
, Ksenia S. Podoynitsyna
, Jan Holmström
A. Georges L. Romme
Eindhoven University of Technology, Department of Industrial Engineering and Innovation Sciences, P.O. Box 513, 5600 MB, Eindhoven, the
JADS, The Joint Graduate School of Tilburg University and Eindhoven University of Technology, Sint Janssingel 92, 5211 DA, 's-Hertogenbosch, the
Aalto University, School of Science, Department of Industrial Engineering and Management, P.O. Box 15500, FI-00076, Aalto, Finland
EIT InnoEnergy Ph.D. School Fellow, KIC InnoEnergy SE, Eindhoven, the Netherlands
To achieve a complex value proposition, innovating ﬁrms often need to rely on other actors in
their innovation ecosystem. This raises many new challenges for the managers of these ﬁrms.
However, there is not yet a comprehensive approach that would support managers in the process
of analysis and decision making on ecosystem strategy. In this paper, we develop a strategy tool
to map, analyze and design (i.e., model) innovation ecosystems. From the scholarly literature, we
distill the constructs and relationships that capture how actors in an ecosystem interact in
creating and capturing value. We embed these elements in a visual strategy tool coined the
Ecosystem Pie Model (EPM) that is accompanied by extensive application guidelines. We then
illustrate how the EPM can be used, and conclude by exploring the multiple aﬀordances of the
EPM tool as a boundary object between research and practice.
In a world of increasingly specialized organizations, a single ﬁrm typically does not possess the resources to develop and com-
mercialize a complex value proposition from start to ﬁnish (Appleyard and Chesbrough, 2017;Kapoor and Furr, 2015). Therefore,
ﬁrms often need to rely on other actors in their innovation ecosystem, to build an ecosystem-wide value proposition (Adner, 2012) that
materializes when the individual contributions of diﬀerent actors are combined (Hannah and Eisenhardt, 2017). On the one hand, the
interdependency in ecosystem relationships conﬁnes ﬁrms; for instance, it delays the launch of new products/services until com-
plementary elements from ecosystem actors become available (Dattée et al., 2018;Overholm, 2015). On the other hand, ﬁrms can
leverage ecosystem relationships for higher value creation by exploiting the synergies and network eﬀects arising from com-
plementarities across actors (Adner and Feiler, 2017;Clarysse et al., 2014).
Academic research has kept pace with the trend toward ecosystem-based innovation, resulting in a substantial list of con-
siderations that apply in this setting. For example, managers have been advised to consider the modularity within the ecosystem
(Baldwin, 2008); the corresponding structure by which value is created in the ecosystem (Adner and Kapoor, 2010); the particular
Received 15 September 2018; Accepted 22 September 2018
Corresponding author. Eindhoven University of Technology, Department of Industrial Engineering and Innovation Sciences, P.O. Box 513, 5600
MB, Eindhoven, the Netherlands.
E-mail address: email@example.com (M. Talmar).
Long Range Planning 53 (2020) 101850
Available online 11 October 2018
0024-6301/ © 2018 The Authors. Published by Elsevier Ltd.
roles of actors within the value structure (Dedehayir et al., 2018); the (potential) network eﬀects arising from the ecosystem com-
position (Williamson and De Meyer, 2012); the strategies for aligning the actors to the value proposition of the ecosystem (Walrave
et al., 2018); the interfaces of collaboration between parties (Davis, 2016); the types of complementarity between the diﬀerent actors
(Jacobides et al., 2018); the inﬂuence of the properties of involved actors on their relative bargaining power and on the likelihood of
these actors to contribute in a desirable way (Adner, 2006;Autio and Thomas, 2014); the risk and value trade-oﬀof having more (or
less) interdependent actors involved in the innovation process (Adner and Feiler, 2017); and the potentially asymmetric inter-
dependence of the actors, and the implications of such interdependence for their behavior (Jacobides et al., 2018). The underlying
assumption in these suggestions is that managers make decisions on ecosystem strategy based on a thorough understanding of not
only what ecosystems are generally like, but also what the speciﬁc ecosystem of their innovation is (going to be) like (Adner, 2006,
2012;Williamson and De Meyer, 2012).
