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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 distil 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.
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Long Range Planning
journal homepage: www.elsevier.com/locate/lrp
Mapping, analyzing and designing innovation ecosystems: The
Ecosystem Pie Model
Madis Talmar
a,d,
, Bob Walrave
a
, Ksenia S. Podoynitsyna
a,b
, Jan Holmström
c
,
A. Georges L. Romme
a
a
Eindhoven University of Technology, Department of Industrial Engineering and Innovation Sciences, P.O. Box 513, 5600 MB, Eindhoven, the
Netherlands
b
JADS, The Joint Graduate School of Tilburg University and Eindhoven University of Technology, Sint Janssingel 92, 5211 DA, 's-Hertogenbosch, the
Netherlands
c
Aalto University, School of Science, Department of Industrial Engineering and Management, P.O. Box 15500, FI-00076, Aalto, Finland
d
EIT InnoEnergy Ph.D. School Fellow, KIC InnoEnergy SE, Eindhoven, the Netherlands
ARTICLE INFO
Keywords:
Innovation ecosystem
Ecosystem modeling
Strategy tool
Strategy process
Innovation strategy
ABSTRACT
To achieve a complex value proposition, innovating rms often need to rely on other actors in
their innovation ecosystem. This raises many new challenges for the managers of these rms.
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 aordances of the
EPM tool as a boundary object between research and practice.
Introduction
In a world of increasingly specialized organizations, a single rm typically does not possess the resources to develop and com-
mercialize a complex value proposition from start to nish (Appleyard and Chesbrough, 2017;Kapoor and Furr, 2015). Therefore,
rms 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 dierent actors are combined (Hannah and Eisenhardt, 2017). On the one hand, the
interdependency in ecosystem relationships connes rms; 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, rms can
leverage ecosystem relationships for higher value creation by exploiting the synergies and network eects 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
https://doi.org/10.1016/j.lrp.2018.09.002
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: m.talmar@tue.nl (M. Talmar).
Long Range Planning 53 (2020) 101850
Available online 11 October 2018
0024-6301/ © 2018 The Authors. Published by Elsevier Ltd.
T
roles of actors within the value structure (Dedehayir et al., 2018); the (potential) network eects 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 dierent actors
(Jacobides et al., 2018); the inuence 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-oof 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 specic 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
eorts 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 rst 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 benets 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 rst
identied 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
2
Table 1
Ecosystem Pie Model (EPM) constructs.
Construct Description
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 oering
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 dened user segments applies to both the rm 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 rms (Teece, 2014), the ability to serve specic 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
EVP in terms of user segmentation is thus an ecosystem-level construct. At the same time, users also constitute separate
actor(s) in the ecosystem because: (1) In some ecosystems, users have substantial discretion regarding which specic
complementary contributions oered 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
actorsas 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 rm-specic value creation (Penrose, 1959) and in order to understand the origin of the
value addition by a specic 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
specically, the basis for creating value as a rm is the activity system of the rm, 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 dierent 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 benets 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 shareas fair (Iansiti and Levien, 2004;
Williamson and De Meyer, 2012). In addition to direct nancial gains, actors may capture value by leveraging the
ecosystem for growth, reputation, higher eciency or additional resources (e.g., knowledge) (Lepak et al., 2007;
Thomas et al., 2015); as such, a rm 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 proles (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
3
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 specic
characteristics of each actor. This circular shape has two benets. 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
the ecosystem.
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 dierent 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 dierent 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)
Construct Description
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 dependenceof that actor on the ecosystem.
AL: 6. Risk The notion of actor-specic 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 sucient 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 eort 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 congurations that are specic 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 dierent 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-specic risk can arise also from the inability of
actor(s) to provide the value addition needed, for example due to stang, technological or legal diculties (Adner,
2006).
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.,
2012).
Fig. 2. Intra-actor relationships of constructs.
