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Innovation ecosystems in management: An organizing typology



The concept of an ‘ecosystem’ is increasingly used in management and business to describe collectives of heterogeneous, yet complementary organizations who jointly create some kind of system-level output, analogous to an ‘ecosystem service’ delivered by natural ecosystems, and one that extends beyond the outputs and activities of any individual participant of the ecosystem. Due to its attractiveness and elasticity, the ecosystem concept has been applied to a wide range of phenomena by a variety of scholarly perspectives and under varying monikers such as ‘innovation ecosystems’, ‘business ecosystems’, ‘technology ecosystems’, ‘platform ecosystems’, ‘entrepreneurial ecosystems’, and ‘knowledge ecosystems’. This conceptual and application heterogeneity has contributed to conceptual and terminological confusion, which threatens to undermine the utility of the concept in supporting cumulative insight. In this article we seek to re-introduce some order into this conceptual heterogeneity by reviewing how the ecosystem concept has been applied to variably overlapping phenomena and by high-lighting key terminological and conceptual inconsistencies and their sources. We find that conceptual inconsistency in the ecosystem terminology relates to two key dimensions: the ‘unit’ of analysis and the type of ‘ecosystem service’ – i.e., the ecosystem output collectively generated. We then argue that although there is considerable heterogeneity in application, the concept nevertheless offers promise to support insights that are distinctive relative to other concepts that describe collectives of organizations, such as those of ‘industry’, ‘supply chain’, ‘cluster’, and ‘network’. We also find that despite extant proliferation, the concept nevertheless describes collectives that are distinctive in that they uniquely combine participant heterogeneity, coherence of ecosystem outputs, participant inter-dependence, and non-hierarchical governance. Based on our identified dimensions of conceptual heterogeneity, we offer a typology of the different ecosystem concepts, thereby helping re-organize this proliferating domain. The typology is based upon three distinct ecosystem outputs—ecosystem-level value offering to a defined audience, collective generation of business model innovation, and collective generation of research-based knowledge—and three research emphases that resonate with alternative ‘units’ of analysis—community dynamics, output co-generation, and interdependence management. Together, these al-low us to clearly differentiate between the concepts of innovation ecosystems, business ecosystems, platform ecosystems, technology ecosystems, entrepreneurial ecosystems, and knowledge ecosystems. Based on the three distinct types of ecosystem outputs, our typology identifies three major types of ecosystems: innovation ecosystems, entrepreneurial ecosystems, and knowledge ecosystems. Under the rubric of ‘innovation ecosystems’, we further distinguish between business ecosystems, modular ecosystems, and platform ecosystems. We conclude by considering innovation ecosystem dynamics, highlighting the important role of digitalization, and reviewing the implications of our model for ecosystem emergence, competition, coevolution, and resilience.
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Llewellyn D W Thomas
LaSalle Universitat Ramon Llull
Erkko Autio
Imperial College Business School
February 1, 2020
Please cite as:
Thomas, L. D. W., and E. Autio (2020), “Innovation ecosystems in management: An organiz-
ing typology”, In Oxford Encyclopedia of Business and Management. Oxford University
Innovation Ecosystems
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The concept of an ‘ecosystem’ is increasingly used in management and business to describe
collectives of heterogeneous, yet complementary organizations who jointly create some kind of
system-level output, analogous to an ‘ecosystem service’ delivered by natural ecosystems, and one
that extends beyond the outputs and activities of any individual participant of the ecosystem. Due to
its attractiveness and elasticity, the ecosystem concept has been applied to a wide range of phenomena
by a variety of scholarly perspectives and under varying monikers such as ‘innovation ecosystems’,
business ecosystems, technology ecosystems, platform ecosystems, entrepreneurial ecosystems,
and knowledge ecosystems’. This conceptual and application heterogeneity has contributed to con-
ceptual and terminological confusion, which threatens to undermine the utility of the concept in sup-
porting cumulative insight.
In this article we seek to re-introduce some order into this conceptual heterogeneity by review-
ing how the ecosystem concept has been applied to variably overlapping phenomena and by highlight-
ing key terminological and conceptual inconsistencies and their sources. We find that conceptual in-
consistency in the ecosystem terminology relates to two key dimensions: the ‘unit’ of analysis and the
type of ‘ecosystem service’ – i.e., the ecosystem output collectively generated. We then argue that alt-
hough there is considerable heterogeneity in application, the concept nevertheless offers promise to
support insights that are distinctive relative to other concepts that describe collectives of organiza-
tions, such as those of ‘industry’, ‘supply chain’, ‘cluster’, and ‘network’. We also find that despite
extant proliferation, the concept nevertheless describes collectives that are distinctive in that they
uniquely combine participant heterogeneity, coherence of ecosystem outputs, participant interdepend-
ence, and non-hierarchical governance.
Based on our identified dimensions of conceptual heterogeneity, we offer a typology of the dif-
ferent ecosystem concepts, thereby helping re-organize this proliferating domain. The typology is
based upon three distinct ecosystem outputsecosystem-level value offering to a defined audience,
collective generation of business model innovation, and collective generation of research-based
knowledgeand three research emphases that resonate with alternative ‘units’ of analysis—commu-
nity dynamics, output co-generation, and interdependence management. Together, these allow us to
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clearly differentiate between the concepts of innovation ecosystems, business ecosystems, platform
ecosystems, technology ecosystems, entrepreneurial ecosystems, and knowledge ecosystems. Based
on the three distinct types of ecosystem outputs, our typology identifies three major types of ecosys-
tems: innovation ecosystems, entrepreneurial ecosystems, and knowledge ecosystems. Under the ru-
bric of innovation ecosystems’, we further distinguish between business ecosystems, modular ecosys-
tems, and platform ecosystems. We conclude by considering innovation ecosystem dynamics, high-
lighting the important role of digitalization, and reviewing the implications of our model for ecosys-
tem emergence, competition, coevolution, and resilience.
Key words: ecosystem; innovation ecosystem; business ecosystem; platform ecosystem; tech-
nology ecosystem; entrepreneurial ecosystem; knowledge ecosystem
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In management, the concept of an ‘ecosystem’ provides an attractive metaphor to describe
collectives of heterogeneous, yet complementary, organizational actors who collectively generate
some kind of ecosystem-level output (Seppelt, Dormann, Eppink, Lautenbach, & Schmidt, 2011), and
one that extends beyond the outputs and activities of any individual participant of the ecosystem
(Adner, 2017; Autio, Nambisan, Thomas, & Wright, 2018; Järvi, Almpanopoulou, & Ritala, 2018).
Ecosystems are distinguished from most other types of organizational collectives (e.g., supply chains,
clusters, networks) by their governance systems and the nature of ecosystem outputs. Distinct from,
e.g., conventional supply chains, ecosystems are not defined by contractual relationships alone
(Jacobides, Cennamo, & Gawer, 2018). Distinguishing ecosystems from generic organizational net-
works, ecosystem roles and shared standards enable ecosystem constituents to engage in productive
interactions that generate a coherent ecosystem-level output, often targeted at a defined audience
(Shipilov & Gawer, 2019). The attractiveness of this rather elastic metaphor primarily rests on its abil-
ity to describe a fresh approach to ‘organically’ govern mutual organizational specialization, coevolu-
tion, and the collective generation of system-level outputs (Adner & Kapoor, 2010; Autio & Thomas,
Perhaps because of its elasticity, the ecosystem concept has been adopted by a wide variety of
scholarly perspectives, with varied phenomenological and conceptual emphases. For instance, the
strategy literature tends to emphasize the collective generation of outputs, defining ecosystems as
“…the alignment structure of the multilateral set of partners that need to interact in order for a focal
value proposition to materialize(Adner, 2017: 40). Economic geography scholars have emphasized
the spatial dimension and defined ecosystems as ..institutional, geographic, economic, or industrial
contexts [which] can be analysed at different levels of aggregation (e.g. firms, industries, universities,
regions, and nations)(Feldman, Siegel, & Wright, 2019: 1). Innovation scholars have emphasized
the knowledge and learning dimensions, defining ecosystems as “…clusters (physical or virtual) of
innovation activities around specific themes (e.g., biotechnology, electronics, pharmaceutical and
software)(Ritala, Agouridas, Assimakopoulos, & Gies, 2013: 248).
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This heterogeneity is not confined to ‘units’ of analysis and scholarly perspectives alone. Schol-
ars have also variably adopted related concepts such as ‘innovation ecosystems’, ‘business ecosys-
tems, technology ecosystems, platform ecosystems, entrepreneurial ecosystems, and knowledge
ecosystems’ with significant overlap in their use. Confusing matters further, these concepts often
conflate innovation processes and outputs.
As examples of the varying foci, flavours, and emphases
of the broad ecosystem literature, Moore (1993:76) notes that in business ecosystems “…companies
coevolve capabilities around a new innovation”, and that business ecosystems “…condense out of the
original swirl of capital, customer interest, and talent generated by a new innovation. In the early
platform literature, Cusumano and Gawer (2002: 54) referred to a “platform and its innovation eco-
system”, and more recently, in an analysis of the downsides of collaborating with complementors in
two-sided platform markets, Mantovani and Ruiz-Aliseda (2016) discuss innovation ecosystems. In
their exposition on the genesis of entrepreneurial ecosystems, Autio et al. (2018) explicated how the
concept of entrepreneurial ecosystems relates to a phenomenon that is similar but distinctive from
those of ‘innovation ecosystems’, ´regional systems of innovation’, and innovative milieus’, and are
distinguished by their focus on facilitating business model innovation by entrepreneurial new ven-
tures. Järvi et al. (2018:1524), in their investigation of the organization of knowledge ecosystems, ap-
ply the concept to the very early phases of innovationinitial knowledge creation and search”. Only
recently have there been attempts to explore the theoretical contours of these concepts and to illumi-
nate their shared theoretical and conceptual underpinnings (Adner, 2017; Autio & Thomas, 2019;
Jacobides et al., 2018).