Although the idea that one can attempt to deliberately manipulate an innovation ecosystem is now well-established (Adner, 2012,
2017;Jacobides et al., 2018;Walrave et al., 2018), there is not yet a comprehensive approach that would empower managers in their
eﬀorts to analyze ecosystems across relevant categories and to develop an informed strategy. To address this gap, we develop a
qualitative strategy tool (Jarzabkowski and Kaplan, 2015) for mapping, designing and analyzing (i.e., modeling) innovation eco-
systems within the so-called structuralist tradition (Adner, 2017;Hannah and Eisenhardt, 2017). In doing so, we integrate the views
and implications arising from recent scholarly work on ecosystems (e.g., Adner, 2017;Jacobides et al., 2018) and instrumentalize the
innovation ecosystem concept for both practical and academic use.
The paper proceeds as follows. We ﬁrst distill from the literature on innovation ecosystems the relevant ecosystem design con-
structs and their relationships. Embedding these in a graphical artifact, we propose a visual strategy tool coined the Ecosystem Pie
Model (EPM). We then provide guidelines for modeling ecosystems with the EPM and illustrate how the tool could be used. We
conclude by exploring the beneﬁts of the EPM tool as a boundary object between research and practice.
Modeling ecosystems: the Ecosystem Pie Model tool
Fig. 1 presents the blank version of the Ecosystem Pie Model tool, including (a short description of) all relevant elements. Each
element, or construct, is further explained in Table 1. Notably, in developing a strategy tool for ecosystem modeling, we ﬁrst
identiﬁed the relevant constructs and relationships that would provide an exhaustive and internally consistent base (cf. March and
Smith, 1995) for representing how a real-world or prospective ecosystem functions in terms of value creation and capture. Building
Fig. 1. The Ecosystem Pie Model tool.
M. Talmar, et al. Long Range Planning 53 (2020) 101850
Ecosystem Pie Model (EPM) constructs.
Ecosystem level constructs
EL: 1. Ecosystem's value proposition Within the structuralist approach (Adner, 2017), an ecosystem is characterized by a system-level goal in the form of a
coherent customer-oriented solution (Appleyard and Chesbrough, 2017;Clarysse et al., 2014). We refer to the intended
value arising from this solution as the ecosystem's value proposition (EVP), which represents an overarching oﬀering
by the supply-side agents in the ecosystem (Slater, 1997), corresponding to an (assumed) need and/or a desire of the
end user (Keeney, 1999;Lepak et al., 2007).
EL: 2. User segments Corresponding to the EVP, user segments specify the target market for the value created in the ecosystem. The need for
clear and deﬁned user segments applies to both the ﬁrm and the ecosystem level (Williamson and De Meyer, 2012;
Winter 1984) and as competition in the market place is increasingly taking place between ecosystems rather than
individual ﬁrms (Teece, 2014), the ability to serve speciﬁc groups of users within the boundaries of the EVP can serve
as a competitive advantage for the ecosystem as a whole (Clarysse et al., 2014). Specifying the target audience for the
actor(s) in the ecosystem because: (1) In some ecosystems, users have substantial discretion regarding which speciﬁc
complementary contributions oﬀered by supply-side actors they will make use of (Parker and Van Alstyne, 2005). For
any particular instance of consumption, the overarching solution of the ecosystem is then re-combined, which in turn
assumes some level of resilience on behalf of the supply-side arrangements in the ecosystem (Jacobides et al., 2018);
(2) Value in ecosystems is often co-created at the interface of supply-side actors and demand side actors, and in
extreme cases, a particular entity can take turns in being on either side, or act in both (Parker et al., 2017); (3) Users
can generate transactable value, such as usage data, that other ecosystem actors can use in providing further value
elsewhere in (or outside) the ecosystem. In an ecosystem, value transfer can thus be bi-directional, moving both toward
and away from the user segments (Appleyard and Chesbrough, 2017).
EL: 3. Actors The organizations, institutions, communities, and individuals are the main agents engaging with value creation and
capture in any given ecosystem (Adner and Feiler, 2017;Autio and Thomas, 2014;Gulati et al., 2012). We conceive
‘actors’as a construct representing the legally independent, but economically interdependent entities involved in
performing distinct productive activities within the modular architecture of the ecosystem (Baldwin, 2008;Jacobides
et al., 2018). Meanwhile, the productive contributions made by actors are not equally critical. In ecosystem analysis,
focus should be placed on actors providing such complements that are non-generic from the point of view of the EVP
(Jacobides et al., 2018). This means that of interest in ecosystem modeling are entities whose productive contributions
need to be at least partly tailored to the purpose of the particular innovation ecosystem, assuming investment on behalf
of the contributing actor (Helfat and Lieberman, 2002). Generic complements, as for example parcel services would be
for most ecosystems, can be assumed as interchangeable at market conditions and deserve thus little or no explicit
attention in ecosystem analysis.
