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THE ORGANIZATION OF
INNOVATION IN ECOSYSTEMS:
PROBLEM FRAMING, PROBLEM
SOLVING, AND PATTERNS OF
COUPLING
Stefano Brusoni and Andrea Prencipe
ABSTRACT
This chapter adopts a problem-solving perspective to analyze the
competitive dynamics of innovation ecosystems. We argue that features
such as uncertainty, complexity, and ambiguity, entail different knowl-
edge requirements which explain the varying abilities of focal firms to
coordinate the ecosystem and benefit from the activities of their suppliers,
complementors, and users. We develop an analytical framework to inter-
pret various instances of coupling patterns and identify four archetypical
types of innovation ecosystems.
Keywords: Innovation ecosystems; patterns of coupling; innovation
Collaboration and Competition in Business Ecosystems
Advances in Strategic Management, Volume 30, 167–194
Copyright r2013 by Emerald Group Publishing Limited
All rights of reproduction in any form reserved
ISSN: 0742-3322/doi:10.1108/S0742-3322(2013)0000030009
167
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INTRODUCTION
Innovation is an increasingly distributed and collective process involving a
variety of actors such as users, suppliers, universities, and competitors
(Freeman & Soete, 1997). Its distributed and collective features stem from
the information and knowledge requirements related to new products and
services – or their integration into solutions (Davies, 2004) – which require
the combination of scientific, engineering, and service knowledge (Hobday,
Davies, & Prencipe, 2005; Patel & Pavitt, 1997; Pavitt, 1998; Rosenberg,
1982). Scholars have proposed the construct of innovation ecosystem to
capture the cross-industry and cross-country complexity of the innovation
process (see, e.g., Adner, 2006; Iansiti & Levien, 2004; Moore, 1993).
Similar to biological ecosystems, innovation ecosystems are inhabited by a
variety of different species of actors who share their fate (Moore, 1993).
Species operate cooperatively and competitively to create value – that is,
they develop and deliver new products, and to capture value – that is, they
satisfy customer needs (Adner & Kapoor, 2010). Innovation characterizes
the ecosystem in constituting the locus around which species coevolve, and
acts as a catalyst for the ecosystem’s evolution (Moore, 1993).
Borrowing from biology, Iansiti and Levien (2004) identify specific
business ecosystem features, productivity,robustness, and niche creation,to
illustrate its status and the role of its actors, for example, keystone species.
Productivity is the innovation ecosystem’s ability to consistently transform
new technologies into improved and new products. Robustness measures the
ability of an innovation ecosystem to survive disruptions caused by
unforeseen technological or socioeconomic change; an ecosystem should
also exhibit variety to support a diversity of species. Niche creation relates to
its capacity to applying emerging technologies across new product domains.
Keystone species organizations lead to the creation of new niches, enhancing
the performance of niche organizations, and eventually increasing overall
ecosystem robustness.
Although extant research provides some valuable insights into the specific
features of innovation ecosystems, we know relatively little about the
microprocesses underpinning the functioning of different ecosystems.
Focusing on the effects of the external challenge of innovation on the
ecosystem’s focal organization, Adner and Kapoor (2010) discuss how the
different location of innovation (i.e., upstream vs. downstream), and its
content (i.e., component innovation vs. complementary innovation) affect
the focal firm’s competitive position in the ecosystem. They find that the
upstream location of these challenges has a positive effect on the focal
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organization’s performance, but a downstream location does not. Adner
and Kapoor (2010) emphasize the interplay between technical and beha-
vioral uncertainty in balancing value creation and value appropriation.
In this chapter, we build on this intuition and focus on the nature and
strengths of the relationships between the focal firm and other actors within
the ecosystem. We draw on the concept of coupling (Orton & Weick, 1990)
to explain the microprocesses that enable (or not) firms to solve problems of
varying degrees of difficulty, in innovation ecosystems populated by
functionally related though often loosely coordinated actors.
The main argument is that the features of the problem that require to be
framed and solved entail different knowledge requirements and specific
patterns of coupling among the ecosystem’s actors. We analyze the focal
organization, which orchestrates the dynamics of the interactions among the
complementary and supporting actors in the ecosystem.
We make two assumptions. First, we conceive focal organizations as
problem-framing as well as problem-solving institutions which arrange and
generate knowledge to make sense of and to solve problems to promote
innovation and capture its value (e.g., Brown & Eisenhardt, 1997; Dosi &
Marengo, 1994; Nickerson & Zenger, 2004; Wolter & Veloso, 2008). Inno-
vation ecosystems provide the context for the focal organization’s decision
about which problems to solve, how to select and deploy the relevant capabi-
lities, and how to implement the solution(s) identified. Second, we conceive
innovation ecosystems as complex entities comprised of a group-related actors
whose combined knowledge and capabilities evolve (e.g., Christensen &
Rosenbloom, 1995;Christensen, 1997;Christensen, Verlindem, & Westerman,
2002). Innovation ecosystems differ in the coupling among their actors, that is,
in the strength and intensity of the linkages among them. Different types of
coupling entail different types of approaches to knowledge generation.
The contribution of this chapter is twofold. First, we argue that the
emergence of different types of ecosystems is related to the knowledge
requirements imposed on the focal organization by the type of problem
requiring resolution. We identify ambiguity, complexity, and uncertainty as
key features of the problems that the focal organization must solve in order
to carry out its strategic and operational tasks. Second, uncertainty,
complexity, and ambiguity lead to the emergence of increasingly difficult
problems which affect the degree of coupling among the ecosystem’s actors.
The degree of their coupling has an impact on the structure of the ecosystem
that develops around the focal organization.
The chapter is organized as follows. The next section discusses the
concept of coupling, followed by a section analyzing the effects of
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ambiguity, complexity, and uncertainty on the manifestation of different
types of coupling in ecosystems. The fourth section discusses the notion of
coupling as process and structure in innovation ecosystems. The final
section provides some conclusions.