In one of the earliest reviews of the ecosystem literature (Autio & Thomas, 2014), we asked
whether the ecosystem concept adds insight beyond existing constructs describing organizational col-
lectives or whether we are simply dealing with yet another attractive catchphrase. We believe we can
There are many examples of conflation of ecosystem concepts in the strategy literature. In our 2014 re-
view we conflate business, innovation and platform ecosystems. Gawer and Cusumano (2014) in their review of
industry platforms move interchangeably between “business ecosystems”, “innovation ecosystems” and “plat-
form ecosystems”. Jacobides et al. (2018), while clearly differentiating between “business ecosystems”, “inno-
vation ecosystems” and “platform ecosystems” in their initial conceptual review, title their main theoretical de-
velopment section “business ecosystems”.
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emphatically state that the ecosystem concept does indeed add insight beyond existing constructs.
fact, we believe the proliferation of perspectives and applications of the different ecosystem concepts
testifies of the salience of the underlying phenomenon (or set of related phenomena)which is digi-
talization opening up new ways for companies to share ideas and knowledge and flexibly combine
their outputs without contract-based coordination. But there is some distance still to cover for the eco-
system literature to reach theoretical maturity. Although there have been calls for greater definitional
rigour in ecosystems research (Adner, 2017; Jacobides et al., 2018; Shipilov & Gawer, 2019), this ri-
gor is still largely missing in the literature. In this article we seek to introduce some clarity to the con-
ceptual proliferation that afflicts the ecosystem literature.
This article is structured as follows. We first detail the sources of the conceptual proliferation
that characterizes the ecosystem literature, identifying two major dimensions of this proliferation: the
nature of ecosystem outputs and the ‘unit’ of analysis (with associated thematic emphasis). We then
detail four characteristics of ecosystems that are common across applications of the concept and
which provide the baseline for our expansive definition. From this baseline we then propose a typol-
ogy that positions different ecosystem concepts, drawing on the two major dimensions of conceptual
proliferation. We conclude by discussing the important role digitalization in the popularity of the eco-
system concept, and the implications of our insights for ecosystem emergence, competition, coevolu-
tion, and resilience.
The notion of an ‘ecosystem’ was introduced into the management literature quite early, with
Moore’s (1993) advocacy of an ‘ecological’ approach to understanding the contexts within which
businesses compete and collaborate. In his seminal paper Moore never actually provided a clear defi-
nition for his term, merely noting that an ‘ecosystem’ is a structure where companies work ‘coopera-
tively and competitively’ across industries to satisfy customer needs. In addition to leaving the notion
of the ‘ecosystem’ vague, Moore also never made it clear what exactly he meant with the notion of an
We deliberately refer to the term ‘ecosystem’ as a concept to underline the as-yet early stage of theoret-
ical development in this area. A ‘concept’ is a generally accepted term to refer to a phenomenon or instance that
may or may not be well understood theoretically. A ‘construct’ is a statement of a concept that is useful for theo-
rizing; a ‘construct’ lends itself for empirical operationalization, whereas a ‘concept’ may not (Suddaby, 2010).
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‘ecological’ approach to strategy, beyond loose parallels to natural ecosystems and stating that this
approach was ‘different’ from industry- or sector-specific analysis. While scholars subsequent to
Moore, such as Iansiti and Levien (2004) and Peltoniemi (2006), began to develop these biology-in-
spired ideas further, they have not been systematically applied (see Gómez-Uranga, Miguel, &
Zabala-Iturriagagoitia, 2014 for an exception). As a result, scholars have applied the ecosystem con-
cept with varying prefixes such as ‘innovation ecosystem’ and ‘business ecosystem’ without enough
specificity, which has given rise to the current conceptual proliferation we highlighted above. We sug-
gest that this proliferation manifests itself along two key dimensions: the ‘unit’ of analysis (together
with thematic emphasis) and the nature of ecosystem outputs.
Setting up the first dimension of conceptual proliferation is the fact that the ecosystem concept,
broadly speaking, can be applied at many levels, or ‘units’, of analysis. This elasticity in terms of
levels of analysis also characterizes the conception of biological ecosystems, which might consist of a
patch of soil with plants and microorganisms, or an anthill, or the entire planet Earth (Pickett &
Cadenasso, 2002; Willis, 1997). In management, similar elasticity can also be observed. For instance,
innovation ecosystems have been considered at various spatial levels of analysis, ranging from subur-
ban (Chesbrough, Kim, & Agogino, 2014 ), city (Claudel, 2018; Visnjic, Neely, Cennamo, & Visnjic,
2016), regional (Radziwon, Bogers, & Bilberg, 2017), to national (Carayannis & Campbell, 2009) and
even global levels (Nambisan, Zahra, & Luo, 2019). Ecosystems have also been considered at non-
spatial levels of analysis, and here the concept has been used to refer to the focal firm and its comple-
mentors and suppliers which need not inhabit the same region, as long as they belong to the same
sector (Adner, 2006, 2017; see Adner & Kapoor, 2010 for an operationalization), platforms and their
complementors (Gawer & Cusumano, 2014; Mantovani & Ruiz-Aliseda, 2016 ), and entire industries
(Ansari, Garud, & Kumaraswamy, 2016). Related to the ‘unit’ of analysis dimension, different levels
also tend to be associated with different thematic emphases, in the sense of key challenges being ad-
dressed. Whereas spatial applications tend to focus on various ecosystem community dynamics (e.g.,
learning and knowledge spill-over processes), non-spatial dimensions tend to emphasize issues related
to governance and coordination. This overlapping thematic emphasis will be adopted to inform our
typology of ecosystem concepts.
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A second dimension of conceptual proliferation arises from the nature of the ‘ecosystem ser-
vice’ (for a review see Seppelt et al., 2011), or ecosystem outputs collectively generated. In ecosys-
tem contexts, ecosystem outputs have been conceptualized as products and services (Adner, 2017),
such as photolithography systems (Adner & Kapoor, 2010), mobile communications (Holgersson,
Granstrand, & Bogers, 2017), software and apps (Boudreau, 2012), as well as new start-ups that in-
stantiate business model innovations (Autio et al., 2018; Kanter, 2012), and new research-based
knowledge (Alexy, George, & Salter, 2012; Almpanopoulou et al., 2017).
For the purposes of our organizing framework, we organize different types of ecosystems into
three broad categories based on the nature of ecosystem outputs. The first category comprises innova-
tion ecosystems. This category covers product and service innovation and describes situations where
the generative outputs created by varied, non-hierarchically related (in terms of contractual relation-
ships) ecosystem participants coalesce into a coherent, user-customizable offering at the ecosystem
level, one that is aimed at a defined audience (e.g., photovoltaic solar energy systems). The second
category covers entrepreneurial ecosystems, or regional communities who facilitate the creation of
new start-up ventures. These communities operate collective discovery and instantiation of new, inno-
vative ways of creating, delivering, and capturing valuei.e., business model innovation and new
ventures that carry them as the ecosystem output. In contrast with the first category, the collective
process of business model innovation usually does not target defined audiences at the ecosystem level,
but rather, tends to be sector and technology agnostic. As the third category, we identify organiza-
tional collectives specializing in the production of new research-based knowledge, an ecosystem out-
put that has previously been studied extensively under the rubrics of national, regional, and sector-
specific systems of innovation. Although technology specific, the outputs of knowledge ecosystems
comprise an important share of pre-commercialized knowledge and are usually not targeted at a de-
fined audience.
In order to re-introduce coherence into the expanding literature on ecosystems in management,
we use the two dimensions of ‘unit’ of analysis and the nature of the ecosystem output to create a con-
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ceptual typology of different ecosystem concepts. Before doing so, however, we next discuss charac-
teristics that are common to all ecosystem concepts and which help differentiate the ecosystem phe-
nomenon from constructs that came before, such as supply chains and networks.
The proliferation of the ecosystem literature has prompted several reviews (for recent reviews,
see Aarikka-Stenroos & Ritala, 2017; Gomes, Facin, Salerno, & Ikenami, 2018; Oh, Phillips, Park, &
Lee, 2016; Scaringella & Radziwon, 2018; Suominen, Seppänen, & Dedehayir, 2019). Drawing on
these and our own reading of this burgeoning literature, we identify four distinguishing commonalities
across different ecosystem concepts in management. The first characteristic is that of participant het-
erogeneity, in that ecosystems are composed of heterogeneous participants in various roles. Although
participant heterogeneity characterizes also other concepts that describe collectives of organizations,
the participant heterogeneity displayed by ecosystems is often broader and can span multiple indus-
tries and transcend the boundary between public and private sectors. The second distinguishing char-
acteristic is that ecosystems facilitate an ecosystem output that is more encompassing than any single
participant could deliver alone. Again, while a system-level output also characterizes supply chains,
these are produced to a predefined design and therefore cannot match the generativity manifest in eco-
systems, defined as their …overall capacity to produce unprompted change driven by large, varied,
and uncoordinated audiences (Zittrain, 2006: 1980). The third distinguishing characteristic is the na-
ture of interdependence among ecosystem participants, which is distinctly different from that charac-
terizing networks and supply chains. The fourth distinguishing characteristic is the nature of ecosys-
tem governance, which relies primarily on non-contractual mechanisms such as role definitions, su-
permodular complementarity, and the co-alignment provided by the ecosystem platform to strike a
balance between generativity and coherence in ecosystem outputs. Whilst none of the four character-
istics alone uniquely distinguishes ecosystems from other organizational collectives, the combination
of the four characteristics is unique to ecosystems, and individual characteristics also help distinguish
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between innovation ecosystems, entrepreneurial ecosystems, and knowledge ecosystems. We next
elaborate on each characteristic.
Participant heterogeneity
Ecosystem communities, regardless of level of analysis or type of innovation, tend to exhibit
high levels of participant heterogeneity. Heterogeneity in ecosystems generally springs from the fact
that their constituent participants may come from a variety of industries and sectors (Autio et al.,
2018; Jacobides et al., 2018; Moore, 1993). This heterogeneity is further accentuated by the roles-
based governance that characterizes ecosystems: instead of ecosystem roles being defined by formal
supplier contracts on a bilateral basis, as is the case of supply chains, ecosystem roles are usually nor-
matively defined and applicable to anyone who chooses to assume that role (Jacobides et al., 2018).