M. Talmar, et al. Long Range Planning 53 (2020) 101850
4
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 rm developing a higher capacity battery needs to be incorporated in the eort by the electric vehicle
manufacturer to develop the technical specications 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 coee machine and the
pricing strategy of coee capsules (as coming from another producer) are interdependent. Finally, while the risk level of actor(s) is
inuenced by all actor-specic characteristics (see Fig. 2), the risk of an actor, in turn, inuences 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 specic 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 suer, 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 dierent innovation ecosystems with a wide array of modeler proles and contexts. Extensive
guidelines for modeling ecosystems with the EPM are available as an online appendix (Talmar, 2018). Here, we briey 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 dierent 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-ofrom 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 rst 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
5
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 specications that are quantitative (e.g., as part of the value capture,
provide the prot 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 condent 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 inuence 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
6
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 reect
how certain critical assumptions (e.g., for a certain scale of demand) made by some actors are potentially reinforced by other actors in
the ecosystem.
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 identied
in the ecosystem for the public transport segment. The team members have continued reecting upon their progress with the use of
the EPM.
Discussion and conclusion
In carrying scientic 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
7
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 dierent viewpoints (of dierent 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 reect 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 dierent modeler proles 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
eorts, 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 rms change their behavior or attempt to assert inuence 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 oered by the EPM to conceive the crafting of ecosystem strategies as design
interventions. Third, researchers may use the EPM framework for representing dierent 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 eectively create oerings to organizations in exchange for access to data collection
opportunities.
Acknowledgments
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 reects the views
only of the authors and the Commission cannot be held responsible for any use which may be made of the information contained
therein.
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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 eld
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
others.
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 rst 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 eld of supply chain management and technology-driven man-
agement innovation.
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
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... Design science (DS) has traction within the entrepreneurship field. An increasing number of scholars publish DS on entrepreneurship topics within top journals (e.g., Berglund, Bousfiha, & Mansoori, 2020;Berglund, Dimov, & Wennberg, 2018;Campos et al., 2017;Dimov, 2016;Sarasvathy, 2003;Talmar, Walrave, Podoynitsyna, Holmström, & Romme, 2020). We expect that many entrepreneurship scholars will have interest in the PDW. ...
... We expect that researcher from other divisions-especially TIM, OMT and ONE-are also interested in this PDW. First, many Technology and Innovation Management (TIM) scholars already seize the potential of DS (e.g., Pascal, Thomas, & Romme, 2013;Talmar et al., 2020;van Burg, Romme, Gilsing, & Reymen, 2008). This PDW will allow to further share ideas and learn from each other on different methodical approaches to DS. ...
... The promise is that entrepreneurship scholars can help address the grand challenges of our time (George et al., 2016) not only to understand them, but also to design solutions to tackle them . We believe that it is this promise of design science that accounts for so much traction among entrepreneurship scholars (Berglund et al., 2020;Berglund et al., 2018;Dimov, 2016;Sarasvathy, 2003;Talmar et al., 2020;Van Burg & Romme, 2014;Zhang & Van Burg, 2020). ...
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This Professional Development Workshop (PDW) on 'Design Science in Entrepreneurship and Innovation' gives scholars and practitioners an opportunity to learn how to conduct design science in entrepreneurship and innovation. Building on the successful PDW last year, this year's PDW aims at digging deeper into methodical aspects of the design science approach. The aim is to share ideas and discuss different methodologies, the use of exploratory and creative methods for designing, and best practices for working in transdisciplinary design science teams. The PDW will be an interactive and engaging hybrid format with two parts. Part 1 will be an interactive presenter panel with experienced design science scholars from the fields of entrepreneurship, technology and innovation, and information systems. Part 2 will be a market-of-makers format to initiate discussions on burning questions, and to initiate novel design science research projects. Overall, this innovative PDW aims at getting participants inspired, learning from others, and building community.