Distinctiveness, Responsiveness, and Organizational Coupling
Organizational Coupling: Distinctiveness and Responsiveness
Orton and Weick (1990) define coupling, that is, the strength and intensity
of the linkages among the nodes in a network, in terms of the two constructs
of responsiveness and distinctiveness. Distinctiveness relates to the property
in the components of a system to retain their identities. For instance, if we
understand a symphony orchestra as a system, the violinist retains her
identity and her distinctiveness within the system allowing her to play her
musical part. Responsiveness relates to the property of the system
components to maintain a degree of consistency with each other, even in
a changing environment. The musicians in a symphony orchestra are
responsive – they play/perform together to translate the written music into a
sound performance. The interaction between distinctiveness and respon-
siveness determines the type of coupling. If responsiveness prevails, the
system is described as ‘‘tightly coupled’’; if distinctiveness prevails, the
system is described as ‘‘decoupled.’’ If distinctiveness and responsiveness are
quite well balanced, the system is described as ‘‘loosely coupled’’ (Orton &
Weick, 1990, p. 205) (Table 1).
We assume that systems must be responsive in order to react to
environmental stimuli while simultaneously promoting changes in the
environment. Responsiveness may be automatic or enacted. Responsiveness
is achieved automatically when the interfaces across organizational units and
subunits are given, and changes are permitted only within a predefined range.
Building on Henderson and Clark’s (1990) seminal paper on architectural
Table 1. The Definition of Loose Coupling.
Distinctiveness
No Yes
Responsiveness No Non-coupled system Decoupled system
Yes Tightly coupled system Loosely coupled system
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innovation, research on modularity proposed that product-level interfaces
also determine organizational-level interfaces (Baldwin & Clark, 2000;
Jacobides, 2005; Jacobides & Winter, 2005; Langlois & Robertson, 1992;
Sanchez & Mahoney, 1996). Responsiveness is achieved simply by respecting
the design rules that define which functions are performed within each
module, the performance range of the module, and what information each
module should receive in order to function, and to transmit to enable other
modules to function. This type of responsiveness assumes that the external
environment varies within a predictable range, which enables the focal
organization to retain a degree of control and coordination with minimal
direct intervention (Brusoni, Jacobides, & Prencipe, 2009; Brusoni &
Prencipe, 2006, 2009, 2011; Ceci & Prencipe, 2008).
However, too much reliance on the automatic organizational responsive-
ness enabled by standardized interfaces can be dangerous for incumbents
(Brusoni, Prencipe, & Pavitt, 2001; Henderson & Clark, 1990; Wolter &
Veloso, 2008). When the extent of technological change becomes difficult to
control or to predict, then reliance on the coordination enabled by stand-
ardized interfaces becomes a source of problems (e.g., Brusoni et al., 2001;
Nickerson & Zenger, 2004).
Within innovation ecosystems, firms must be able to develop heuristics,
routines, and procedures to change their internal and external patterns of
communications, command, and information filters. We describe this as
enacted responsiveness. Responsiveness is enacted when the interfaces
among units are not precisely defined ex ante and, therefore, require active
management to ensure consistency. Interfaces are characterized by a
dynamics of change according to which variations in a unit interface entail
unpredictable and/or ambiguous variation in one or more unit interfaces.
These dynamics of change, therefore, require active direction to maintain
some degree of consistency across organizational units, and with the
external environment. Enacted responsiveness requires managerial attention
(Ocasio, 1997) and explicit managerial authority (Radner, 1992).
To return to the example of the symphony orchestra, the violinist can be
responsive to the other violinists in the orchestra by playing the notes
written on the musical score, which in some cases is enough, or can adapt
the performance by following the directions of the orchestra conductor or
soloist, which will be especially necessary for tricky musical passages or
interpretations. The former performance is automatic responsiveness; the
second is enacted responsiveness, which is the focus of this chapter. Enacted
responsiveness is necessary each time units have to cope with interdependent
and unpredictable (as opposed to modular and predictable) interfaces
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(Christensen et al., 2002, p. 959). We argue that achieving this type of
responsiveness is particularly difficult in ecosystems where the focal firms
cannot directly control or influence the activities of their complementors
(e.g., Adner & Kapoor, 2010).
The Determinants of Distinctiveness and Responsiveness and their Impact
on Ecosystems
This section discusses why and how the interplay between distinctiveness
and responsiveness determines the emergence of specific types of coupling in
innovation ecosystems. We focus on focal organizations that rely on inno-
vation ecosystem units to frame and solve problems. We discuss ambiguity,
complexity, and uncertainty, and how they define different and decreasingly
difficult problems for the focal organization.
Ambiguity. Ambiguity refers to lack of meaning in a situation and the
resulting inability to interpret or to make sense of it (Weick, 1969). Zack
(2001) argues that ambiguity relates to the state where the decision maker is
not able to formulate questions. Ambiguity is a central concept in the
literature on organizational decision-making. According to March (1978,
1987) ambiguity is a pervasive feature of organizations. March (1987)
lamented the fact that in decision theory, management science, and
microeconomics the study of organizational choice focused on optimization
at the expense of given preferences and alternatives, and that little effort was
devoted to generating alternatives or developing choices. Nevertheless,
situations characterized by ambiguity are common in the literature.
Ambiguous situations are unclear and vague in the sense that the decision
maker does not have the interpretive knowledge required to frame and define
them. The knowledge requirements in ambiguous situations are related to
sense-making efforts, generation of alternatives and discovery of possible
solution paths, rather than demonstration, exploitation, and problem solving.
The challenges are related to the absence of an accepted meaning or the
presence of multiple contradictory meanings.
Research has identified the difficulty inherent in situations characterized
by an absence of criteria against which to rank options or to define the
options available. This has resulting in a large body of evidence on the
processes used by top and middle management to define the available
options rather than to make a decision. For example, Henderson and
Clark (1990) highlight the danger posed by radical or architectural
innovations, for incumbents, which try to accommodate new problems in
old problem-solving patterns embodied in tacit and rigid organizational
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structures and communication channels. Garud and Rappa (1994) docu-
ment the slow emergence of new technological frames in the case of cochlear
implants. Tripsas and Gavetti (2000) analyze the problems faced by
Polaroid in trying to adapt its business model to the digital imaging
paradigm. The technological capabilities existed, but top managers failed to
reframe the problem and to move away from established ways of generating
revenue (by selling film) to a model where cameras did not require film.