Actor level constructs
AL: 1. Resources Resources are the basis of ﬁrm-speciﬁc value creation (Penrose, 1959) and in order to understand the origin of the
value addition by a speciﬁc actor in an ecosystem, one needs to understand the resources of that actor (Davis, 2016).
This does not imply an actor necessarily owns the resources it uses for generating value. Instead, some of the resources
can be obtained from other actors in the ecosystem (Lubik and Garnsey, 2016), for instance via setting up a joint
venture or using shared facilities in a technology park. The ecosystem literature also emphasizes the importance of
resource complementarity for driving value creation at the ecosystem level (Kapoor and Furr, 2015;Koenig, 2012): the
total value creation potential of an ecosystem increases as the result of a more heterogeneous and complementary
ecosystem resource base (Iansiti and Levien, 2004;Teece, 1992), especially if these resources are non-generic
(Jacobides et al., 2018).
AL: 2. Activities Activities are the mechanisms by which an actor generates its productive contribution to the ecosystem. More
speciﬁcally, the basis for creating value as a ﬁrm is the activity system of the ﬁrm, which encompasses how it converts
resources into value addition through a process of coordinating within and/or across resource interactions between
actors (Möller et al., 2005;Zott and Amit, 2010). Activity is an actor-level construct, but its boundary-spanning nature
implies that diﬀerent activities across the ecosystem are related by means of value delivery toward users. Based on the
sequence of necessary activities to accomplish the EVP, actors in the ecosystem are structurally positioned with regard
to each other, and with regard to the user segments (Adner, 2017).
AL: 3. Value addition As an outcome of its activities, each actor contributes to the ecosystem in the form of a productive component (for
which they likely possess a comparative advantage relative to the other actors) (Autio and Thomas, 2014;Rothaermel,
2001). We refer to this individual contribution as an actor's ‘value addition’, which constitutes a module within the
supply-side system accomplishing the EVP (Clarysse et al., 2014;Nambisan and Sawhney, 2011). Furthermore,
following the core-periphery approach developed by Borgatti and Everett (2000), some value additions can in a strict
sense be uniquely complementary (i.e., necessary) to the composition of the EVP (Jacobides et al., 2018), while others
are value enhancing but not a necessary condition for the EVP to be accomplished (Gawer and Cusumano, 2002).
AL: 4. Value capture While the ecosystem creates and delivers end-user value through network interactions, in appropriating value, each
actor embedded in the ecosystem seeks beneﬁts for itself (Teece, 1986). In ecosystem modeling, the ‘value capture’
construct represents how,what kind, and how much value created by the ecosystem is captured by a particular actor.
The opportunity to capture value is a key motivation to join an ecosystem (Lepak et al., 2007;West and Wood, 2013)
and for the actors to commit to the ecosystem, they should perceive their ‘share’as fair (Iansiti and Levien, 2004;
Williamson and De Meyer, 2012). In addition to direct ﬁnancial gains, actors may capture value by leveraging the
ecosystem for growth, reputation, higher eﬃciency or additional resources (e.g., knowledge) (Lepak et al., 2007;
Thomas et al., 2015); as such, a ﬁrm capturing non-monetary value, which for instance can be monetized outside the
particular ecosystem, may still be highly motivated to contribute to that ecosystem.
AL: 5. Dependence Ecosystems are networks that can include actors of diverse proﬁles (Adner and Kapoor, 2016). For some of these actors,
accomplishing the EVP may be of utmost importance, while for others contributing to a particular EVP is just a small
(continued on next page)
M. Talmar, et al. Long Range Planning 53 (2020) 101850
on the insight that the operating logic of any given innovation ecosystem is dependent on the properties of the individual actors as
well as the properties of the ecosystem network (Adner, 2006;Dattée et al., 2018), we distinguished constructs and their relationships
at the ecosystem level (EL) and the actor level (AL).