Reflecting this looser regulation of ecosystem participation (relative to contractually defined supply
chains), Iansiti and Levien (2004) described an ecosystem as including the loose networks of suppli-
ers, distributors, outsourcing firms, makers of related products or services, technology providers, and
others. More recently, (Adner, 2017: 40) explicitly noted that ecosystems are composed of a “multilat-
eral set of partners. Others have specifically included customers in their ecosystem community (e.g.,
Autio and Thomas (2014) included customers as “use side participants”). Others have included com-
petitors: for instance, Moore (1996) included competitors in his original definition, as has much of the
“open innovation ecosystem” literature (Bogers et al., 2017; Frankort, 2013). Yet others have consid-
ered non-market participants, such as universities and public research institutions (Clarysse, Wright,
Bruneel, & Mahajan, 2014; Järvi et al., 2018; van der Borgh, Cloodt, & Romme, 2012), and govern-
mental organizations, such as regulatory authorities, standard-setting bodies, and the judiciary (Autio,
Kenney, Mustar, Siegel, & Wright, 2014; Garnsey & Li, 2013; Teece, 2007). These inclusive defini-
tions partly reflect the thematic foci of the respective ecosystem studies: for example, studies of
‘knowledge ecosystems’ are more likely to include also public-sector participants in the ecosystem,
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making it challenging to distinguish this stream from the closely related and much more well estab-
lished ‘systems of innovation’ stream of literature on public-private interaction especially from the
notion of sectoral systems of innovation (Malerba, 2002; Malerba & Breschi, 1997).
Ecosystem outputs
Ecosystems facilitate the collective generation of ecosystem outputs. One such output com-
prises products and services that are compatible with one another, often adhering to a modular product
architecture that allows the user to assemble a customized composition of modules to suit individual
preferences. A canonical example is the assembly, sometimes by users, of individualized PCs from
components offered by the ‘Wintel’ ecosystem. In modularly structured ecosystems, participants in-
teract in order for a focal value proposition to materialize(Adner, 2017: 40; Jacobides et al., 2018)
and the specific products and services that they co-produce are abstracted into the more general notion
of a coherent, yet customizable ecosystem value offering targeted at a defined set of users. Another
ecosystem output comprises innovative business modelsan output that characterizes entrepreneurial
ecosystems in particular (Autio, Cao, Chumjit, Kaensup, & Temsiripoj, 2019; Autio et al., 2018). In
entrepreneurial ecosystems, the participants “facilitate entrepreneurial opportunity pursuit by new
ventures through radical business model innovation” (Autio et al., 2018: 74), and the innovative busi-
ness models are operationalized by new ventures. However, this output of entrepreneurial ecosystems
differs from product and service generating ecosystems in two important respects. First, business
model innovations are not intended for consumption by a defined audience the same way as the out-
puts of product and service generating ecosystems are: business model innovations benefit entrepre-
neurial ventures who harness them for entrepreneurial competitive advantage. In this sense, entrepre-
neurial ecosystems do not facilitate an actual ecosystem value offering (i.e., help resolve the needs of
a defined audience), only ecosystem outputs in a more general sense.
Yet another ecosystem output comprises generic knowledge production. In ‘knowledge ecosys-
tems’, participants interact so that there is collaborative exploration of new knowledge as central ac-
tivity and output” (Järvi et al., 2018: 1524). In such situations, the knowledge may not yet be embod-
ied in defined products, services, or even business models, but rather, capture the outputs of distrib-
uted innovative activities by ecosystem participants, making this concept virtually indistinguishable
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from the concept of sector systems of innovation in the long-established ‘systems of innovation’ liter-
ature (Malerba, 2002). This ecosystem output also differs from those generated by product and service
generating ecosystems, in that its audience is much more diffuse and comprises, e.g., the scientific
and the industrial community (who may or may not be part of the knowledge ecosystem itself) and
comprises both pre-commercialized and commercialized knowledge.
Participant interdependence
Probably the most widely referenced characteristic of ecosystems is participant interdepend-
ence. In biology, ecosystem interdependence is generally termed ‘mutual symbiosisand occurs when
otherwise unrelated species exchange materials, energy, or information in a mutually beneficial man-
ner (Miller, 1994). In management, while symbiosis has been occasionally referenced (Aarikka-
Stenroos & Ritala, 2017; Autio & Thomas, 2014), interdependence between heterogeneous ecosystem
participants has been mostly considered from technological, economic, and cognitive perspectives.
The first type of interdependence is technological, in that the heterogeneous actors within the
ecosystem are co-specialized, often around a shared platform or a common modular architecture
(Adner, 2012; Autio et al., 2018; Jacobides et al., 2018). Co-specialization occurs when there exists
coevolved, idiosyncratic dependence between entities that emanates from the requirement to provide
inputs into the ecosystem that are mutually compatible and so can support coherent, yet customizable
ecosystem output (Jacobides et al., 2018; Teece, 1986). For example, in telecommunications ecosys-
tems, different organizations are co-specialized when one organization supplies the technology, an-
other customer relationship management, and a third infrastructure management, which together de-
fine the ecosystem output. Ecosystem outputs typified by technological interdependence tend to be
composed of modular offerings available from a set of horizontally related suppliers, and where the
final choice of which components to integrate may be left to the customer, as might the integration
work itself (Autio, Dattée, & Thomas, 2019; Jacobides, Sundararajan, & Van Alstyne, 2019).
Compared to product and service generating ecosystems, entrepreneurial ecosystems do not ex-
hibit as much technological interdependence as they do role dependence, where the roles are orga-
nized around the early life cycle of new ventures. In entrepreneurial ecosystems we observe different
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ecosystem participants (e.g., new venture accelerators, funding and resource providers, advisors, train-
ers) each operating in a specialized role that requires specialized expertise, yet the roles are interde-
pendent in the sense that none of the ecosystem participants alone is able to create the ecosystem out-
put i.e., business model innovation as carried by new ventures. In contrast, knowledge ecosystems
exhibit specialization to a shared, specialized knowledge base, which may comprise both pre-com-
mercialized and commercialized knowledge. In knowledge ecosystems interdependencies are exhib-
ited along different stages of the knowledge maturity continuum.
A second type of ecosystem interdependence is economic, in that the value that each member
receives from participating in the ecosystem is dependent on the simultaneous availability of compati-
ble offerings by others. Economic interdependencies can occur when the ecosystem enables econo-
mies of scale and scope (Autio et al., 2018; Jacobides et al., 2018; Thomas, Autio, & Gann, 2014).
Participants can also exhibit economic dependence when there are externalities, when the actions of
one agent affect the interests of another agent other than by affecting prices(Davis & North, 1970:
134). In ecosystem contexts, many externalities are driven by the size of the network, where the utility
from participating in the network is dependent on the numbers of others in the network, either through
production or consumption economies (Katz & Shapiro, 1986). Such network externalities can be di-
rect, linked to the physical network and its size, where increases in network size creates increases in
the quality (Katz & Shapiro, 1985) and utility of output (Gupta, Jain, & Sawhney, 1999). They can
also be indirect and emerge through the provision of complementary products and services (Gupta et
al., 1999). Economic interdependencies can also arise from technological interdependencies, where
supermodular complementarities in consumption occur across the different components. Supermodu-
larity occurs when the presence of complementary modules makes the modules more valuable to the
user, as is the case of, say, the Android platform and Android-compatible applications (Jacobides et
al., 2018). In entrepreneurial and knowledge ecosystems, economic interdependencies arise from
knowledge sharing and knowledge spill-overs concerning either ‘what works’ in business model inno-
vation or specialized knowledge. Knowledge sharing and spill-overs drive the growth and maturation
of the ecosystem’s shared knowledge base, and therefore increase its value for the ecosystem partici-
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A third type of interdependence is cognitive, in the sense that ecosystem participants have a set
of socially constructed, historical patterns of material practices, assumptions, values, beliefs, and
rules … which provide the formal and informal rules of action, interaction, and interpretation that
guide and constrain decision makers(Thornton & Ocasio, 1999: 804). Cognitive interdependence is
an important aspect of a functioning ecosystem, as participants can represent many different speciali-
zations, with differences in knowledge and skills and cognitive frames (Weick, 1995). Cognitive in-
terdependence is particularly important in promoting ecosystem cohesion when the ecosystem partici-
pants are heterogeneous, and each participant may adhere to specific world and economic views that
are not necessarily widely shared with others within the ecosystem. Cognitive interdependence is also
important because the self-interests and expertise of ecosystem participants may differ, increasing
cognitive distance among these (Wareham, Fox, & Cano Giner, 2014) . Cognitive interdependence is
often expressed through an ecosystem collective identity (Gawer & Phillips, 2013; Thomas & Ritala,
2019), that consists “the shared meaning that an organizational entity is understood to have that
arises from its members’ (and others’) awareness that they belong to it(Cornelissen, Haslam, &
Balmer, 2007: S3). The ecosystem collective identity helps bind the ecosystem participants together
by attenuating the negative effect of cognitive distance and by encouraging commonality in perspec-
tives towards the ecosystem and coherence in responses to systemic and environmental changes
(Friedland & Alford, 1991).
Non-contractual governance
The final characteristic that distinguishes ecosystems from value and supply chains, national
and regional systems of innovation, and R&D consortia is their relatively strong reliance on non-con-
tractual governance (Gulati, Puranam, & Tushman, 2012; Jacobides et al., 2019). On this basis,
Jacobides et al. (2018) suggest that ecosystems should be seen as a distinctive solution to the distrib-
uted governance challenge characterized by co-specialization among hierarchically independent, yet
interdependent ecosystem constituents. Instead of formal, relationship-specific contracts that uniquely
define each relationship within, say, a supply chain, interactions among ecosystem participants are co-
ordinated by a co-alignment structure that enables ecosystem participants to specialize in specific
roles that are not necessarily defined by formal contracts.
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An ecosystem co-alignment structure reflects both the interdependencies that typify an innova-
tion ecosystem, as well as power relations between its constituents. In particular, innovation ecosys-
tems that feature strong technological interdependence often employ technological architectures and
platforms as their co-alignment structure (Wareham et al., 2014), while other types of ecosystems
(e.g., entrepreneurial, knowledge ecosystems) may emphasize economic or cognitive co-alignment
structures (Autio & Thomas, 2018; Järvi et al., 2018). For instance, an ecosystem collective identity
that epitomizes the participants’ shared sense of identification with the ecosystem community
(Cornelissen et al., 2007; Gawer & Phillips, 2013; Thomas & Ritala, 2019; Wareham et al., 2014) can
give meaning to the questions who we are and what we do(Navis & Glynn, 2010). This shared
ecosystem identification shifts the perspective of ecosystem participants from the individual to the
collective, so that ecosystem participants jointly confront challenges that lie beyond their own imme-
diate responsibilities and defines how they want to organize and address mutual interdependence
(Adner, 2012).