Prencipe (2001) discusses the slow and path-dependent process that enabled
assembling firms to nurture in-house technological capabilities in order to be
prepared to unexpected changes in the value chain. Acha (2004) suggests
that technology framing directly influences senior managers’ interpretive
systems, which, in turn, are fundamental for directing the organization’s
current position and future strategic opportunities. Kaplan (2008)
investigates how the interaction among different frames generates the
context for competition within organizations struggling to make sense of a
new technology and the business opportunities it affords.
Ambiguous problems have implications for the interplay between
distinctiveness and responsiveness and, therefore, affects organizational
coupling. The examples presented above suggest that in order to solve ambi-
guous problems, organizations must create contexts that allow individuals to
talk, interact, disagree, and try out their ideas. The main organizational
challenge is to provide a socially cohesive yet intellectually diverse set of
individuals with an interactive, face-to-face, information-rich environment.
Hence, ambiguity is at odds with the idea of specialized, compartmentalized
systems. Kogut and Zander (1992, 1996) argue that organizations as opposed
to markets, provide a ‘‘context of discourse and coordination among
individuals with disparate expertise’’ (1996, p. 503), which, in turn,
constitutes the basis for the development of a distinct organizational identity
necessary for the creation of socially shared knowledge. This is a major
responsibility for focal organizations embedded in broad ecosystems and is
highlighted by Adner and Kapoor’s (2010) discussion of complementors.
Adner and Kapoor (2010) suggest that the location of innovation along the
value chain can have different effects on the focal organization. Upstream
innovations introduced by or with the involvement of suppliers, are less
problematic for the focal organization than downstream innovations
introduced by complementors. The latter are not linked directly to the focal
firm, may not share the same knowledge base, and have no supplier
relationship. However, their innovative activities are critical to the viability of
the focal organization, which has no control over or clear understanding of
them, or responsiveness to them. The problem is rendered ambiguous
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because it lacks any level of cognitive overlap required to understand the
nature of the problem and to frame it. Diversity may be a source of novel
ideas, but problem framing requires some reconciliation among the
distinctive elements of different strategies and ideas. Garud and Rappa
(1994) and Kaplan (2008) discuss the time-consuming efforts involved in the
emergence of a common frame of reference required to solve a complex
strategic problem. Cacciatori and Jacobides (2005) and Cacciatori (2012)
argue that non-emergence of common cost accounting categories can prevent
an integrated organization from developing functional routines for design,
build, and maintenance related to complex civil engineering structures.
We thus propose:
Proposition 1. Increased ambiguity will require decreased distinctiveness
and increased responsiveness.
In our view, ambiguity requires tightly coupled innovation ecosystems
comprised of strong focal firms able to coordinate the activities of suppliers
and complementors. For example, Rolls-Royce Aircraft Engines, one of the
world’s largest aircraft engine manufacturers, relied on a tightly coupled
structure to develop a path-breaking technology (the wide chord fan blade).
Rolls-Royce’s in-house capabilities, accumulated over time, provided a base
for its learning processes and promoted the framing and enacting of a
radical breakthrough. The role of the firm’s frame of reference and
especially the underlying learning processes that led to changes to it, were
crucial for the continuous introduction of innovative technological solutions
(Lazonick & Prencipe, 2005; Prencipe, 2001). Pisano (2006) argues that in
the biopharmaceuticals industry, the seeming lack of progress in pushing
new biotechnology-based drugs through the pipeline, to some extent can be
explained by a lack of knowledge integration processes in the industry. The
concentration on specialization and division of labor does not provide the
right context for the integration of specialized knowledge to produce novel
products. Thus, biopharmaceuticals, as an organizational system, is char-
acterized by excessive distinctiveness. In the tire manufacturing sector,
Brusoni and Prencipe (2006, 2011) show that the successful introduction of
radical innovation is facilitated by the presence of a strong, tight, and
cohesive group of specialists working together to redefine the technology
and its associated business model.
In vaccinology, the recent evolution of anti-HIV vaccines demonstrates
increasing integration in response to ambiguity. The treatment or
prevention of HIV-AIDS is surrounded by huge scientific ambiguity. The
basic science is still weak and, although a number of therapies are available,
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scientists engaged in trying to develop a vaccine are faced with a very
difficult problem: there is no animal model available and nobody has
survived the disease,
1
and the virus mutates rapidly. In response to these
challenges, the International AIDS Vaccine initiative (IAVI) was established
in 1997 to advance the search for a vaccine. IAVI originally was organized
according to the traditional nongovernmental organization (NGO) model,
to raise money, to redistribute it to researchers, to evaluate results, and to
raise more money. According to IAVI sources, this model was not successful
and there was a rapid shift toward a more proactive, integrating role to shift
the research agenda, define the boundaries of the field, fill the gaps in the
ongoing research, and channel funds toward neglected subfields (e.g.,
Chataway, Brusoni, Cacciatori, Hanlin, & Orsenigo, 2007). The shift from a
brokering to a proactive ‘‘integrating’’ role was a response to the need to
coordinate developments from specialized subfields in a context of extreme
scientific and policy ambiguity.
Complexity. Simon (1969, p. 195) defines a complex system as:
one made up of a large number of parts that interact in a non-deterministic way. In such
systems, the whole is more than the sum of the parts, not in an ultimate, metaphysical
sense, but in the important pragmatic sense that, given the properties of the parts, and
the laws of their interaction, it is not a trivial matter to infer the properties of the whole.
Simon (1976) proposed methods to characterize system complexity:
cardinality, that is, number of components comprising the system;
interdependence among components; decidability; and information content
in relation to the variety of the system components.
2
Complex problems compared to ambiguous problems demand specific
and different knowledge. In the case of a complex problem, the firm knows
its overall structure but is unable to compute an ‘‘optimal’’ solution. The
knowledge requirements of complexity arise from the limits of computa-
tional ability to deal with a combination of: (a) plurality and variety of
elements; (b) interactions among system elements at various levels (i.e.,
among subsystems, between the whole system and the subsystems); and
(c) the unpredictability of the interactions among components.
Work on modularity discusses the implementation of an organizational
and product design strategy aimed at reducing complexity by introducing
standard interfaces. Monteverde (1995), in the context of the semiconductor
industry, discusses unstructured technical dialogue and its impact on vertical
integration decisions in the design of digital memory and analogue products.