In its visual form pictured in Fig. 1, the EPM employs elements of actor-based sectors and embedded circles representing speciﬁc
characteristics of each actor. This circular shape has two beneﬁts. It enables the simultaneous detailed representation of ecosystem-
and actor-level properties, and it enables users to capture relationships that transcend the immediate vicinity of any actor in a value
chain. A similar visual structure has also been used to represent other classes of multi-actor value systems (e.g., Bocken et al., 2013;
Bourne and Walker, 2005;Lüftenegger, 2014).
Relationships of constructs
The constructs included in Fig. 1 and detailed in Table 1 interact, both within and between actors (Adner, 2012,2017;Nambisan
and Sawhney, 2011). In Fig. 2, we summarize the intra-actor relationships between the constructs. This allows a modeler to develop
the characterization of actors across relevant constructs, and to test for the consistency of the overall description of the actor as part of
Relationships between constructs on the inter-actor level include the following. First, given the modular nature of the ecosystem,
the actors can combine their value additions to the EVP in diﬀerent ways. They can integrate their value additions in the hands of end
users, for example when the latter draw on the electricity grid to charge their electric vehicles; alternatively, the exchange between
ecosystem actors can serve to integrate their value additions, such as when a battery is incorporated in an electric vehicle, con-
stituting a value chain where some actors are sequenced closer to the end user while others are positioned further from the end user.
As such, an ecosystem can span one or several value chains (Adner and Kapoor, 2010). Second, resources such as intellectual property
from diﬀerent actors can be shared or (re-)combined to enhance the ability of any particular actor(s) to create value (Leten et al.,
Table 1 (continued)
share of their total activities (Adner, 2017). The extent to which the success of the actor is related to that of the
ecosystem represents the ‘dependence’of that actor on the ecosystem.
AL: 6. Risk The notion of actor-speciﬁc risk is directly related to the constructs of dependence and value capture, and indirectly to
all other constructs. For the EVP to materialize, the actors need to achieve a suﬃcient level of agreement, alignment
and commitment about their individual contributions (Koenig, 2012;Walrave et al., 2018;Williamson and De Meyer,
2012). A potential source of risk here is the unwillingness of certain actor(s) to contribute; for example, due to
inadequate incentives such as a low ratio of value capture to the costs borne by an actor (Iansiti and Levien, 2004); or
an actor's low dependence on, and consequently low eﬀort toward, the success of the ecosystem (Adner, 2017).
Potential unwillingness can also arise from the extent to which participation in the ecosystem requires actors to invest
in resources, activities and/or products/service conﬁgurations that are speciﬁc to this particular ecosystem and could
not be redeployed elsewhere (i.e., the fungibility of the resources or activities) (Cennamo et al., 2018). Furthermore, an
actor may be unwilling to contribute to, or even desire to undermine the ecosystem for strategic reasons; for instance,
due to a diﬀerent vision of leadership in the ecosystem, or because the EVP at hand could shift the power balance in the
industry (Iansiti and Levien, 2004). In addition to unwillingness, actor-speciﬁc risk can arise also from the inability of
actor(s) to provide the value addition needed, for example due to staﬃng, technological or legal diﬃculties (Adner,
The overall likelihood that the EVP is achieved can be determined by multiplying the individual likelihoods of each
critical actor to be both willing and able to contribute to the EVP (cf. Adner and Feiler, 2017). Thus, although risk is an
actor-level construct representing the likelihood of a particular actor to fail to contribute its specialized value addition
to the realization of the EVP, the unwillingness or the inability of any actor to contribute would undermine the
prospective performance of the whole ecosystem and thus warrant action from other actors (Adner, 2006;Gulati et al.,
Fig. 2. Intra-actor relationships of constructs.
M. Talmar, et al. Long Range Planning 53 (2020) 101850
2013). Third, the activities of an actor can be boundary-spanning, that is, deliberately combining with the activities of other actors.
For example, the activity of a ﬁrm developing a higher capacity battery needs to be incorporated in the eﬀort by the electric vehicle
manufacturer to develop the technical speciﬁcations of the whole vehicle. Fourth, the value capture of an actor is determined by the
value capture of the other actors. For instance, considering a target price per cup, the pricing strategy for a coﬀee machine and the
pricing strategy of coﬀee capsules (as coming from another producer) are interdependent. Finally, while the risk level of actor(s) is
inﬂuenced by all actor-speciﬁc characteristics (see Fig. 2), the risk of an actor, in turn, inﬂuences the activities of other actors and,
consequently, each other actor's value additions and value capture levels. For example, if one or more producers of critical com-
ponents (are expected to) fail to deliver a speciﬁc level of performance within their module in the ecosystem, the other actors have a
choice regarding whether to allow the performance of the entire system to suﬀer, develop the critical component themselves, or invite
additional parties to join the ecosystem to deliver it.