Ecosystem co-alignment structures also reflect power relationships within the ecosystem, or its
stratification (Gulati et al., 2012). Stratification naturally emerges in networks, regardless of the na-
ture of the networked system, its constituents, or the specific nature of network connections (Barabási,
2002). Although the alignment structure assigns ecosystem participants specific roles that are not nec-
essarily defined by formal contracts, the degree of stratification between these roles (and participants)
can vary, reflecting power differentials between participants. Depending on the size of the ecosystem
community, its degree of stratification and the roles defined by the co-alignment structure, ecosystem
governance modes can range from top-down, hierarchical direction through established lines of com-
mand to lateral and informal coordination, for example, through the communication of knowledge and
the propagation of social roles and behavioural norms (Gawer & Phillips, 2013; Järvi et al., 2018).
Given the many levels, or ‘units’, of analysis at which the ecosystem concept has been applied
in management and business disciplines and the range of different ecosystem outputs (i.e. ‘innova-
tion’) that can be collectively generated, any overall definition will necessarily be rather general. That
Innovation Ecosystems
- 16 -
said, integrating the characteristics of participant heterogeneity, system-level outputs, participant in-
terdependence, and distinctive governance, we define an ecosystem as: a community of hierarchi-
cally independent, yet interdependent heterogeneous participants who collectively generate an eco-
system output. Figure 1 presents our typology and Table 1 presents definitions for each of the eco-
system types as well as other important ecosystem concepts.
[Insert Figure 1 and Table 1 around here]
Based on this definition and addressing the challenges introduced by the dimensions of concep-
tual proliferation, we now provide an organizing typology of ecosystem concepts. The first dimension
of the typology is the ecosystem-level output. This output takes three main forms, for innovation eco-
systems, entrepreneurial ecosystems, and knowledge ecosystems, respectively: ecosystem value offer-
ing targeted at a defined audience (innovation ecosystems), business model innovation encapsulated
in new start-up ventures (entrepreneurial ecosystems), and new research-based knowledge
(knowledge ecosystems). The second dimension of our typology is the research emphasis of extant
scholarshipnamely community dynamics, output co-creation, and interdependence management. By
research emphasis, we are not directly referring to the unit or level of analysis (although different re-
search emphases tend to be associated with different ‘units’ of analysis as noted above), but instead to
the focus of scholarly attention. We next discuss our organizing typology of ecosystem concepts.
Innovation Ecosystems
In our organizing typology, innovation ecosystems are those that exhibit a coherent ecosystem-
level value offering targeted at a defined audience as their ecosystem-level output. This
conceptualization has primarily arisen from the strategy literature, although it has also drawn from
earlier work on modularity (e.g. Baldwin & Clark, 2000). Innovation ecosystems are multi-stake-
holder venues for value co-production, which often have a platform, or a set of shared technological
compatibility standards as a co-alignment mechanism (Adner, 2017; Autio & Thomas, 2014;
Constantinides, Henfridsson, & Parker, 2018; Jacobides et al., 2018; Thomas et al., 2014; Yoo,
Boland, Lyytinen, & Majchrzak, 2012). This supply-side emphasis on value co-production reflects the
roots of much of the strategic management literature in industrial organization economics, where
Innovation Ecosystems
- 17 -
firms are portrayed as operating in clearly delineated industries, subject to competitive forces deter-
mined by industry structural properties, and competing in an open market with their products and ser-
vices (Porter, 1980; Porter, 1985). Innovation ecosystems differ from conventional supply chains in
that not all supplier relationships are contractually governed, yet the value of the offerings of any
given ecosystem participant for the customer may be boosted by concurrent availability of comple-
mentary products and services (Adner, 2017; Ceccagnoli, Forman, Huang, & Wu, 2012; Teece, 2018).
Following our typology (Figure 1) and echoing the typology of Jacobides et al. (2018), we sug-
gest that there are three types of innovation ecosystems: business ecosystems’, which emphasize the
broader community within which a focal firm operates; modular ecosystems,’ which emphasize the
collective co-production of an ecosystem value offering directed at a defined audience, and platform
ecosystems which emphasize the coordination of technological interdependencies, generally through
platforms. We now discuss each, reviewing how the four ecosystem characteristics have been consid-
ered in each context.
Business ecosystems. When the research emphasis is on the broader economic context which a
focal firm must monitor and react to, an innovation ecosystem has generally been called a business
ecosystem. Deriving from the initial formulations of Moore (1993) and Iansiti and Levien (2004),
many writings on business ecosystems do not make the explicit assumptions that characterize the
other types of innovation ecosystems (see below). Adner (2017) calls these ecosystems-as-affilia-
tion, and he uses the label to broadly denote an ecosystem as a community of actors operating around
a shared co-alignment structure, such as a shared set of standards or cognitive schemata. Such com-
munities tend to be characterized by greater levels of fluidity, emergence, and co-creation in the form
of co-created, yet emergent ecosystem value offerings composed of generative inputs by ecosystem
participants. As actor roles in such communities often are less fixed than in the case of other innova-
tion ecosystems, there is also more scope for innovation that potentially changes the roles and rela-
tionships among ecosystem actors. Business ecosystems can have quite a broad scope for instance,
Teece (2007:1325) considers them to include the community of organizations, institutions, and
individuals that impact the enterprise and the enterprise’s customers and supplies … including
Innovation Ecosystems
- 18 -
complementors, suppliers, regulatory authorities, standard-setting bodies, the judiciary, and
educational and research institutions”.
Modular ecosystems. Modular ecosystems emphasize the collective generation of an
ecosystem output targeted at a defined audiencea value offeringwith the analytic interest on the
focal firm and the set of components (upstream) and complements (downstream) that support it, and
which have a clear supply-push and value production emphasis (Adner, 2017; Adner & Kapoor, 2010;
Hannah & Eisenhardt, 2018; Jacobides et al., 2018). In his review of this “structural” stream, Adner
(2017:40) defined modular ecosystems
as: “…the alignment structure of the multilateral set of part-
ners that need to interact in order for a focal value proposition to materialize.” This alignment struc-
ture needs to be able to align unpredictable innovative inputs from hierarchically unrelated partici-
pants yet retain enough coherence such that the different outputs can work together and thus deliver
the overarching ecosystem value offering. Modular ecosystems deliver their value proposition through
a co-alignment structure expressed as a shared product or service architecture featuring architectural
interfaces that allow the platform ecosystem to be partitioned into a relatively stable set of modules
(Baldwin & Woodard, 2009; Tiwana, Konsynski, & Bush, 2010). Examples from literature include,
e.g., the semiconductor lithography equipment industry and the photovoltaic solar panel industry
(Adner & Kapoor, 2010; Hannah & Eisenhardt, 2018). We use the term modular ecosystems to distin-
guish these structureswhich often represent evolution of supply chainsfrom platform ecosystems,
which are underpinned by a digital platform that acts as the underpinning co-alignment structure.
Modular ecosystems generally have (comparatively) a narrow scopeconsisting of the focal firm(s)
and immediately adjacent complementors and suppliers, with the customer represented in abstract
through their adoption and or acceptance of the ecosystem output, in the sense that the ecosystem out-
put would not be viable if it did not meet specific customer needs.
Platform Ecosystems. Platform ecosystems are innovation ecosystems that emphasize the role
of technological dependencies in the ecosystem and mostly focuses on a specific class of
technologiesnamely, a shared connectivity interface broadly referred to as a ‘platform. Particularly
Adner used the term innovation ecosystems to refer to what we define as ’modular ecosystems’.
Innovation Ecosystems
- 19 -
when accessible through the Internet, this co-alignment structure ensures that a network of location-
unbound complementors are able to create complements that enhance the ecosystem value offering of
the platform without the need to resort to formal contracts particularly where there is a platform
owner who can also act as an adjudicator of platform interactions (Ceccagnoli et al., 2012; Gawer &
Cusumano, 2008; McIntyre & Srinivasan, 2017). A key governance challenge for platform ecosys-
tems is the coordination and maintenance of the necessary capabilities and standards for the alignment
structure (McIntyre & Srinivasan, 2017; Tiwana et al., 2010; Wareham et al., 2014) that define the
technical specifications (Suarez, 2005) and ensure compatibility among the ecosystem participants
and components (Eisenmann, 2007). Platform ecosystem governance needs to address a fundamental
tensionbetween the need for flexibility and variety versus the need for integrity and standardization.
While flexibility allows ecosystem participants to generate variety within the ecosystem, integrity
helps align them so as to maintain the coherence of ecosystem outputs (Wareham et al., 2014).
A related concept that sometimes pops up within the ecosystems literature is that of technol-
ogy ecosystems’ (also sometimes called digital ecosystems and software ecosystems), which has
been employed mainly in the information systems discipline. The usage of this concept overlaps with
those of modular ecosystems and platform ecosystems (see for instance Tiwana et al., 2010; Wareham
et al., 2014) , and occasionally knowledge ecosystems. This concept mostly considers ecosystems in
the context of digital infrastructures, which are shared, unbounded, heterogeneous, open, and evolving
sociotechnical systems comprising an installed base of diverse information technology capabilities
and their user, operations, and design communities (Hanseth & Lyytinen, 2010). The architecture and
functionalities of digital infrastructures allow multiple constituents to interact and co-create digital ar-
tefacts such as open source software and other digital artefacts, due to the opportunities for ‘con-
strained serendipity amongst distributed actors (see for instance Adomavicius, Bockstedt, Gupta, &
Kauffman, 2007; Thomas & Tee, 2019; Yoo et al., 2012). Because this concept overlaps with others
in our typology, we do not include it as a distinct ecosystem type.