The unpredictability of the interactions between product and process design
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led to an unstructured technical dialogue that called for vertical integration.
Sanchez and Mahoney (1996) discuss the implications of modularity in
various industries, to illustrate product design strategy aimed at simplifying
interaction patterns across ranges of known components. Hoetker,
Swaminathan, and Mitchell (2007) establish a link between product
modularity and the ability of firms to reconfigure their structures and
Tiwana (2008) argues that technological modularity can substitute for
explicit managerial control. Cabigiousu and Camuffo (2012) claim that a
stable product architecture reduces information-sharing needs.
The relationship between product modularity and organizational
structure is difficult to disentangle. Complexity is a multifaceted concept
that involves several dimensions. Cardinality, interdependence, and
unpredictability have distinct effects on organizational coupling: an increase
in cardinality induces increased distinctiveness, while higher levels of
interdependency and unpredictability require increased responsiveness.
The increased number of components in products requires dedicate design
and production resources. This applies also if the bodies of knowledge
underlying the components increase, which requires that organizations
continuously monitor, absorb, and eventually integrate ever more technol-
ogies (e.g., Brusoni et al., 2001). We suggest that an increase in disti-
nctiveness would be a better solution for this situation. Other things being
equal, a decentralized organizational system would allow a broader and
deeper understanding of the individual scientific and technological disci-
plines. For example, when the breadth of the knowledge base underpinning
a certain product market increases, parallel design becomes more viable and
more efficient than sequential design (Clark & Fujimoto, 1991). Reliance on
a wide network of specialized suppliers allows organizations in complex
environments to more easily reconfigure by exploiting the advantages pro-
vided by technological modularity (Lorenzoni & Baden-Fuller, 1995;
Lorenzoni & Lipparini, 1999; Hoetker, 2006). Individual organizational
units can work independently of and concurrently with other units and new
organizations can be added to the overall system when new and potentially
useful components and functionalities are introduced. Tiwana (2008) argues
that increasing interfirm modularity (i.e., distinctiveness) lowers the need for
interfirm knowledge sharing.
Thus, with reference to complexity as cardinality (i.e., the number of
elements of the problem), we propose that:
Proposition 2a. Increased (decreased) problem complexity will require
increased (decreased) distinctiveness.
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If interdependency and unpredictability increase, coordination becomes
more difficult. High levels of interdependency mean that a change to the
design of one component will require changes in the design of other
components in the system. Unpredictability may add to the complexity of
problem. In modular products such as Baldwin and Clark (2000) and
Langlois and Robertson (1992) describe, the components are numerous
and highly interdependent. However, their interdependencies are predictable
and disintegration is a viable organizational strategy. Unpredictable
interdependencies render system behavior similarly unpredictable. Thus,
the activities of each organizational unit dedicated to component design,
require active coordination. Responsiveness must increase in order to
accomplish objectives and ‘‘unstructured technical dialogue’’ (Monteverde,
1995) becomes necessary. The findings from micro-level studies of modular
technologies do not show consensus that modularity leads to a sharp
decrease in the reliance on hierarchies. For example, Tiwana (2008) notes
that, in software projects, modularity leads to a reduction in process control,
but an increase in outcome control. Hoetker’s (2006) study of the evolution
of the notebook industry finds no evidence of a positive relationship
between technological modularity and decreasing hierarchical control or
coupling. Staudenmayer, Tripsas, and Tucci (2005) find that firms involved
in new software development create specific organizational processes to
manage product interdependencies. For instance, members of software
development teams interact more frequently – on an ad hoc basis – to
achieve effective adjustment to ongoing changes. In the case of Lucent,
Staudenmayer et al. (2005, p. 314), describe how:
If two different teams or individuals caused a system ‘‘crash’’ upon reintegration they
communicated directly with each other to negotiate a solution – all dependencies were to
be pursued person to person and not via proxy or third party yInterestingly, Lucent
found that most interdependencies were only sporadic and not consistent.
Staudenmayer et al. (2005) also find that the software house, Red Hat, set
up longer term development projects not connected with a specific product
release in order to deal with the problem of coordinating systemic product
upgrades, and the firm’s partners developed ex-ante solutions to deal with
major product changes:
The Red Labs teams typically had 5–10 internal developers, 10–20 active external
developers representing different firms’ (and even individuals’) interests, and 100–200
less active external members whose primary roles were tracking what was happening,
providing limited input, and developing small pieces of the products/features.
(Staudenmayer et al., 2005, p. 314)
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Similarly, the case discussed by Li and Garnsey (2013) in this volume,
describes complexity related to broad patterns of interdependencies among
organizations engaged in developing tools to treat tuberculosis. Li and
Garnsey (2013) describe an ecosystem-level innovation that relies on a
mature technology (the drug), but required development of a new set of
relationships among innovators, distributors, and complementors to faci-
litate diagnosis and guarantee affordability in base-of-the-pyramid markets.
They focus upon the progressive development of a new supporting infra-
structure – partly technological (i.e., the GenXepert platform), partly insti-
tutional (i.e., the Foundation for Innovative New Diagnostics (FIND)) –
that enabled the focal organization to reorganize the value chain around its
core technology, through a series of interventions (or enacted responsive-
ness), to realign incentives and capabilities, and enable the distribution of an
innovative medical technology in some of the world’s poorest countries.
Thus, with reference to complexity as interdependencies, we propose:
Proposition 2b. Increased (decreased) complexity requires increased
(decreased) enacted responsiveness.
Propositions 2a and 2b propose new innovation ecosystems characterized
by both distinctiveness and responsiveness, that is, loosely coupled systems.
This conceptualization of loose coupling is consistent with Orton and
Weick’s (1990) definition, but differs from the understanding in the
modularity literature. Sanchez and Mahoney (1996) describe modular
networks as loosely coupled systems; in our view, these should better be
described as decoupled systems characterized by automatic (i.e., embedded
in the technical interface) responsiveness. We need a finer grained definition
of loose coupling to allow analytical clarity and to explain some apparent
paradoxes in analyses of the relations between different types of innovation
and vertical (dis)integration (e.g., Wolter & Veloso, 2008).