... Typically, the new value is either generated by reducing friction in existing solutions, lowering maintenance costs, or creating new product bundles or industries (Williamson & De Meyer, 2020). Therefore, several authors highlight the essential role of understanding the customers' daily problems, and accordingly, solutions are usually based on people's basic needs (e.g., Jacobides et al., 2019;Talmar et al., 2020). For example, Kawohl et al. (2020) emphasize the life areas ecosystems emerge around, e.g., Mobility, Health, Education, and Living. ...
... Based on their activities, the actors are placed in the ecosystem and fill out standardized roles (Talmar et al., 2020). A role is defined as a "characteristic set of behaviours or activities undertaken by ecosystem actors" (Dedehayir et al., 2018, p. 18). ...
... Several authors notice that the intent should not be to maximize the own profits but to increase the total value captured by the various ecosystem participants (Dondofema & Grobbelaar, 2019;Williamson & De Meyer, 2012). Value capturing and sharing should reflect the actors' contribution and involve either monetary remuneration or data, which can be monetized at a subsequent stage (Talmar et al., 2020). Nambisan and Sawhney (2011) assume that firms with the most valuable assets may not participate in the ecosystem if the apportionment lacks clarity. ...
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The insurance industry faces economic, socio-economic, and technological changes, forcing companies to put aside past experiences and reinvent themselves. In this context, innovation ecosystems, offering complex customer value proposition through the cooperation of different multilateral, not vertically integrated partners, find increasing attention in industry journals and academic literature. However, this is not yet reflected in the German insurance markets’ development. Against this backdrop, this thesis objective is to identify the reasons for the slow development and thus the challenges for insurance companies in establishing innovation ecosystems. The research question was divided into two sequential sub- questions. First, the current ecosystem strategies being pursued in the industry are identified to, secondly, clarify what organizational challenges arise from pursuing these strategies. Given the absence of a holistic theory in the field of interest and that no study has yet addressed the challenges of establishing innovation ecosystems, a comprehensive literature review is conducted to define innovation ecosystems, derive characteristics, and identify the challenges. These challenges are then specified to insurance companies with the help of eleven semi-structured expert interviews and are evaluated with Mayrings’ qualitative content analysis. The analysis introduces a taxonomy for the strategies insurance companies are currently approaching to establish innovation ecosystems: setting up a Customer Service Portal and acting as Strategic Partners / Investors. Furthermore, the discus- sion implies that both strategies are not entirely comparable to establishing an ecosystem, for which the reasons might be found in seven insurance-specific organizational challenges. The results recommend that the challenges mainly concern the processes around the customer, the development of innovative business models, partner orchestration and the IT architecture.
... Therefore, resource dependency sheds light on the positive dynamics of both GVCs and local ecosystems (Adner, 2006;Mercan and Goktas, 2011;Fukuda and Watanabe, 2012). Based on related research, latecomer firms participate in GVCs and interact with actors around value chains (i.e., global and local) that directly contribute to and support the value-creation activities of latecomers (Talmar et al., 2020). A local ecosystem can connect myriad actors to access knowledge, aside from the resources offered by GVCs. ...
... The results provide supportive evidence for these insights. Latecomer firms who participate in GVCs and interact with the local ecosystem can contribute to and support the value-creation activities of latecomers (Talmar et al., 2020). ...
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... It is very likely that deeptech ventures are the most risky ones in this population, and therefore have failure rates above 90 percent. As such, deeptech venturing is extremely challenging, because these ventures:  develop products and systems with a very high technological complexity, arising from the combination of extremely complex hardware and software [5];  have a long time-to-market of usually at least 3 to 5 years-but often much longerand thus require major investments, in terms of both financial and human resources: in terms of financial resources, a typical deeptech venture needs EUR 10M to 20M in the first (series A) investment round, and hundreds of millions in the second investment round [27];  often require extensive innovation ecosystems (or clusters of collaborating firms) around the deeptech value proposition of the focal venture, in which various suppliers also have to invest in developing new components and services [28][29][30]. ...