Entrepreneurial Ecosystems
Entrepreneurial ecosystems differ from innovation ecosystems in important respects. First,
there is no ecosystem value offering targeted at a defined audience. Although entrepreneurial
Innovation Ecosystems
- 20 -
ecosystems facilitate an ecosystem output by cultivating a shared knowledge base regarding ‘what
works’ in harnessing advances in digital technologies and infrastructures for novel ways of organizing
for the creation, delivery, and capture of value (i.e., business model innovation), the resulting business
models can be applicable in virtually any sector and be targeted at any audience (Acs, Autio, & Szerb,
2013; Auerswald, 2014; Isenberg, 2010; Spigel, 2017). In the case of entrepreneurial ecosystems, this
audience is largely internal i.e., composed of new ventures who tap the experiences of others to
discover new business model practices that might be put into profitable use (Autio et al., 2018). In
entrepreneurial ecosystems, the ecosystem output consists of innovative business models and the new
ventures that embody them, but this output is not targeted at a defined audience.
While entrepreneurial ecosystems resemble concepts previously explored by economic geogra-
phers and innovation researcherssuch as clusters’, knowledge clusters’, industrial districts’, in-
novative milieus’, and regional and national systems of innovation (Arıkan & Schilling, 2011;
Crevoisier, 2004; Delgado, Porter, & Stern, 2010; Freeman, 2004; Lundvall, 1992; Tallman, Jenkins,
Henry, & Pinch, 2004)they constitute a distinct type of cluster because of their emphasis on entre-
preneurial agents and business model innovation, as opposed to product, service, or technological in-
novation co-created by a more diverse set of active ecosystem participants. This implies a distinc-
tively different learning and opportunity dynamics in entrepreneurial ecosystems, as opposed to entre-
preneurial regions and clusters previously documented in the literature. The process of discovering
‘what works’ in terms of digitally enhanced business model practices is driven by constant experi-
mentation by new ventures, as well as the sharing of resulting insights within the regional entrepre-
neurial ecosystem. In classical entrepreneurial clusters, opportunities are driven by, e.g., the discovery
of niches and the linear spill-over of research-based knowledge advances and their translation into
specific products and services (Autio et al., 2018; Autio & Thomas, 2019; Kenney & Von Burg,
1999). This distinction is reflected in characteristic structural elements of entrepreneurial ecosystems
such as new venture accelerators, coworking spaces, makerspaces, start-up academies, university en-
trepreneurship programs, crowdfunding, angel investors, business angels, venture capital, and men-
tors, all of which enable them to more effectively facilitate business model experimentation and asso-
ciated horizontal knowledge spillovers (Autio et al., 2018; Goswami, Mitchell, & Bhagavatula, 2018).
Innovation Ecosystems
- 21 -
Entrepreneurial ecosystems are predominantly a regional phenomenon. Experimentation-driven
collective discovery and related knowledge exchange regarding effective business model recipes are
facilitated by geographical proximity. Entrepreneurial ecosystems also attract specialized resources
(e.g., venture funding, new venture accelerators, specialized advice) that derive economies of scope
from spatial proximity. Harnessing spatial affordances, participants of entrepreneurial ecosystems ex-
plore and discover business model innovation opportunities opened by advances in digital technolo-
gies and infrastructures to support a distinctive cluster-level learning dynamic that is expressed
through the creation and scale-up of new ventures (Autio et al., 2018). These innovation opportunities
derive from the technical architecture of digital infrastructures, and, being industry and sector agnostic
and challenging legacy business models optimized by established incumbents, they ultimately support
an economy-wide redesign of value creation, delivery, and capture processes (Autio & Levie, 2017).
Entrepreneurial ecosystems support the cultivation and dissemination of cluster-level architectural
knowledge on a generic business process (as opposed to product-or technology-specific innovation):
effective business model innovation and entrepreneurial start-up and scale-up (Tallman et al., 2004).
The co-alignment structure of entrepreneurial ecosystems is mostly cognitive and economic,
and they do not exhibit a high level of stratification due to their sector-agnostic nature (Colombelli,
Paolucci, & Ughetto, 2019). A limitation of received research on entrepreneurial ecosystems is that
most of this research has overlooked the core knowledge dynamic of entrepreneurial ecosystems and
has instead focused on exploring how individual actors can influence their role within the entrepre-
neurial ecosystem. For instance, scholars have considered the governance of research joint ventures
(Audretsch & Link, 2019), venture capital and technology parks (Cumming, Werth, & Zhang, 2019),
individual researchers (Cunningham, Menter, & Wirsching, 2019), and universities (Meoli, Paleari, &
Vismara, 2019). Given their importance to regional economic development, there are significant pol-
icy efforts being undertaken to manage their interdependencies (Autio & Levie, 2017; Spigel, 2017)
and understand the dynamics of digital technologies in this context (von Briel, Davidsson, & Recker,
Knowledge Ecosystems
Innovation Ecosystems
- 22 -
The concept of ‘knowledge ecosystems features generic research-based knowledge and
associated applications as their system-level output. This concept has primarily been employed in the
innovation literature, reflecting the increasingly open processes of R&D and innovation (Bogers et al.,
2017; Von Hippel, 2007). Distinctive underlying theoretical logics do not yet appear to be fully
developed, however, and most of the themes explored under this rubric echo those extensively
explored within the ‘systems of innovation’ tradition over the past four decades (Lundvall, Johnson,
Andersen, & Dalum, 2002; Lundvall, 2007; Malerba, 2002; Malerba & Orsenigo, 1997; Nelson &
Winter, 1982). Given this overlap, the insights from the ‘knowledge ecosystems’ appear difficult to
distinguish from those in the extensive literature on systems of innovation. For instance, it is not clear
how a partial knowledge ecosystem differs from an R&D consortium (Doz, Olk, & Ring, 2000),
given the dependence of both on formal forms of governance (Järvi et al., 2018). Similarly, it is
challenging to see how a pre-figurative knowledge ecosystem is distinguished from regional systems
of innovation (Freeman, 2004), or a ‘knowledge cluster(Tallman et al., 2004), or even triple and
quadruple helix processes (Etzkowitz & Leydesdorff, 2000), given that their coordination isinfor-
mal, with no formal or determinate structures(Järvi et al., 2018: 1530). In addition to the indetermi-
nacy of their co-alignment structure, it is also not particularly clear how knowledge is embodied as
an ecosystem-level output (although see Leten, Vanhaverbeke, Roijakkers, Clerix, & Van Helleputte,
2013 for an exception).
Because of the emphasis of collective learning and knowledge exchange processes, knowledge
ecosystems have been primarily described at a regional level of analysis and in pre-competitive set-
tings (Clarysse et al., 2014; Järvi et al., 2018). With collaborative exploration of new knowledge as
their central activity and output (Järvi et al., 2018; van der Borgh et al., 2012), the goal of knowledge
ecosystem participantswhose commercial interests may divergeis to engage in joint creation of
new pre-commercial knowledge to create a shared resource that no single participant would be able to
create independently (Järvi et al., 2018; Leten et al., 2013). Given the focus of research under this la-
bel on knowledge as a system-level output, knowledge ecosystems have been described as consisting
of universities, public research institutions, bridging and brokering organizations, and for-profit firms
Innovation Ecosystems
- 23 -
collaborating to create new knowledge in a pre-competitive setting (Clarysse et al., 2014; Järvi et al.,
2018; Valkokari, 2015; van der Borgh et al., 2012).
To summarize and recap, the conceptual proliferation of the ecosystem literature can be largely
attributed to the elasticity of the concept in terms of ‘units’ of analysis, associated thematic foci, as
well as the variety of ‘ecosystem services’, or outputs they facilitate. This elasticity partly reflects the
ambiguity of the underlying phenomenon: organizations and individuals can coordinate their actions
for many different purposes, and they can cohabit different spaces (physical and virtual) in different
formations. Ultimately, however, the different manifestations of ecosystems all share two salient fea-
tures: they facilitate the co-generation of ecosystem outputs, and they do this without resorting to for-
mal supplier contracts. We next discuss implications of our model for ecosystems literature and re-
When considering our organizing conceptual typology, it is useful to remind ourselves what ul-
timately drives the ecosystem phenomenon and why the literature has expanded so dramatically over
the past decade. Ultimately, the trends of open innovation and business model innovation are enabled
by digitalizationthe application of digital technologies by business and society such that these be-
come infrastructural. Being generic-purpose communication and coordination technologies, digital
technologies and infrastructures enable organizations to radically re-think and re-design their interac-
tions for value creation, delivery, and capture, and for the co-generation of compatible outputs. Digital
technologies do not allow this solely by virtue of their coordination-enabling potency, but also be-
cause they can dramatically reduce asset specificity that constrains interactions among businesses that
predominantly rely on physical assets (e.g., production machinery) for the creation of valuable out-
puts. Physical assets exhibiting low degree of asset specificity (i.e., they cannot be easily allocated to
an alternative use without significant loss of value), the consequences of co-specialized investment
tend to be more durable and therefore require long-term relationships underpinned by formal con-
tracts. By alleviating this key constraint that regulates conventional relationships between business
firms that predominantly exploit physical assets for value creation, digitalization has enabled much
Innovation Ecosystems
- 24 -
more organic, emergent, and coevolving interactions within organizational communities for the pur-
pose of co-generating valued ecosystem-level offerings and outputs. Digital technologies being gen-
eral-purpose technologies, these effects are seen in virtually any sector and Moore’s law ensures
that this transformation is not likely to go away any time soon.
We believe the recent popularity of the ecosystem concept in management and business litera-
tures is largely because digitalization enables flexible, organic, coevolving, and emergent evolution of
collaborations within spatial and virtual communities of organizations in ways that rigidly asset-spe-
cific arrangements of inter-organizational collaborations do not. It is also notable how the ecosystem
concept is demarcated by previous biology-inspired in organizational research notably, the popula-
tion ecological perspective to populations of organizations (Hannan & Freeman, 1977). Whereas the
population ecological perspective focused on the effect of environmental effects on firm-level out-
comes, notably, entry and survival (e.g., effect of environmental resource munificence and population
density on organizational survival), the focus of the work on innovation ecosystems has been focusing
on the collective generation of outputs, again reflecting the enabling impact of digitalization on inter-
action and collaboration among heterogeneous, hierarchically independent ecosystem participants
(Autio et al., 2018).
If we accept that ecosystems in management are organic, coevolving phenomena, what do we
know about their dynamics? In this final section of our discussion, we comment on four dynamic as-
pects of innovation ecosystems as coevolving organizational communities. Our focus is on ecosystem
emergence, competition, coevolution, and resilience.