Illustration of how the EPM is used
The EPM tool, presented in this paper, has been prototyped and improved in a substantial number of iterations, where it has been
used to model more than 260 diﬀerent innovation ecosystems with a wide array of modeler proﬁles and contexts. Extensive
guidelines for modeling ecosystems with the EPM are available as an online appendix (Talmar, 2018). Here, we brieﬂy illustrate the
modeling process in an example that involves a novel process for storing (renewable) power in liquid form, developed at a Dutch
university. The generic nature of this technology made it possible to commercialize the invention in a number of diﬀerent application
areas. However, it was also clear that in all of these possible applications, wide-scale adoption of the technology could only be
achieved with a substantial shift in the activities of incumbent actors. The team developing the technology considered this to be a
major barrier (cf. Adner, 2017;Geels, 2004). Nevertheless, a spin-oﬀfrom the university was created to (try to) bring the technology
to the market in such a way as to increase the chances of its adoption. It was at this point that the team engaged in ecosystem
modeling by using the EPM with a focus on considering multiple ecosystem alternatives for the commercialization pathway of the
technology. Three of these alternatives were positioned within the domain of mobility (i.e., using the technology in city public
transport, in trucks, or in boats) and another one in power storage for use in buildings. Fig. 3 presents the ﬁrst steps in modeling one
of these examples, including how the team used the logic from Fig. 2 to map its own potentially valuable characteristics, as well as a
prospective EVP and a corresponding user segment.
Fig. 3. First step in modeling a potential ecosystem in public transport.
M. Talmar, et al. Long Range Planning 53 (2020) 101850
As Fig. 3 illustrates the basis of EPM modeling is qualitative information concerning each of the constructs. Nonetheless, in some
contexts, one can add useful detail in terms of additional speciﬁcations that are quantitative (e.g., as part of the value capture,
provide the proﬁt function of each actor) or visual in nature (e.g., representing the value additions of each actor by a scheme of its
product). Furthermore, to represent ‘dependence’, the EVP tool uses the grades L (low), M (medium) and H (high) as represented by
the letter and the position of the respective circle on the downstream separation line for each actor. In our example, the technology
development team assumed that, due to limited resources, it would initially enter only into one application area, thus being highly
dependent on achieving that EVP.
Subsequently, the direct chain of adopters between the technology team and the end users was considered, as visualized in Fig. 4.
Here, a minimum of three parties were deemed necessary: a) a chemical equipment manufacturer to produce the necessary reactors
for converting chemical fuel back into electricity; b) a bus manufacturer with prior experience with electric buses; and c) local public
transport operators who would tender buses from the manufacturers and ultimately provide the transportation service. In line with
the notion that an EVP can emerge from the combination of complementary subsystems in the ecosystem (Adner, 2017), modelers
would assign all actors within a direct adoption chain with the same (background) color of yellow. Additionally, to represent the level
of risk arising from the willingness and ability of each actor to contribute, the estimated risk level is translated into generic color
codes (red = high risk; yellow = medium risk; green = low risk) (cf. Adner, 2012). As a result, the technology development team
was conﬁdent about its own module in the system, but the broader adoption chain appeared risky with regard to two actors (see
Fig. 4). As indicated by the red asterisks next to the titles of these actors, both actors would also contribute value additions that are
critical from the point of view of the EVP, thus constituting a major concern for the team.
In the next step, the team modeled the subsystems that complement the main adoption chain. As featured in Fig. 5, these included
the value chain around fuel supply as well as a distinct role for municipalities. These actors were expected to inﬂuence the dynamics
surrounding the ecosystem and therefore the team adjusted some of the previous characterizations in Figs. 3 and 4, while checking
continuously for the internal consistency of each actor's characterization (Fig. 2).
The team conducted similar exercises regarding each of the other three potential applications and subsequently compared and
assessed the four alternatives. Based on the comparison, they decided to target the public transport segment. In this respect, the other
three ecosystems modeled (for trucks, boats, and buildings) were assessed as less viable and riskier, especially in terms of the
potential for upscaling the distribution and use of the technology. Meanwhile, in the public transport segment, municipalities are
setting increasingly stricter requirements for greenhouse gas emissions in tender calls for public transport services, which triggers a
chain of demand for compliant vehicles from the public transport operators to bus manufacturers. While that demand does not
immediately translate into a demand for the team's value proposition per se, it does have the power to eliminate, or at least reduce the
competitiveness of some economically more attractive technological options from competitors; and thus, to increase the likelihood of
adoption of the proposed value proposition.