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The Brainport-Eindhoven region has developed into a leading location for deeptech entrepreneurship in Europe. Against all odds, it has transformed itself from a region that heavily depended on the multinational company Philips, into a diverse and fast-growing deeptech ecosystem. While this success has not gone unnoticed, there is not yet a clear account of how and why the Eindhoven region emerged as a global hotspot for deeptech innovation and entrepreneurship. Moreover, such an account might provide an exemplary model of a collaborative ecosystem, one that provides an alternative to the “winner-takes-all” entrepreneurial culture of Silicon Valley. This essay explores the performance of the Eindhoven region in terms of three structural conditions. First, the focus on deeptech R&D and entrepreneurship appears to be deeply rooted in the region’s history as well as strong competencies in systems engineering, design thinking, and multidisciplinary collaboration. Second, a collaborative approach to regional policy gives industrial, academic, and governmental actors an equivalent position in its “triple helix” governance. Finally, the Eindhoven region benefits from a systemic approach toward co-locating R&D and entrepreneurial activities on five campuses. Overall, the huge complexity of deeptech systems and products apparently requires a truly collaborative approach at all levels of the entrepreneurial ecosystem.
... As the actors in the ecosystem depend on each other for developing the ecosystem's value proposition (EPV), alignment between these actors is essential [11], [21], [40]- [42]. ...
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The multidimensional nature of urban challenges requires firms to develop smart solutions with interdependent actors, often referred to as innovation ecosystems. However, firms might find it challenging to define a viable value proposition for a smart city offering at the outset, as value propositions depend on other actors' complementary technologies, an uncertain yet emerging demand, and the multifaceted nature of urban challenges. This article deploys effectuation theory from entrepreneurship research to enable focal firms to create value by managing uncertainties and available means at multiple levels of emerging ecosystems. We performed a case study within a large, incumbent corporation operating as the focal firm within a smart city context to gain insight into the value creation efforts and the challenges inherent in this process. We iteratively synthesized the case study insights with extant research on effectuation into a multilevel value creation framework in smart city ecosystems. The framework highlights how awareness of the range of alternative futures based on (potential) ecosystem-level means can enable a focal firm to develop actionable value propositions and identify relevant stakeholders within the emerging network of the focal firm. Our findings show that a holistic overview of dynamics in ecosystems can support firms in creating value in nascent ecosystems.
... Why would an organisation engage in inter-firm partnerships using an innovation ecosystem structure to develop a new product or service? While many different research streams and scholars have addressed this question, two main themes arise from the literature: the necessity of inter-firm collaboration to create specific types of innovations (Adner, 2017;Gomes et al., 2018a;Hannah and Eisenhardt, 2018;Talmar et al., 2020) and the use of common technological platforms to access highly contextual knowledge and deliver coherent value propositions with a global reach (Ketonen-Oksi and Valkokari, 2019;Nambisan, 2018;Nambisan et al., 2019;Valkokari et al., 2017). ...
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Firms face the challenge of creating and managing global innovation ecosystems (GIEs) to develop radical innovation. In such a situation, focal firms and their partners may deal with global and local uncertainties requiring unique managerial approaches and strategies. Consequently, uncertainty management becomes central to explaining why some ecosystems successfully develop more radical innovations while others fail in a global landscape. Prior research lacks insight into how focal firms orchestrate GIEs to cope with uncertainty. By studying four focal firms and their ecosystems, we propose a new framework for uncertainty management—global uncertainty management in GIEs framework. This study provides theoretical insights into the focal firms' governance logic to manage local and global uncertainties and the GIE strategies they employ. We hope that this study will reinvigorate the literature on uncertainty management and ecosystem management.
... Business ecosystem modeling obtained also attention in the Internet of Things (IoT) research domain, addressing ecosystem design methods (Uchihira et al., 2016) and presenting a framework to fully understand the complexity of IoT business models. Further, design frameworks (e.g., Tsujimoto et al., 2018;Garmann-Johnsen et al., 2021) or ecosystem mapping (Talmar et al., 2020) were introduced. However, to the best of our knowledge, no specific BMML currently exists suitable for business ecosystems. ...