How do ecosystems emerge? Although we know what drives the ecosystem phenomenon (i.e.,
digitalization), there have been few studies that explore specific processes of innovation ecosystem
emergence (for exceptions, see Autio & Thomas, 2018; Dattée, Alexy, & Autio, 2018; Hannah &
Eisenhardt, 2018; Snihur, Thomas, & Burgelman, 2018).
A distinction needs to be made between spatially confined ecosystems (e.g., entrepreneurial
ecosystems) and those that are not spatially confined. This is because spatially confined ecosystems
(e.g., entrepreneurial ecosystems, knowledge ecosystems) typically build on what existed before. For
Innovation Ecosystems
- 25 -
example, the current incarnation of entrepreneurial ecosystems, with their emphasis on digitally en-
hanced business model innovation, exhibit very different dynamics than do ‘clusters of entrepreneur-
ship’ or similar regional agglomerations of entrepreneurial activity of the pre-digitalization era
(Delgado et al., 2010; Feldman, 2001). The classic 1990’s era entrepreneurial cluster at the core of
many regional systems of innovation tended to emphasize linear, technology-push innovation, in
which entrepreneurial agents translated advances in research into commercial application. The support
structures of entrepreneurial clusters (e.g., science parks) were optimized to support such knowledge
translation. In the digital era, many such structures have been co-opted to support the digitally en-
hanced process of business model experimentation and discovery, and many science parks currently
house or have been transformed into new venture accelerators that subscribe to the ‘lean entrepreneur-
ship’ heuristic. Whereas the dominant entrepreneur-driven innovation processes in regional systems
of innovation were science and research centric or occurred within the confines of asset-specific sup-
ply chains, the 2010s era entrepreneurial ecosystems exhibit much more user-centric business model
discovery processes that harness digitalization to sidestep and undermine established incumbents’ leg-
acy business models. This means that in entrepreneurial and knowledge ecosystems, the emergence
processes do not necessarily represent greenfield-type construction of a de novo ecosystem from
scratch, as might have been the case of many a classic entrepreneurial cluster (Feldman & Francis,
2006), but rather, represent instances of gradual transformation of an existing cluster into a new mo-
dus operandi through the establishment of, e.g., new venture accelerators in the region.
The emergence of other types of ecosystems (business ecosystems, modular ecosystems, plat-
form ecosystems) appears to follow a different pattern. While spatially confined ecosystems can come
to being through a gradual transformation (while retaining structures and processes that dominated an
earlier era), spatially unbound ecosystems are often de novo creations and therefore might require ac-
tive agency in order to be set in motion. Along these lines, the seminal article of Moore (1993) pro-
posed a four-stage evolutionary model of ecosystem creationbirth, expansion, leadership, and self-
renewalwhere an ecosystem progresses from a random collection of ad-hoc stakeholders to a more
structured and coherent community driven by a lead firm who balances cooperative and competitive
Innovation Ecosystems
- 26 -
processes (cf. Hannah & Eisenhardt, 2018). In the platform ecosystem literature, Gawer (2009) sug-
gested a three-stage model, where the platform ecosystem evolves under the direction of a platform
leader from a closed system towards greater openness. This view has been further developed by
Thomas et al. (2014), who suggested that in addition to developing along a predefined openness tra-
jectory, platform ecosystems can also evolve along three distinct leverage trajectories. More recently
Teece (2017) analyzed the requirements at each stage of this evolutionary lifecycle in terms of the in-
novation ecosystem leader’s dependence on the high-level dynamic capability categories of sensing,
seizing, and transforming. Recent empirical scholarship has begun to provide empirical evidence of
these stages (see, for instance Jha, Pinsonneault, & Dube, 2016; Leong, Pan, Newell, & Cui, 2016).
On a related view of ecosystem emergence, Dattée et al. (2018) showed how active agency is required
in the early stages of innovation ecosystem creation, and how it is critical for the ecosystem promoter
to exercise dynamic control of the visioning process (cf. Ansari et al., 2016; Snihur et al., 2018) in or-
der to encourage sign-up and early commitments to the emergent ecosystemas opposed to static
control of predefined ecosystem assets, as maintained by the ‘ecosystem blueprint’ view (Adner &
Kapoor, 2016).
Do ecosystems compete? Although we have a good understanding of what makes an ecosystem
unique as a venue for collaborative creation of ecosystem-level outputs, we know surprisingly little
about how ecosystems compete. Like the case of ecosystem emergence, a distinction needs to be
made between spatially confined innovation ecosystems and those that are not spatially confined. This
is because spatially confined innovation ecosystems are not exposed to specific market conditions de-
termined by user choice as are innovation ecosystems, whose value offerings are more directly tar-
geted at defined audiences. For this reason, the literature on competition in entrepreneurial and
knowledge ecosystems is sparse, although their properties may be systematically measured (Stam,
2018) and ranked (Szerb, Ács, Komlósi, & Ortega-Argilés, 2015).
As entrepreneurial and knowledge
ecosystems do not address defined audiences to the extent that market choice would be relevant for
See also:
countries/; retrieved 14/10/2019.
Innovation Ecosystems
- 27 -
their survival, such ecosystem rankings are more likely to cater to the needs of policy-makers. In the
case of spatially confined ecosystems, the competition that does occur tends to be on the supply side
rather than the demand side, in that entrepreneurial ecosystems compete for venture capital, angel in-
vestors, mentors and entrepreneurs. However, there is little research to date to explore this dynamic.
Spatially unbound ecosystems operate in a market context as their ecosystem-level outputs are
subject to competing value offerings. The notion of ecosystem competition was present in the earliest
of ecosystem literatures, with Moore (1993) subtitling his seminal HBR article “a new ecology of
competition” and titling his 1996 book “The Death of Competition”, referring to the end of traditional
competition between individual products or services (cf. Chen, 1996). Rather than occurring at the
level of products, competition plays out instead between ecosystems that span multiple (traditionally
defined) product markets (Cennamo, 2019; Rochet & Tirole, 2003). Similarly, ecosystem competition
is not a zero-sum game where competitors compete for a market of a given size (Priem, 2007) but is
instead focused on how the ecosystem can meet as many customer needs as possible (Cennamo &
Santalo, 2013). As a consequence, competitive behaviours in ecosystem settings are different
(Cennamo, 2019), consisting of, for instance: subsidization of one set of customers to support others
(Rochet & Tirole, 2003); varying the openness of the ecosystem to participants (Boudreau, 2010);
varying level of exclusivity (Cennamo & Santalo, 2013); as well as specific strategic moves such as
platform envelopment (Eisenmann, Parker, & Van Alstyne, 2011). Interestingly, while there has been
substantive research into competition in platform ecosystems, there is much less substantive research
that considers competition in business or modular ecosystems which have been seen more as venues
to understand interdependencies and governance (for an exception, see Hannah & Eisenhardt, 2018).
How do ecosystems change? We know that ecosystems are not static, in that they change over
time. In biology, ecosystems exhibit a “mutual adjustment of their components” that tends towards the
perfect dynamic equilibrium that can be attained in a system developed under given conditions and
with the available components(Tansley, 1935: 300). In the language of management, ecosystems
coevolve’ (Basole, 2009; Moore, 1993) through a process where environmental changes and changes
Innovation Ecosystems
- 28 -
in the ecosystem participants mutually influence each other, prompting mutual adjustments (Lewin &
Volberda, 1999; Merry, 1999; Van De Ven & Garud, 1994).
The notion of coevolution was initially suggested in the earliest innovation ecosystem litera-
ture. For instance Moore (1993) stated that ecosystems “…coevolve capabilities around a new inno-
vation, suggesting that ecosystem participants need to adjust their investments and choices over time
to maintain their complementarity with other participants, technologies and institutions. Others have
noted how ecosystem output coevolves with the business models of the ecosystem participants, ex-
plaining why certain participants join, stay in, or leave the ecosystem at specific points in time (van
der Borgh et al., 2012). The platform ecosystem literature has also considered how competition be-
tween ecosystem participants can lead to changes in the ecosystem itself (Mäkinen, Kanniainen, &
Peltola, 2014; Tiwana, 2015). More broadly, others have considered how external environmental fac-
tors can lead to ecosystem coevolution. For instance, Tiwana et al. (2010) argued that ecosystem gov-
ernance and participant interactions need to coevolve with changes to the environment, and others
have indicated that competition between challengers and incumbents can drive coevolution of an eco-
system (Ansari et al., 2016; Snihur et al., 2018). Still others have looked at how ecosystem partici-
pants can collectively evolve so as to ensure that the standards of legitimacy that define success for an
ecosystem are appropriate (Autio & Thomas, 2018).
The study of evolutionary processes within ecosystems thus seems to offer a trove of opportuni-
ties. Yet there has been little research that has substantively investigated coevolution, as most of the
papers mentioned refer to coevolution as a supporting dynamic. We believe this is an important re-
search gap, as coevolutionary processes within ecosystems represent an important dynamic regulating
their growth and stability. Given that ecosystems are open systems, we suggest there might be merit in
heeding the view of Rosenkopf and Nerkar (1999), who pointed out that coevolution is driven by both
internal factors (within-level coevolution) or was well as external factors (cross-level coevolution).
Finally, how to ecosystems remain healthy? In biology, an ecosystem is considered resilient
when it persists in the face of environmental changes and continues to provide ecosystem services to
Innovation Ecosystems
- 29 -
the species that depend on it (Willis, 1997). In ecosystems literature, the concept of resilience was in-
troduced by Iansiti and Levien (2004: 72), who argued that ecosystems need to be robust, i.e., capable
of absorbing external shocks and maintaining the potential for productive innovation: “the benefits are
obvious: A company that is part of a robust ecosystem enjoys relative predictability, and the relation-
ships among members of the ecosystem are buffered against external shocks.” By virtue of their rela-
tive absence of asset specificity and contractual relationships, innovation ecosystems are naturally bet-
ter positioned to exhibit resilience in the face of environmental change than are contractually under-
pinned, asset-specific supply chains. Interestingly, Adner and Kapoor (2010) arguably serves as an
illustration of the implications of a lack of resilience and the inability of innovation ecosystem partici-
pants to coevolve.