As the last step toward completing the EPM, its users would mark particular relationships between actors (e.g., the combination of
Fig. 4. Model after considering the direct chain of adopters to reach end users.
M. Talmar, et al. Long Range Planning 53 (2020) 101850
resources, or the zero-sum nature of value capturing opportunities) on the EPM, employing arrows that indicate the direction of the
relationships. There is a large potential number of important relationships, but ‘hiding messy reality is exactly what a good re-
presentation is supposed to do’(Baldwin and Woodard, 2009, p. 34). As such, we recommend limiting the number of explicitly
represented relationships in the EPM to the most relevant and/or the potentially least comprehensible from the point of view of the
general EPM principles outlined earlier. Following these guidelines, Fig. 5 includes relationship arrows that predominantly reﬂect
how certain critical assumptions (e.g., for a certain scale of demand) made by some actors are potentially reinforced by other actors in
In sum, the development team in this example analyzed the commercialization pathways for its new technology by modeling four
ecosystem options. This allowed the team to address the following aspects of their innovation strategy for each alternative: (1) What
kind of changes in the activity systems of external actors would adopting the technology assume? (2) How are these actors likely to
react to the EVP concept? (3) How to alleviate the risks associated both with the adoption chain actors and the complementary
service providers? (4) Which modules of an ecosystem could the team internalize and which would better be left to others to develop
and maintain? (5) What would be a suitable mechanism for the technology development team to capture value (e.g., licensing,
becoming an OEM) so as to encourage external parties to adopt the technology and accomplish the respective EVP? (6) What are the
implications of that choice to the resource requirements of the development team? And ultimately: (7) which of the alternative
ecosystems will the development team pursue in real life. As a result of the analysis, the team proceeded with pursuing the public
transportation alternative by undertaking lobbying activities to municipalities and addressing the obstacles and challenges identiﬁed
in the ecosystem for the public transport segment. The team members have continued reﬂecting upon their progress with the use of
Discussion and conclusion
In carrying scientiﬁc knowledge to management practice, and in return practical wisdom to science, boundary objects are needed
at the interface of scholarship and practice (Eppler and Platts, 2009;Romme, 2016;Spee and Jarzabkowski, 2009). In this research,
we have proposed such a boundary object in the form of a strategy tool for modeling ecosystems as conceptualized in the structuralist
approach (Adner, 2017). The tool serves to consider and integrate relevant ecosystem properties such as interdependency (Gulati
et al., 2012), complementarities (Jacobides et al., 2018) and alignment risks (Adner, 2017).
As such, the EPM tool empowers managers to make informed decisions about their innovation strategy, by helping them make
Fig. 5. Completed EPM for public transportation application for the technology under consideration.
M. Talmar, et al. Long Range Planning 53 (2020) 101850
sense of ecosystems as complex entities along relevant ‘thinking paths’(Wright et al., 2013). This process-oriented approach helps
manifest several traits valued by managers in strategy tools (Jarzabkowski and Kaplan, 2015;Wright et al., 2013), including a step-
by-step systematic analysis of the situation, considering diﬀerent viewpoints (of diﬀerent actors), exploring linkages between inter-
related elements of the system, focusing on the critical factors that are likely to determine the success of an ecosystem, as well as
potentially identifying needs for additional data.
The illustrative case in the previous section together with many other examples (e.g., Plompidou, 2017;Red Eléctrica de España,
2018;Salminen, 2017) demonstrate that the EPM is highly instrumental in developing strategic insight across several contexts. It
delivers insights in a prospective outlook, by considering future ecosystems with novel EVPs; but it can also be used retrospectively,
to reﬂect upon an existing ecosystem and then redesign its operational structure (cf. Jarzabkowski and Kaplan, 2015). In addition, our
experiences suggest the EPM can deliver value to ecosystem insiders, but also to external experts that seek to map and analyze a
particular ecosystem for purposes such as evaluating investment opportunities or developing research funding policy. As such, the
EPM can successfully facilitate highly diﬀerent modeler proﬁles and modeling purposes. Furthermore, it can guide the strategy
process of a single actor, and create inclusivity in developing strategy across actors (Hautz et al., 2017). In the latter, several
organizations would engage in ecosystem design together, using the EPM process to explore opportunities for common innovation
eﬀorts, to guide potentially uncomfortable conversations on topics such as dependence and risk, and/or to resolve misalignments.