Uncertainty. Uncertainty refers to lack of information required to predict
the state of a context (Kahneman, Slovic, & Tversky, 1982; Leblebici &
Salancik, 1981; Tosi, Aldag, & Storey, 1973). Uncertain problems are
characterized by well-framed problems, whose solutions require extensive
searches of information across alternative sources. Uncertain situations can
be identified in a variety of empirical settings. For example, computational
chemistry firms working on behalf of pharmaceutical companies, identify
compounds whose properties have been predefined by the pharmaceutical
company’s research team. The advantage of small, specialized providers of
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computational chemistry services is their ability to search extensive
databases of basic components and their building blocks, while the ‘‘target’’
is defined by the client based on the strengths of its research team (Orsenigo,
Pammolli, & Riccaboni, 2001). Similarly, in Open Sources Software
(OSS) environments, crowds of independent programmers contribute to
making continuous improvements to source code. Nevertheless, and despite
Raymond’s (1999) famous claim that ‘‘given enough eyeballs, every bug is
shallow,’’ successful OSS environments, such as Linux, rely on well-established
frames defined by well-guarded kernels that are unlikely to be modified. Thus,
not all bugs are ‘‘equal’’: the environment is uncertain because complex
operating systems require continuous maintenance and updating, but the
overall frame is unambiguous and is defined and guarded by a restricted group
of programmers to prevent forking (Narduzzo & Rossi, 2005).
Knowledge requirements entailed by uncertainty derive from the lack of
information about future states of the environment. According to
information theory, information and uncertainty are inversely related
(Newell & Simon, 1972; Shannon & Weaver, 1949). Information theorists
argued that data, such as cues and message units, have the potential to
reduce the level of uncertainty. However, relevant cues must be sifted out of
noise in order to become information that can be used to reduce uncertainty.
Decision makers need to identify organizational solutions for the acquisition
of data and transform them into information to improve the accuracy of
their predictions (Gifford, Bobbit, & Slocum, 1979). The absence of
complexity and ambiguity allows decision makers to rely on a well-
established and familiar model in defining the problem to be solved.
Although information gathering may be difficult and time-consuming, it
does not require development of a novel problem frame.
In uncertain situations, reliance on a wide and diverse network of
information providers may be advantageous. Hansen (1999, p. 82) studies a
sample of new product development projects in microelectronics showing
that weak interunit ties helped the project team to search for useful
knowledge in other subunits, but impeded the transfer of complex
knowledge. Granovetter (1973) highlights that the strength of weak ties
relies on the ability to search for and acquire diverse information. Similarly,
Sturgeon (2002, p. 455) argues that the emergence of modular networks in
the international personal computer (PC) and microelectronics industry is
related to the possibility of exploiting ‘‘distinct breaks in the value chain
[since they] tend to form at points where information regarding product
specifications can be highly formal ylinkages are achieved by the transfer
of codified information.’’
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On this basis, we argue that uncertain problems can be managed more
appropriately by systems that retain a high level of distinctiveness.
Responsiveness (at least the enacted type we discuss here) is not required
because the problem solving builds on an accepted problem frame.
Coordination can be achieved by respecting the interfaces established
among the known subproblems. According to Orton and Weick (1990),a
decoupled system is a system where distinctiveness prevails. We argue that
decoupled systems are more appropriate for gathering new data as long as
they can be transferred efficiently along established channels. In decoupled
systems, individual units become independent sensors that monitor different
(in terms of their nature), distant (in terms of disciplines, e.g., electronics
and fluid dynamics), and dispersed (i.e., geographically) sources with the
aim of acquiring new data that must be transformed into information.
Therefore, uncertainty paves the way to the emergence of decoupled
organizational systems since resolving uncertain situations requires the
collection of more and newer data. A good example is the recent shift from
closed to open innovation to respond to increasing uncertainty in consumer
goods markets (Chesbrough, 2003). For instance, Procter & Gamble shifted
from a tightly coupled approach to R&D – characterized by global in-house
research facilities that hired and retained the world’s best talents in scientific
and technological disciplines, to a decoupled approach they called ‘‘Connect
and Develop.’’ Connect and Develop relies on external connections with
individuals, suppliers, and private and public research laboratories that
experiment with new products and technologies in new markets (Houston &
Sakkab, 2006). Thus, Procter & Gamble have overcome the not-invented-
here-syndrome and revised its research aim, which is to identify promising
new product and process ideas throughout the world, and exploit their
development, manufacturing, and marketing capabilities.
Decoupling is important not only for the acquisition of information but
also for decision making. Decoupled systems are likely better suited to
increasing variety through the bottom-up process of information gathering
and to promoting top-down decisions. Bourgeois and Eisenhardt (1988)
found that in the high velocity environment of the microcomputer industry
in the late 1970s and early 1980s, those firms that achieved better
performance were characterized by greater delegation of decision-making.
In the best performing firms, the power distribution within top management
teams leans toward functional executives. Similarly, Nickerson and Zenger
(2004) argue that markets and decentralized forms of organizing more
generally, are better able than authority-based organizations to solve
decomposable problems through incremental (directional) search processes.
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On this basis, we propose:
Proposition 3. Increased uncertainty requires increased distinctiveness
and decreased responsiveness.
Uncertainty, therefore, leads to decoupled innovation ecosystems.
Although the emergence of decoupling is related to the presence of
uncertain problems with known structures, there may be other factors, with
organizational implications, that might affect the structure of the innovation
ecosystem. There are at least three main barriers to the emergence of a
decoupled, decentralized ecosystem: appropriability regime; munificence of
the ecosystem; and safety.
Appropriability refers to the ability of the organization to capture the
financial returns from the introduction of innovation (Teece, 1986).
Appropriability regimes vary in strength depending on the effectiveness of
the prevailing intellectual property rights protection system, for example,
patent and trademark system. A strong appropriability regime enables
distinctiveness to prevail, allowing some relaxation of the symbiotic
relationships among firms in the same ecosystem. For example, Arora and
Gambardella (1994) argue that a strong intellectual property rights regime
based on patents, copyright, or embodiment of knowledge into saleable
artifacts or services, can lead to the emergence of efficient technology
markets where specialized firms can compete with the incumbents on the
basis of superior skill in specific steps of the innovation process. Arora and
Gambardella argue that the increase in the numbers of contract research
organizations in biopharmaceuticals, independent software vendors, spe-
cialized engineering firms, and ‘‘fabless’’ semiconductors companies are all
examples of the increasing specialization and division of labor enabled by
knowledge codification, and by abstract categories which makes it easier to
define and enforce ownership of the intellectual property in knowledge
modules. This reduces the benefits of vertical integration even in innovative
contexts. In this context, specialization and thus distinctiveness are
prioritized over responsiveness.