Ecosystem resilience has generally been considered along two dimensionsresponse diversity
(Krause, Razavi, Moschoyiannis, & Marinos, 2009) and governance (Wareham et al., 2014). Re-
sponse diversity is the ecosystem’s ability to generate a broad variety of offerings to meet customer
requirements (Cennamo & Santalo, 2013; Zhu & Iansiti, 2012), thereby enhancing the ecosystem’s
ability to adjust to changes in demand. For instance, Leong et al. (2016) considered how the adapta-
tion of product offerings within an ecosystem could also eventually enhance the ecosystem resilience.
Response resilience is regulated by ecosystem governance (Wareham et al., 2014), particularly when
it comes to the ability of the ecosystem to resolve the fundamental tension between the need for flexi-
bility and variety versus the need for coherence and standardization. Here the emphasis is on
understanding how the ecosystem’s interdependent participants interact to continue to produce the
ecosystem outputwith the corollary that if coordination within the ecosystem is inadequate, the
ecosystem will fail (e.g., Adner, 2012; Adner & Kapoor, 2010; Kapoor & Lee, 2013). With reference
to governance, emerging work in platform contexts is also investigating the implications platform
characteristics (e.g., Penttinen, Halme, Lyytinen, & Myllynen, 2018) and the dynamics of generativity
(Cennamo & Santaló, 2019) may have for resilience. In the context of entrepreneurial and knowledge
ecosystems, there is little that we are aware of that has substantively considered ecosystem resilience
(for an exception, see Roundy, Brockman, & Bradshaw, 2017).
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In this article we have outlined the dimensions of conceptual proliferation that afflict the eco-
system concept in management, highlighting how this proliferation operates along two main dimen-
sions: ‘unit’ of analysis and the nature of ecosystem outputs collectively generated. This proliferation
has resulted in conceptual heterogeneity, which hampers cumulative insight. We identified four char-
acteristics of ecosystemscommunity heterogeneity, ecosystem outputs, participant interdependence,
and distinctive governanceand how these distinguish the various ecosystem concepts from other
concepts and constructs used to theorize about organizational collectives. From this baseline we pro-
posed a general definition of an ecosystema community of hierarchically independent, yet interde-
pendent heterogeneous participants who collectively generate an ecosystem outputand an organiz-
ing conceptual typology that positions the different concepts used in ecosystem research and can be
used to delineate different streams of ecosystem research. We concluded by discussing the important
role digitalization has for the popularity of the ecosystem concept, and four dynamic aspects of inno-
vation ecosystems: emergence, competition, coevolution and resilience.
The concept of ecosystems in management describes a real phenomenon of high practical rele-
vance, yet one that can assume a broad range of manifestations. In order to coherently address this im-
portant phenomenon, researchers are challenged to adopt a coherent vocabulary to ensure cumulative
insight. We hope that the organizing conceptual framework suggested in this article will help the
mushrooming community of ecosystem researchers progress towards this goal.
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Innovation Ecosystems
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FIGURE 1 Ecosystem Typology
TABLE 1 Ecosystem Vocabulary
A community of hierarchically independent, yet interdependent
heterogeneous participants who collectively generate an ecosystem
A community of hierarchically independent, yet interdependent
heterogeneous participants who collectively generate an ecosystem
output and related value offering targeted at a defined audience.
A regional community of hierarchically independent, yet
interdependent heterogeneous participants who facilitate the start-up
and scale-up of entrepreneurial new ventures who compete with
innovative business models.
A regional community of hierarchically independent, yet
interdependent heterogeneous participants who advance the translation
of advances in research knowledge into products and services.
An system-level output that has been collectively generated by
heterogeneous ecosystem participants.
Value Offering
An ecosystem output that is targeted at a defined audience whose needs
it helps address.
Innovation Ecosystems
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... As we recognize different ecosystem types discussed in the literature, we argue that these can be traced back to differences in founding conditions as an imprinting cornerstone for ecosystem emergence. Authors from various disciplines (Gomes et al., 2018;Tsujimoto et al., 2018;Phillips and Ritala, 2019;Suominen et al., 2019;Thomas and Autio, 2020) have used the term "ecosystem" with a variety of meanings, leading to a number of open research questions (Kapoor, 2018). ...
Conference Paper
Abstract The rise of ecosystem prominence has provided several definitions of how we understand ecosystems nowadays. In this context, several scholars have considered influencing factors for ecosystem emergence. This paper addresses this consideration and analyzes the salient characteristics of different ecosystem types and their potential persistence since ecosystem founding to improve the understanding of emergence. We applied a three-step approach (1) identifying ecosystem types based on bibliometric analysis, (2) exploring salient characteristics per ecosystem type using qualitative content analysis and (3) deriving founding conditions from the salient characteristics following a conceptual approach. Based on a bibliometric analysis, we identified business/innovation, entrepreneurial and service ecosystems. In a second step, we developed salient characteristics within the themes of structure, power constellation/interdependencies and governance by inductive coding. As we identified a significant difference in alignment structure, we analyzed if alignment structure persists since ecosystem origin and explains why ecosystems differ. We analyzed potential pairings between alignment structure and their respective founding condition for every ecosystem type. With the alignment structures’ persistence, we can better understand why ecosystem types differ.
... The mechanisms for such leadership, however, have rarely been analyzed (Jacobides et al., 2018). While there is general agreement that ecosystems are managed neither through hierarchy nor solely through contracts (Thomas and Autio, 2020), few studies have empirically examined the organizing activities of these engagements (Thomas and Ritala, 2022). Put differently, as Jacobides et al. (2018) state: "We need a clearer sense of how ecosystems are structured and governed." ...
Full-text available
This paper uses process theory as a theoretical lens to analyze AstraZeneca's enactment of an open innovation initiative with the purpose of strengthening the firm's surrounding innovation ecosystem. Based on empirical data collected over 7 years, we develop a process model of open innovation enactment and explain how the initiative gradually transformed while maintaining its guiding principles, which were set from the start. In applying a process perspective, we highlight open innovation initiatives as dynamic and evolutionary – but not deterministic – developments. As such, we provide a comprehensive and more nuanced understanding of not only what open innovation is but also how it becomes. This study also contributes to the innovation ecosystem literature by theorizing how firms orchestrate innovation ecosystems through open innovation initiatives over time.
... These elements are key to understanding women's innovative entrepreneurial activities in emerging economies, although the literature highlights that there are not specific market conditions determined by user choice. Innovation ecosystems are defined as a community of interdependent heterogeneous participants targeted at a defined audience (Thomas and Autio, 2020), and traditionally, targeted to technology and product development irrespective of the gender of the entrepreneurs. To identify the participants for innovation ecosystems, facilitating institutions appear among with talent, the density of researchers, entrepreneurs, entrepreneurial culture, access to capital, and a supportive regulatory environment (Oh et al., 2016). ...
This chapter aims to study institutional voids affecting the conduct of business in emerging and Latin American countries and how they impact female innovative entrepreneurship activity. The authors use three databases: Global Entrepreneurship Monitor Adult Population Survey, the World Bank Database Ease of Doing Business Index, and the World Bank Open Data. The study uses a panel of 90 countries from the period 2011-2017 and focuses on emerging and Latin American countries. A total of 18 emerging economies are included in the analyses, and six Latin American countries are among them. To test their research questions, the authors use probabilistic models. The results show that the ease of doing business increases the probability of having gender-balanced entrepreneurial activity, and this probability decreases when the analyzed country is a Latin American one. This chapter contributes to shedding light on the understanding of female innovative entrepreneurship in an overlooked region.
... Replications of our study can help validate its usefulness in ecosystem positioning. Besides, the visualized generic ecosystem and further analysis of the interview data can reveal technological, economic, and cognitive interdependencies present in ecosystems (Thomas and Autio 2020). Lastly, aligning our collected data with databases such as Crunchbase could extend the derived list of roles and activities, improving the AM-specific results. ...
Conference Paper
Full-text available
Despite the increasing managerial awareness for ecosystems to organize complex value propositions, little is known about how different roles can establish their business models (BM) in ecosystems. AM drives innovations in the product design and manufacturing fields predominantly across companies, indicating the eco-systemic organization of value creation without orchestrating and dominant keystone actors yet. This paper explores ecosystem determinants by analyzing the dynamic additive manufacturing (AM) paradigm. We conduct an empirical study with companies from the AM domain to visualize their value activities and define generic roles in the interdependent value creation process, adopting the e³-value methodology. By exploring these ecosystem determinants, our results aid practitioners in positioning their BMs in the AM domain and generate descriptive insights for the orchestrator BM design in a dynamic domain without orchestrating keystones.
... The first view on ecosystems focuses on a powerful hub actor (i.e., an orchestrator) that organizes the ecosystem around a joint value proposition (Jacobides et al., 2018;Shipilov and Gawer, 2020;Thomas and Autio, 2020;Thomas and Ritala, 2021) -oftentimes delivered over a digital platform. An example of an orchestrated digital ecosystem is the Amazon Marketplace (Ritala et al., 2014), where the platform orchestrator ( ...
Full-text available
For better or worse, digital technologies are reshaping everything, from customer behaviors and expectations to organizational and manufacturing systems, business models, markets, and ultimately society. To understand this overarching transformation, this paper extends the previous literature which has focused mostly on the organizational level by developing a multi-level research agenda for digital transformation (DT). In this regard, we propose an extended definition of DT as "a socioeconomic change across individuals, organizations, ecosystems, and societies that are shaped by the adoption and utilization of digital technologies ." We suggest four lenses to interpret the DT phenomenon: individuals (utilizing and adopting digital technologies), organizations (strategizing and coordinating both internal and external transformation), ecosystems (harnessing digital technologies in governance and co-producing value propositions), and geopolitical frameworks (regulating the environments in which individuals and organizations are embedded). Based on these lenses, we build a multi-level research agenda at the intersection between the bright and dark sides of DT and introduce the PIAI framework, which captures a process of perception, interpretation , and action that ultimately leads to possible impact. The PIAI framework identifies a critical research agenda consisting of a non-exhaustive list of topics that can assist researchers to deepen their understanding of the DT phenomenon and provide guidance to managers and policymakers when making strategic decisions that seek to shape and guide the DT.