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Digital technologies have led to the emergence of new ways of creating value and, by enabling business ecosystems, to a change in the way businesses are organized. However, due to their non-hierarchical and complementary nature, business ecosystems are not easy to create or sustain. To support the systematic design and analysis of business ecosystems, business modeling has emerged as a valuable tool. Our structured review of the literature on business model modeling languages has found 45 potentially relevant publications. Although a preliminary analysis shows that existing modeling languages only provide limited support for business ecosystems, they can provide a useful conceptual basis to enable further research. This research-in-progress paper outlines first steps taken to identify recently developed business model modeling languages and to synthesize and organize the knowledge dispersed across disciplines. Thus, the future results of study are expected to contribute to research on business model collaboration and business ecosystems.
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Purpose This study aims to examine the impacts of the novelty-centered business model design (NBMD) and efficiency-centered business model design (EBMD) on mass customization capability (MCC), as well as the mediating role of supply chain integration. Design/methodology/approach Using survey data from 277 Chinese manufacturing firms, we test the hypothesized relationships by conducting structural equation modeling. Findings The results indicate that both NBMD and EBMD have significantly positive impacts on product-oriented MCC and service-oriented MCC. In addition, three dimensions of supply chain integration play different mediating roles in the relationship between BMD and MCC. Specifically, relational integration partially mediates the impacts of NBMD and EBMD on service-oriented MCC, information integration partially mediates the impact of NBMD on product-oriented MCC and service-oriented MCC and operational integration partially mediates the impact of NBMD and EBMD on product-oriented MCC. Originality/value This study opens the “black box” in the relationship between business model design and MCC, which offers insights on the complex process of supply chain integration and considers business ecosystem for operational performance.
Chapter
Entrepreneurship has gained considerable attention in the business field due to its substantial benefits to national economies. Schumpeter (1942) defined “entrepreneurship” as more than just starting a new business, as it involves introducing revolutionary changes in business methods and practices. Exploration, amalgamation and utilisation of new technologies have become one of the key challenges, but also of opportunities for entrepreneurship, and no industry is considered to be unaffected by its impacts. Further, the Covid-19 global pandemic has taken a huge toll on lives and economies globally, leading the business world into faster technological transformation primarily through business digitalisation. These challenging transformations call for problem solving through mindful innovation and fast responses, which tap into what has been construed as the essence of entrepreneurs. However, from the emergent lessons of the pandemic, it is becoming clear that a consideration of the ethical and sustainable aspects of entrepreneurial activities is essential, when pursuing financial success by tapping into the potential of ever-increasing technology developments. Hence, the need for technoentrepreneurial education taking the above into account is increasingly acknowledged among higher education authorities worldwide, and by governments who have a key responsibility for investment in entrepreneurship education programmes. Ultimately, it is argued that entrepreneurship empowered by technology has the ability to actively contribute to the redevelopment and redeployment of societies and economies.
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Research Summary The recent surge of interest in “ecosystems” in strategy research and practice has mainly focused on what ecosystems are and how they operate. We complement this literature by considering when and why ecosystems emerge, and what makes them distinct from other governance forms. We argue that modularity enables ecosystem emergence, as it allows a set of distinct yet interdependent organizations to coordinate without full hierarchical fiat. We show how ecosystems address multilateral dependences based on various types of complementarities ‐ supermodular or unique, unidirectional or bidirectional, which determine the ecosystem's value‐add. We argue that at the core of ecosystems lie non‐generic complementarities, and the creation of sets of roles that face similar rules. We conclude with implications for mainstream strategy and suggestions for future research. Managerial summary We consider what makes ecosystems different from other business constellations, including markets, alliances or hierarchically managed supply chains. Ecosystems, we posit, are interacting organizations, enabled by modularity, not hierarchically managed, bound together by the non‐redeployability of their collective investment elsewhere. Ecosystems add value as they allow managers to coordinate their multilateral dependence through sets of roles that face similar rules, thus obviating the need to enter into customized contractual agreements with each partner. We explain how different types of complementarities (unique or supermodular, generic or specific, uni‐ or bi‐directional) shape ecosystems, and offer a “theory of ecosystems” that can explain what they are, when they emerge and why alignment occurs. Finally, we outline the critical factors affecting ecosystem emergence, evolution, and success ‐‐ or failure.