On the other hand, a weak appropriability regime calls for more respon-
siveness in the ecosystem. Issues of access and control require management
through secrecy and close interaction, that is, higher degree of coupling, in
order to capture the financial revenues from innovation. A higher degree of
coupling along the supply chain, for instance, would enable organizations to
control each step of the innovation process. In particular, weak appro-
priability increases ambiguity; it renders, for example, the connection
between investments in innovation and profitability unclear and fuzzy. This
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discussion is related of course to the very important distinction between
technological and behavioral uncertainty (Adner & Kapoor, 2010). The
former is informed by the problem characteristics and the latter about the
no less important appropriability considerations. Hence, a weak appro-
priability regime is likely to push the ecosystem toward increased coupling
(irrespective of the nature of the problem), and to increase the need for
responsiveness and tighter forms of coupling.
Tushman and Anderson (1986, p. 145) define munificence as the ‘‘extent
to which an environment can support growth’’ and argue that ‘‘environ-
ments with greater munificence impose fewer constraints on organization’’
to grow (Tushman & Anderson, 1986, p. 445). Following Tushman and
Anderson, we argue that munificence or the generosity of the ecosystem, is
conducive to less enacted responsiveness and to decoupled organizational
systems. Environments characterized by greater munificence have a higher
degree of freedom to choose the patterns of growth identified by their
distinctive units and the need for enacted responsiveness is reduced. The
resource slack associated with a munificent environment allows greater
tolerance of mistakes by the organization.
A munificent environment offers the organization higher and safer growth
prospects, enabling the exploitation of the advantages of specialization and
reducing the risks (e.g., poor appropriability, coexistence of different
standards, barriers to entry, etc.). For example, in the early 1980s, the PC
market was growing rapidly. IBM, which entered the race late, relied on a
wide network of suppliers for its entry: most components were bought from
specialized suppliers (Microsoft and Intel which provided the operating
system and microprocessors). This strategy backfired and IBM realized that
it had overestimated the continuing advantage of the IBM brand. Its
customers moved away from its OS/2 preferring to buy cheaper and more
modular PCs produced by IBM’s competitors. This unintentionally created
a completely different ecosystem.
More recently, the Internet bubble led to massive entry into various
segments of the information and communication technology industry: based
on skyrocketing stock markets, thousands of small, specialized, Internet-
based firms entered the market and something similar happened in
biotechnology. Now, both sectors are experiencing a process of rapid, and
brutal, reorganization: there are too many firms, that are too small, and are
incapable of delivering new products or services to the market, which does
not represent a viable industry structure. Most revenue-generating biotech
firms have achieved this success by developing software to support the
drug development activities of the old pharmaceutical giants. Very few
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biotechnology firms have grown to become fully fledged pharmaceutical
companies (Orsenigo et al., 2001; Pisano, 2006). While specialization (and
thus distinctiveness) has led to a phase of very rapid exploration and
learning sustained by an over-optimistic stock market, the development of
products and services requires tightly coupled organizations. Hence, the
current trend toward reintegration and enacted responsiveness.
Another source of constraint for decoupled ecosystems is safety. Who will
be responsible in the case of a malfunction? If our computers do not work,
we can hardly blame Intel. If a jet engine fails, it is often the airline (not the
engine manufacturer) that attracts the criticism. Similarly, in the automotive
sector, failures become the responsibility of the assembler, irrespective of
which supplier manufactured the faulty part. In relation to the nature of the
problem, the automotive industry remains tightly coupled, with relatively
few entrants able to challenge the big players in particular because of safety
issues (Sako, 2003). This applies also to the aeronautic industry. Although
characterized by increasingly global supply chains (e.g., Kotha & Srikanth,
2013), it is still tightly coupled in relation to the allocation of responsibility
in the case of a malfunction – as exemplified by the recent case of the
Dreamliner.
DISCUSSION: MODELS OF COUPLING IN
BUSINESS ECOSYSTEMS
Because ambiguity, complexity, and uncertainty represent problems involving
knowledge requirements, we would suggest that they affect the choices that
focal organizations have to make about distinctiveness and responsiveness to
the framing and solving of the problems within an ecosystem. Distinguishing
the effects of ambiguity, complexity, and uncertainty on distinctiveness and
responsiveness suggests the need to discuss the emergence of different coupling
patterns in innovation ecosystems: ambiguous problems call for tightly coupled
ecosystems; complex problems call for loosely coupled ecosystems; and
uncertain problems call for decoupled business ecosystems. This distinction
complements and extends research on strategy and organizational theory by
addressing some empirical and theoretical gaps (e.g., Wolter & Veloso, 2008).
Coupling as Process
The proposed framework also explains the process of evolution of eco-
systems from decoupled, through tightly coupled to loosely coupled
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structures. Orton and Weick’s (1990) dialectical definition of coupling is
inherently processual. In considering the introduction of radical innovations,
it is important to distinguish between two phases. The first phase of
exploration and information gathering, when the focal organizations rely
on a broad network of external, specialized organizations to search and
map the territory. The second phase is directed to the integration of new
knowledge in new products to provide a solution to the problem. This
phase calls for tight coupling, that is, structures characterized by close
cooperation and cohesion that facilitate the development of a common
organizational code necessary to perform efficiently unstructured technical
dialogue (Monteverde, 1995) and, therefore, to frame the problem and
enact its solution. However, tight coupling is a temporary feature of
ecosystems. Once a code is in place and ambiguity is reduced, innovation
ecosystems are likely to evolve as loosely coupled structures, and focal firms
can reduce their direct interventions in the activities of their suppliers and
complementors.