The second major area of contribution for the framework enclosed is academic research. In ecosystem research, a core topic of
inquiry is ecosystem strategy: how do ﬁrms change their behavior or attempt to assert inﬂuence on the behavior of others, based on
the analysis of their ecosystem setting (Adner, 2017;Dattée et al., 2018;Davis, 2016;Hannah and Eisenhardt, 2017). The proposed
EPM framework for ecosystem modeling empowers researchers to study this topic in at least four novel ways. First, the main
challenges in developing ecosystem strategy arise from the context of the ecosystem, as represented by the interplay between its
structural elements. The EPM framework serves to explicitly consider the structural elements of a particular ecosystem (Adner, 2017),
thus setting the stage for contextualizing scholarly inquiries into strategic decision making in and around a particular ecosystem.
Second, scholars can use the visual representation oﬀered by the EPM to conceive the crafting of ecosystem strategies as design
interventions. Third, researchers may use the EPM framework for representing diﬀerent innovation ecosystems, which enables
comparability of research input/output. Fourth, by presenting research outcomes in the form of a synthesized visual form and
detailed strategic implications, scholars can eﬀectively create oﬀerings to organizations in exchange for access to data collection
We are grateful to the several hundred individuals and organizations who have implemented and provided feedback to the EPM,
and to Joan van Aken for his review of an earlier version of this manuscript. The study was performed as part of the Erasmus Mundus
Joint Doctorate SELECT+ 'Environmental Pathways for Sustainable Energy Systems'. This project has been funded by the Education,
Audiovisual and Culture Executive Agency (EACEA) (Nr 2012-0034) of the European Commission. The publication reﬂects the views
only of the authors and the Commission cannot be held responsible for any use which may be made of the information contained
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Dr. Madis Talmar, DSc is an Assistant Professor of Entrepreneurship & Innovation at Eindhoven University of Technology and a consultant for innovation programs in
energy corporations. His research interests include innovation ecosystems, design science, and organization design, with focus on (innovation) management in the ﬁeld
of energy. He has published over 35 articles in practitioner-oriented management journals and consulted organizations of both public and private origin. Academically,
he has published in the journal Technological Forecasting and Social Change.
Dr. Bob Walrave is an Assistant Professor of Modeling Innovation Systems at the Eindhoven University of Technology. His research interests are centered around
strategic decision making in dynamically complex situations in the context of innovation management and entrepreneurship. His work has been published in journals
such as Journal of Management Studies, Research Policy, Technological Forecasting and Social Change, R&D Management,and Industrial and Corporate Change.
Dr. Ksenia Podoynitsyna is an Associate Professor of Data-Driven Entrepreneurship at JADS, the Joint Graduate School of Tilburg University and Eindhoven
University of Technology. Her research focuses on business model and ecosystem innovations triggered by sustainability transitions and big data. Ksenia has published
in the Journal of Business Venturing, Journal of Product Innovation Management,Entrepreneurship Theory and Practice, and Technological Forecasting & Social Change among
Prof. Jan Holmström is a Professor in Operations Management. He has been, since 1999, at the Department of Industrial Engineering and Management, Aalto
University School of Science and Technology. He is one of the ﬁrst researchers to introduce design science research to operations management, focusing on technology-
enabled innovation in operations management. He has a background as a systems analyst with Unilever and technology consultant with McKinsey & Company. He is
the author of more than 80 peer-reviewed journal publications and the co-author of a book in the ﬁeld of supply chain management and technology-driven man-
Prof. Dr. A. Georges L. Romme is a Professor of Entrepreneurship & Innovation at Eindhoven University of Technology. He holds an MSc in economics from Tilburg
University and a PhD in management studies from Maastricht University. Before moving to TU/e, Georges held academic positions at Maastricht University and
Tilburg University. He has published in Organization Science, Strategic Management Journal, Industrial and Corporate Change, Journal of Management Studies, Journal of
Product Innovation Management, and many other journals. He currently serves on the editorial boards of Designs and Journal of Organization Design.
M. Talmar, et al. Long Range Planning 53 (2020) 101850