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Purpose: the purpose of the article is to substantiate the ecosystem approach to managing the interaction of an enterprise with educational and other organizations to provide it with the necessary human capital. Methods: this article is based on an ecosystem approach. A resource approach was used to substantiate the advantages of the professional and educational ecosystem. The method of situational analysis is used. The questionnaire method made it possible to evaluate the results of the ecosystem of a particular enterprise based on the expert`s opinion. The assessment of the structural model of management of the professional and educational ecosystem was carried out using correlation analysis. Results: the possibilities of an ecosystem approach to the formation of new organizational forms of interaction between subjects of the real sector of the economy and the education system in a dynamically changing external environment are shown. The content of the vocational education ecosystem is revealed as a kind of socio-economic, the key features of which are the specific composition of subjects and a special goal (creation of joint value) – reproduction of the enterprise human capital. The key objects of management of the vocational education ecosystem are highlighted: resources, processes, results and interaction of participants. An algorithm for evaluating the management system of the vocational education ecosystem of the enterprise is presented. The experience of the vocational education ecosystem of a knowledge-intensive enterprise is analyzed and practical recommendations for its development are given. Conclusions and Relevance : the approach to the management of the vocational education ecosystem through the analysis of key objects considered in the article has practical significance and allows us to give concrete practical recommendations for its improvement. Based on the results of the analysis of the objects of management of the vocational education ecosystem of the machine-building enterprise, the need to strengthen the relationship of enterprises with educational organizations that form the basis of human capital has been established. The key advantage of the vocational education ecosystem of the enterprise is the unhindered accessibility for employees of the enterprise in advanced training, the implementation of the principle of lifelong learning.
Although business-to-business (B2B) firms increasingly realize the benefits of belonging to a sound ecosystem, few studies examine how heterogenous actors contribute to the branding of such a higher-order system. Building on the ecosystem concept and integrative branding theory, this research offers an evolving three-stage process model to guide actors in restoring an ecosystem brand suffering from the lack of innovativeness and public disapproval. Using data from 51 informants in three key groups of actors: (1) citizens, (2) firms, and (3) authorities, as well as complementary sources, this study uncovers a nuanced pattern of interplay between the different ecosystem actors. The findings show that the restoration of an ecosystem brand is possible through (1) reframing, (2) blending, and (3) proacting. These stages are driven by a single-actor, dyadic, and triadic focus on sustainability-led innovation. The proposed model provides important insights for practitioners and scholars seeking to understand brand restoration of complex, multi-actor, adaptive systems.
Full-text available
In this White Paper, we explain why business ecosystems are more relevant then ever and define their nine core characteristics. Experienced practitioners illuminate these characteristics with examples from management practice.
Purpose Although there is a growing research stream on Performance Measurement and Management Systems (PMMS) in Ecosystems literature, current research offers limited theoretical insights into how PMMS deal with two types of strategies in uncertain ecosystems: ecosystem-based strategy – EBS (at the focal firm level) and ecosystem strategy – ES (at the ecosystem level). This study aims at identifying how PMMS are employed to deal with different types of strategies in uncertain ecosystems. Design/methodology/approach The authors employed an inductive, rich multiple case approach in five focal firms with platform ecosystems. Data collection involved multiple sources of information (primary and secondary data), combing retrospective and longitudinal perspectives. Data analysis combined replication and comparison logic with coding. Findings This study identifies four major distinctive dimensions of Ecosystem PMMS under uncertainty: (1) Integrative Performance (considering the different ecosystem actors’ performance), (2) Interdependence Performance (mutual, yet not necessarily convergent amongst ecosystem partners), (3) Regulative Performance (paradoxical in nature, having to cope with both flexibility and stability) and finally (4) Phased Learning Performance (non-linear). Research limitations/implications Our primary contribution is a new framework for PMMS literature: a performance measurement and management system for dealing with strategies in ecosystems. This framework enables managing performance regarding both types of strategies (EBS and ES) and their interplay in uncertain ecosystems. Practical implications The ecosystem management requires focal firms to measure and manage the overall ecosystem’s performance, and it varies according to the type of strategy adopted in each case. Our framework provides dimensions that guide firms to build and implement PMMS for an ecosystem consistent with the ES. Therefore, it may improve performance, especially in uncertain business contexts. Originality/value The findings enrich PMMS literature in an ecosystem context related to the ES in uncertain environments.
Market formation in sustainability transitions is characterised by deep and longstanding uncertainty. Albeit prior research provides a rich repertoire of theoretical frameworks on how incumbent firms and entrepreneurs deal with uncertainty, there is still scant literature on the role of the government in orchestrating uncertainty concerning market formation. By studying multiple cases involving an innovation agency and municipalities in Brazil, we propose a new framework composed of a distinguishing typology of uncertainties and patterns of actions regarding how the government can orchestrate the mitigation of these uncertainties in market formation in sustainability transitions. Government actors cope with configurational uncertainty through boundary experimentation, affiliation uncertainty by removing barriers, and interdependence uncertainty by setting long-term mechanisms (nurturing, facilitating and driving interdependence). Our framework suggests that government actors adopt strategies to increase their legitimacy in driving market formation under uncertainty and use epistemic regulation to mitigate hypes. Implications and opportunities for further research are also discussed.
Full-text available
Ecosystems-communities of interdependent, yet hierarchically independent heterogeneous participants who collectively generate an ecosystem value proposition-often emerge through collective action, where ecosystem participants interact with each other and the external environment. When such organizational forms are emerging, they require legitimacy to overcome the "liability of newness". Adopting a collective action lens and taking a legitimacy-as-process approach, we propose a process model of ecosystem collective action, where an orchestrator, complementors, users, and external actors together drive ecosystem legitimacy. We identify three key legitimation processes: discursive legitimation, performative legitimation, and ecosystem identity construction, and demonstrate how these three processes together facilitate the emergence of ecosystem legitimacy and reduce the liability of newness of emerging ecosystems.
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We explore value co-creation in ecosystems from three broad disciplinary perspectives: those of strategic management, service marketing, and information systems. These perspectives offer complementary insights into what is a complex phenomenon. We consider the premises, underlying theoretical considerations, insights, and contributions of each, identify commonalities and complementarities, and suggest directions for future research agenda.
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The emergence of digital platforms and ecosystems (DPE) as a venue for value creation and capture for multinational enterprises holds considerable implications for the theory and practice of international business. In this paper, we articulate these implications by considering the dual perspectives of cross-border platforms and ecosystems-as a venue for multifaceted innovation and as multisided marketplace-and focusing on three overarching themes at the intersection of DPEs and international business, that is, DPEs as affording new ways of internationalization, as facilitating new ways of building knowledge and relationships, and as enabling new ways of creating and delivering value to global customers. We explain specific DPE-related concepts and constructs that underlie these themes and discuss how they could be incorporated into existing IB theories in ways that would enhance their richness and continued relevance as well as their ability to better predict a multitude of emerging IB phenomena.
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Business-to-Business platforms provide connectivity and data exchange services and compete in several dedicated services, such as electronic invoicing (e-invoicing). Thus far, little research has examined the factors that influence firms’ choice among competing platforms: Which platform features matter, and in what proportion in the decision maker’s choice? To address this gap, we conduct an empirical study and triangulate with past theoretical explanations that have sought to account for the firm’s choice. Based on the analysis, we formulate a platform selection model that includes nine features: (1) reach, (2) total cost, (3) usability, (4) ease of system integration, (5) implementation capability, (6) platform support for service improvement, (7) service customization, (8) platform reputation, and (9) long-term sustainability. We apply conjoint analysis using firms’ selection data, collected from 282 firms that have recently made a purchase decision among e-invoicing platforms. All features except the vendor’s implementation capability are found to significantly influence the platform choice. Two features—usability and reach—dominate the choice and account for nearly 50 percent of the likely outcome. We use cluster analysis to examine the effect of firm size on firms’ preferences in platform features. As hypothesized, larger companies prefer interoperability, scale, and network effects, while smaller companies value local use efficiency and ease of use because they are more concerned with usability and cost. Our theoretical and managerial claims concerning platform choice highlight the difficulty of bootstrapping, the role of pricing and cost, the minor importance of implementation-related features, and the impact of longevity during platform contracting.
In this article, we provide a rationale for this Special Section on innovation and entrepreneurial ecosystems. We also present a summary of the papers presented in the Special Section, highlighting research questions, theories, data and methods, and principal findings and conclusions. A research agenda is also identified, involving multi-level research on agents, institutions, and regions on the context, process, and impact of innovation and entrepreneurial ecosystems.
Organizations are simultaneously embedded in inter-organizational networks and ecosystems, yet research on networks and ecosystems developed in isolation. The aim of this partial integration is to bring new energy into maturing research on organizational networks and greater structure to the burgeoning research on ecosystems. In this article, we underlie similarities and differences between networks and ecosystems; bring the ecosystems’ focus on modularity and complementarity at the forefront of inter-organizational research while enriching ecosystems scholarship with systematic applications of network analytic tools to map the patterns of component interdependencies.
Purpose The ecosystem perspective on innovation and business has emerged as the secret sauce of innovative organizations. While its theoretical foundations are premised on innovation system literature, the broad adoption of the ecosystem concept has resulted in conceptual ambiguity. The purpose of this paper is to tackle the ambiguous use of innovation ecosystem terminology and structure a conceptual frame for the field, identifying definitions of an innovation ecosystem and how the concept has been established in previous literature. Design/methodology/approach This paper examines the ambiguous use of terminology by reviewing the literature with bibliometric coupling and co-citation analysis by which thematic differences in ecosystem literature were identified. The study gathered the scientific publications from Thomson Reuters Web of Sciences Core Collection ( n =4,681) from 1990 to 2015. Findings Six major bibliometrically coupled clusters were identified, of which the three largest clusters are innovation system studies, regional innovation studies and technological innovation studies. In addition, further analysis shows an emerging cluster that is focused on ecosystems, having its roots in eight seminal papers. This ecosystem research cluster includes seven sub-clusters, such as innovation ecosystem studies, business ecosystem studies and studies focusing on ecosystem development. Research limitations/implications The authors’ approach highlights how a lot of recent ecosystem studies actually belong to previous, well-developed research streams. However, there is also a separate, emergent research stream that includes the innovation and ecosystem studies. As a research implication, the paper concludes by suggesting the research agenda for further studies. Originality/value Even though literature on innovation systems and ecosystems is extensive literature, no studies have captured the emergence of the ecosystems approach and its relation with the systems of innovation literature.