Research on the emergence of technological platforms (Boudreau &
Hagiu, 2009; Gawer, 2009) provides new insights into the evolutionary
processes of ecosystems and how focal firms struggle to establish themselves
as major bottlenecks in these systems. A platform is meant to provide a
context for suppliers, customers, and complementors to interact with no
need for explicit coordination by the focal firm so long as these actors obey
some rules. Platforms enable automatic responsiveness instead of enacted
responsiveness, which reduces the scope of coordination efforts of the focal
firms. Once a platform is in place, focal firms can enjoy the benefits of
distinctiveness without the need for direct involvement in developing
products, and coordinating day-to-day activities. Ultimately, innovation
activities can be delegated to complementors and users.
Most platforms are rooted in the recent evolution of the microelectronics
industry and the emergence of the Internet as a platform enabling online
transactions. For example, in the mobile phone sector, focal firms competed
to establish their own platforms (West & Mace, 2010). Platforms include a
variety of components, such as hardware, software, network infrastructure
and, more recently, content, that is, prerecorded and entertainment,
information such as news and sport, and applications such as games (West
& Mace, 2010). Hence, platforms are a coordination tool that enables other
ecosystem actors to make choices within a given range. Firms can conceive
platforms in different ways. While Nokia, Sony Ericsson, and others relied
on disintegrated platforms, that is, they separated the supply of key
components from hardware sales, Apple pursued an integrated platform
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that included the operating system, hardware, built-applications, and online
services, though still enabling suppliers to develop apps and components
(West & Mace, 2010). The chapter by West and Wood (2013) in this volume
discusses the performance implications of different design decisions (in
relation to openness vs. closedness of the underlying platform) in the case of
the operating systems for mobile telephony.
Instances of these processes occur also in sectors characterized by the
absence of open or proprietary platforms. Brusoni et al. (2001) describe the
technological evolution of aircraft engine control systems and their
organizational implications. Since the 1970s the engine control system has
been characterized by a radical shift in its underlying technologies: from
hydromechanics to digital electronics. In the 1950s, aircraft engine control
systems were based on hydromechanical technologies and were application-
specific, that is, a change in the design of the engine required a change in the
design of the control system. Ecosystem structures were tightly coupled.
When hydromechanical technology reached maturity, engineers modular-
ized the interfaces between the engine and the control system, so that the
ecosystem structures evolved toward decoupling.
The higher thrust engines, such as the turbofan, which were being
developed during the 1970s included a large number of parameters that
required accurate measurement and computation: the hydromechanical
control systems could not cope. The complexity of the thrust calculations,
the high level of accuracy and fast response time required, and the fact that
most parameters (turbine temperature, fan speed, altitude) were available in
electronic form, rendered digital electronics more suitable for the control
systems (Prencipe, 2000). The introduction of digital electronics progres-
sively enlarged the role, importance, and functions of the engine control
system: the digital control system became the ‘‘brain’’ of the engine.
Although digital control systems controlled a higher number of engine
components, these interdependencies were governed by the interface
software. The software component meant that the digital control systems
were not application-specific, and hardware and software modules could be
reused in different applications. Following the introduction of digital
technologies, the ecosystems of firms engaged in the engine development
process evolved into loosely coupled structures with limited direct
involvement of the large engine manufacturers (Brusoni et al., 2001).
Considering coupling as a technology-enabled process helps our under-
standing of how focal firms can overcome the challenges posed by the lack
of control over their complementors (Adner & Kapoor, 2010). Platforms,
such as ITunes, and architectural rules, for example, the digital control
Problem Framing, Problem Solving, and Patterns of Coupling 185
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system, may impose precise boundaries on what complementors can do,
without becoming exceedingly normative and imposing on the focal firm the
need for complete control of the system. An important question remains
about the competitive processes underpinning the emergence of platforms
and architectures and how they challenge the role and capabilities of the
focal organizations. The model of ‘‘organizational level synchrony,’’
discussed in this volume by Davis (2013), is very much related to the
emergence of systems integration capabilities (Brusoni & Prencipe, 2001;
Prencipe, 1997), and adds analytical clarity and rigor to a discussion based
so far on detailed, but often difficult to compare case studies.
Coupling as Structure
Wolter and Veloso (2008) developed a model to analyze the evolution of
industry scope in response to incremental, modular, architectural, and
radical technological change (Henderson & Clark, 1990). Their model
predicts that the direction of industry integration will be straightforward for
incremental innovation, that is, no change and architectural innovation,
that is, increased integration. The model predicts that modular and radical
innovation will have indeterminate effects on the direction of industry
integration, by reinforcing the incentives for both disintegration and
integration. Our framework helps to explain such cases. We argue that
what appears to be an indeterminate effect (Wolter & Veloso, 2008, p. 597)
is the outcome of the dialectical tension between distinctiveness and respon-
siveness that leads to loosely coupled ecosystem structures. This is consistent
with Orton and Weick’s (1990, pp. 204–205) quest for a theory that
considers ‘‘a system that is simultaneously open and closed, indeterminate
and rational, spontaneous and deliberate.’’
Prior studies show that, in order to successfully introduce and implement
changes, focal organizations must be equipped with capability for systems
integration that allows organizational and technological leadership of the
ecosystem units (Brusoni et al., 2001). To paraphrase Orton and Weick
(1990), leadership is essential to identify and govern dispersed sources of
change. On the one hand, ecosystems must have the ability to offer timely
and cost effective and increasingly complex products, which also need to be
highly customized (e.g., mass customization for the automotive sector). This
contrasts with calls for high levels of specialization, localized adaptation,
and efficiency. On the other hand, ecosystems must be able to monitor the
technological and competitive landscape to identify possible breakthroughs,
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develop new architectures, and reorganize supply chains. This requires
persistence, knowledge integration capabilities, effectiveness, and adapt-
ability. Loose coupling emerges as a distinctive ecosystem structure to
manage the indeterminacy related to modular and architectural innovations
(Wolter & Veloso, 2008).
Modular innovations imply complex problems whose resolution requires
organizational structures to generate and exchange knowledge. One of the
key advantages of modularity is that tasks can be allocated to distinct
organizational units and, as long as the interfaces remain in place, require
little or no managerial coordination (Sanchez & Mahoney, 1996; Schilling,
2000; Sturgeon, 2002). However, empirical studies illustrate that out-
sourcing decisions related to knowledge, are more conservative than those
related to manufacturing (e.g., Brusoni et al., 2001; Chesbrough &
Kusunoki, 2001;Fleming & Sorenson, 2001; Gambardella & Torrisi,
1998;Takeishi, 2002).