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Innovation ecosystems are increasingly regarded as important vehicles to create and capture value from complex value propositions. While current literature assumes these value propositions can be known ex-ante and an appropriate ecosystem design derived from them, we focus instead on generative technological innovations that enable an unbounded range of potential value propositions, hence offering no clear guidance to firms. To illustrate our arguments, we inductively study two organizations, each attempting to create two novel ecosystems around new technological enablers deep in their industry architecture. We highlight how ecosystem creation in such conditions is a systemic process driven by coupled feedback loops, which organizations must try to control dynamically: firms first make the switch to creating the ecosystem following an external pull to narrow down the range of potential applications; then need to learn to keep up with ecosystem dynamics by roadmapping and preempting, while simultaneously enacting resonance. Dynamic control further entails counteracting the drifting away of the nascent ecosystem from the firm's idea of future value creation and the sliding of its intended control points for value capture. Our findings shed new light on strategy and control in emerging ecosystems, and provide guidance to managers on playing the ecosystem game.
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Path-breaking innovations are increasingly developed and commercialized by networks of co-creating actors, called innovation ecosystems. Previous work in this area demonstrates that the ‘internal’ alignment of actors is critical to value creation in the innovation ecosystem. However, the literature has largely overlooked that the success of an innovation ecosystem also depends on its ‘external’ viability, determined by the broader socio-technical environment. That is, path-breaking innovations inherently challenge the prevailing socio-technical regime in a domain (e.g., established rules, artifacts and habits) that tends to be resistant to change. Overcoming this resistance is a major challenge for ventures pioneering path-breaking innovations. The paper contributes to the literature on innovation ecosystems by explicitly considering the socio-technical viability of the innovation ecosystem around a path-breaking innovation. In particular, we theorize about the objects of manipulation in an innovation ecosystem and discuss the strategies that a focal venture, orchestrating the innovation ecosystem, can employ in manipulating these objects so as to increase the socio-technical viability of the ecosystem. We arrive at a multi-level perspective on innovation ecosystem development that integrates internal alignment and external viability and informs a research agenda for future studies in this field.
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We explore how decision makers perceive and assess the level of risk in interdependent settings. In a series of five experiments, we examine how individuals set expectations for their own project investments when their success is contingent on the success of multiple, independent partners.We find that individuals are subjectively more confident and optimistic in an interdependent venture when its chances of success are presented as separate probabilities for each component and that this optimism is exacerbated by a greater number of critical partners, leading to (1) the inflation of project valuations, (2) the addition of excessive partners to a project, and (3) overinvestment of effort in the development of one's own component within an interdependent venture. We examine these dynamics in settings of risky choice (with exogenously given probabilities) and in an economic coordination game (with the ambiguity of agency and strategic risk).We conduct our study with awide range of participant samples ranging from undergraduates to senior executives. Collectively, our findings hold important implications for the ways in which individuals, organizations, and policymakers should approach and assess their innovation choices in ecosystem settings.
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Responding to increasing practitioner and academic interest in Open Strategy, this article builds on recent theoretical and empirical studies in order to advance research in the following ways. We begin by developing a definition of Open Strategy that emphasizes variation along the two dimensions of transparency and inclusion, as well as the dilemmas and dynamics inherent in its practices. We identify five dilemmas in particular: those of process, commitment, disclosure, empowerment and escalation. We continue by exploring key dynamics in Open Strategy, including both movements along the dimensions of transparency and inclusion, and movements between the two dimensions. Respecting the acute dilemmas of Open Strategy, we allow in each case for movement away from greater openness. The article concludes by proposing an agenda for future research on Open Strategy.