Batteries and battery technologies are an example of this case. Batteries
are modular components with well-defined and standardized interfaces with
the products they power, but they are also critical for the functioning of
these products. For instance, the life and weight of a mobile phone battery
has a major impact on portability, a critical feature of the mobile phone.
The technological evolution of mobile phones is on the technological
development of batteries, making the battery one of the most critical – yet
fully modular – components. Nokia has established strategic partnerships
with battery producers to enact responsiveness in relation to defining battery
specifications, and has created an in-house dedicated laboratory to research
the underlying technologies to promote developments in new battery
technologies.
3
In 2013, the aviation authorities grounded the Boeing 787
Dreamliners belonging to various airlines because of battery malfunctions.
Apparently, Boeing’s approach to designing, developing, and manufactur-
ing the 787 did include back-up technological expertise in battery
technologies. The modular interface related to batteries led to a well-
defined pattern of division of labor between Boeing and its battery suppliers
with the result that their learning trajectories became ossified around the
design rules that informed the existing, modular pattern of division of labor,
which resulted in the malfunction. There were similar reasons for the
aircraft engine gearbox problems described in Brusoni and Prencipe (2011).
Loose coupling also provides an appropriate structure to manage the
indeterminacy related to architectural innovations. As architectural
innovations do not affect a component’s underlying technologies, working
with internal suppliers is beneficial because it saves on transaction costs
Problem Framing, Problem Solving, and Patterns of Coupling 187
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(Wolter & Veloso, 2008). However, using internal suppliers may result in
cognitive and behavioral inertia that reverses the integration benefits as
organizational processes, for example, information channels and filters,
become set around old architectures (Henderson & Clark, 1990). In
addition, as architectural innovations are infrequent, the additional costs
related to full vertical integration render it not economically viable. Loose
coupling enables established organizations to circumvent the weak incen-
tives of suppliers to invest in new technologies. In many cases, the problem
is not related to suppliers not proposing new ideas, but rather to the
inability of systems integrating organizations to understand the advantages
of new technical solutions (e.g., Chesbrough & Kusunoki, 2001). Loosely
coupled ecosystems, composed of focal organizations equipped with strong
R&D functions linked to a dense web of specialized suppliers, are better able
to explore the technological space, act upon new ideas, and reduce
bureaucracy. In the case of the pharmaceutical industry, Nesta and Saviotti
(2005) demonstrate the strategic importance of integrative capabilities for
explaining firms’ innovativeness and, ultimately, their profitability and
survival. In this context, being effective means being able to respond to
or even introduce changes that match the changes in the architecture of
artifacts and in the intra and interorganization of labor.
We would suggest also that loose coupling contributes to discussion on
the nature and essential features of hybrid ecosystems, or the forms existing
between markets and hierarchies. From a loose coupling perspective, hybrid
ecosystems are not residuals, but represent a distinctive way of organizing
economic activities. Loosely coupled ecosystems exploit the advantages of
both distinctiveness and responsiveness to develop and maintain over time
the capability to sense and to implement changes. Elements of loosely
coupled systems are simultaneously subject to spontaneous and independent
changes so that they maintain some degree of indeterminacy. The open-
ended dynamics of loose coupling, which may originate in local changes at
the organizational unit-level, may create opportunities for system-level
changes and, therefore, increase the adaptability of the entire ecosystem. On
the other hand, the degree of indeterminacy may require order to govern
dispersed elements and implement changes (Orton & Weick, 1990).
CONCLUSIONS
This chapter set out to develop an analytical framework to analyze coupling
in ecosystems. The framework proposed is based on the determinants of
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coupling: distinctiveness and responsiveness. Using these features as the
foundation for our framework draws on the idea of tight or loose coupling
or decoupling among actors, determined by the interplay between
distinctiveness and responsiveness. The manifestations of distinctiveness
and responsiveness and, therefore, the types of coupling among actors, are
influenced by uncertainty, complexity, and ambiguity. Uncertainty, com-
plexity, and ambiguity pose different degrees of difficulty in relation to
knowledge requirements, which, in turn, can be matched to and resolved by
different types of coupling.
In our view, it is important to adopt a problem-solving perspective for the
discussion of ecosystems in order to understand when this concept is useful.
For example, under conditions of mere computational uncertainty, the
extent of interaction and coordination required of the ecosystem is minimal.
This might include those problems that can be usefully approached using the
traditional tools of, say, transaction cost economics. Ambiguous problems
are relatively rare, but more interesting from an ecosystem viewpoint.
Indeed, firms struggle to make sense of problems whose dimensions are
unclear, and where it is not clear who might encompass the relevant
capabilities, need help, or interact. This makes it difficult for firms to achieve
competitive advantage. In complex problem situations, there may be a basic
problem structure or a basic infrastructure, such as the Internet. However,
firms may not know how to deal with them. The requirement may be for a
combination of in-house technical developments to integrate existing
technologies (e.g., the first smartphone), coupled with openness to enable
users and complementors to complete the new technology with content and
functionalities (e.g., the open interfaces that enabled the development of
numerous apps for the smartphone). It is in this context that the notion of
ecosystems might be most helpful.
NOTES
1. However, in July 2012, a team of physician-researchers at the Division of
Infectious Diseases of Brigham and Women’s Hospital, after performing bone
marrow transplants in two men with longstanding HIV infections, announced that
these two men no longer have detectable HIV in their blood cells (Science Daily, July
26, 2012).
2. Similar to the case of uncertainty, complexity has a subjective dimension.
Simon (1976, p. 508) argues that alongside the system’s structure, this complexity
‘‘may also lie in the eye of a beholder of that system.’’
Problem Framing, Problem Solving, and Patterns of Coupling 189
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3. Based on the framework proposed in this chapter, we would argue that Nokia
lost its industry leadership to Apple due to a lack of responsiveness related to
overreliance on specific product features – a large product portfolio to cater for all
market niches as opposed to the solo product strategy pursued by Apple,
customizable with apps – which also led to the critical importance of service-based
offering a
`la iTunes, a complementor, being overlooked (Adner & Kapoor, 2010).
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