a meta-framework for efficacious adaptive enterprise...
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A meta-framework for Efficacious Adaptive Enterprise
Architectures
Rogier van de Wetering1 and Rik Bos1
1 Open University of the Netherlands, Valkenburgerweg 177
6419 AT Heerlen, the Netherlands
Abstract. Tuning enterprise architectures to stay competitive and fit is an
enduring challenge for organizations. This study postulates a meta-framework
for Efficacious Adaptive Enterprise Architectures (EA), the 2EA framework.
We use fundamental long-standing principles found in complex adaptive
systems. These principles explain adaptive success. Also, we set forward
managerial implications about the dynamics of EA to function effectively on
four architectural levels, i.e. enterprise environment, enterprise, enterprise
systems and infrastructure. Principles of efficacious adaptation have not been
incorporated into current EA frameworks and methods underlining an
improvement area. Subsequently, we extend baseline work into a meta-
framework and evaluate it accordingly following the design science method.
Our meta-framework supports organizations to assess and adapt EA capabilities
– modular units of functionality within the organization – to the continuously
changing environment, stakeholder interests and internal organizational
dynamics. Our research contributes to foundational work on EA and can be
used for strategic EA development and maturation.
Keywords: Enterprise Architecture, Efficacious adaptation, Complex Adaptive
Systems, Meta-framework
Submission category: position paper
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1 Introduction
Organizations that want to be more competitive need to align their business
operations and information technology (IT) resources [1] and take into account the
dynamics of the changing environment [2-4]. Complementary to this they need to
lever intangible resources to build competences [5]. Effective use of flexibility and
adaptability of IT is one way in which large organizations can maintain a competitive
edge [6, 7]. It is however, not Information systems and Information Technology
(IS/IT), nor business models or any organizational arrangement that ‘separately’
create competitive advantage. Organizations can be viewed as complex adaptive
socio-technical systems. Competitive advantage is therefore the result of an
integrated, consistent and coherent business, organizational, informational and
technological design [8].
Over the past decade or so, IS/IT research and management practice increased
attention towards the adaptive and co-evolutionary nature of IS/IT [9, 10] and
dynamic, multi-faceted, and non-deterministic processes to align IS/IT and the
business in constantly-changing business environments [11]. This evolutionistic and
dynamic approach1 has its roots in nonlinear science such as physics, biology, bio-
chemistry and economy and has a profound impact on management, strategy,
organization and IS/IT studies. Merali et al. [12] even argue that such an approach
should frame the future IS/IT research in terms of the development of the field.
1.1 Enterprise Architecture research practice
Enterprise Architecture (EA) practices enable organizations to achieve strategies
through orchestrated and aligned organizational processes, governance and
organizational structures, from holistic perspectives, models and views [13, 14]. In
this process EA provides insights, enables communication among stakeholders and
guides complicated change processes [15]. While EA capability deployment is not
homogenous and universal for organizations, EA’s enable organizations to get value
across all business units, operations, technology, and human resources and align this
with the use of resources [13]. EA’s models and frameworks generally guide design
decisions across the enterprise, specify how information technology is related to the
overall business processes and outcomes of an organization and ensure that the
relationships and dependencies among architectural components are managed [16].
EAs are commonly represented in different layers in order to describe a set of
cohesive or related elements in order to create structure in a chaotic environment [16,
17]. This is also recognized by service oriented approaches, e.g. Service-Oriented
Architecture (SOA) developments [18].
Since its conception in the late eighties [Cf. 19] the EA domain has received
substantial interest both from theorist, government EA initiatives, consultants and IT-
practitioners. However, academic and theoretical discussions remain modest [20] and
extant literature stumbles upon fundamental problems. These problems include lack
1 The authors in the current article refer to the field of Complexity Science. Complexity science
will be addressed in subsection 1.2 and section 2 more extensively.
A meta-framework for Efficacious Adaptive Enterprise Architectures 3
of uniformity in definitions and dimension [21] and lack of explanatory theory and
publications delivering only modest views on how EA yields benefits. Also the focus
is rather from a technical baseline [22]. Another important issue is the lack of
empirical findings on how EA delivers benefits [1, 23].
To date, very little research has been done on fitness and efficacious adaptation in
the context of EA. The need for an integral understanding of dynamic architectural
complexity, adaption and enterprise transformation is also stressed by [24]. Although
there are various studies dealing with complexity science in the domain of EA [E.g.
25] convincing attempts with proper theoretical framing are scarce. A first attempt to
design a theory-based conceptual framework that helps to analyze, identify
improvement areas and drive adaption of EA within organizations is valuable.
1.2 Research premises and objectives
This article is based on the premise that long standing first principles of efficacious
adaptation – Proper et al. [26] call these scientific principles – from natural,
biological, social and economical sciences explain adaptive success. These principles
have a direct impact on organizations’ ability to adapt and co-evolve their IS/IT
capabilities to the rapidly changing environment [9, 10]. Doing so, we built upon
ideas and principles from complexity science and Complex Adaptive Systems theory,
CAS [27], serving as a theoretical frame of reference in the construction of a meta-
framework. CAS are typically concerned with the study of nonlinear dynamical
systems, and has recently become a major focus of interdisciplinary research.
EA’s – or Enterprise systems architectures, as it is sometimes called [28] – come
with many definitions [13, 14]. For the purpose of this paper, we define an enterprise
architecture as ‘an abstract representation – or blueprint – of the entire enterprise, see
also Urbaczewski and Mrdalj [29] representing the high-level structure (or
organization logic) of an enterprise, its business processes, IT-infrastructure
capabilities and the relationships among the various capabilities across the
hierarchical layers and the external environment’. We see EA capabilities, in this
respect, as modular units of functionality within the organization, including processes,
people, technology and assets. We consider them as loosely coupled, modular
building blocks of the enterprise and its architecture. They describe ‘what’ is required
to meet strategic enterprise objectives, demands and be competitive irrespective of
‘how’ they are managed on a lower level of design and implementation. Capabilities
are thus abstractions of complex behavior and architectural structure. See Azavedo et
al. for a comprehensive foundational ontological discussion [30].
Our main objective – and contribution of this current paper – is to propose a theory
driven conceptual meta-framework for efficacious EA adaptation. This framework
can be used to leverage current and future EA capabilities and support the process of
adapting EA capabilities.
We describe our conceptual meta-framework on a generic level, so that it is both
compatible with existing familiar methodologies and (reference) frameworks (like
Zachman, TOGAF, Four-Domain, DODAF, FEAF, ESARC, DYA, etc.). We focus on
both key challenges at I) different architectural layers and II) the position of EA in
relation to an organization’s effort to continuously adapt to changes in the
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environment and stay fit. Hence, in this paper we address the following main research
question: “How can a meta-framework for efficacious EA adaptation be designed
using complexity science as a frame of reference and principles of adaptive success?”
The remainder of the article is structured as follows. We begin with a brief review
on theories in complexity science research. Section 3 introduces the framework
development process, while section four describes the dynamics at various
architectural layers of our meta-framework. Section five outlines and discusses eight
principles of efficacious adaptive success of EA. We end with the discussion and
conclusions.
2 Theories in complexity science research
Complexity science and complex adaptive systems (CAS) research includes studies
on themes as co-evolution, adaptation, interacting agents, decentralized control, self-
organized emergent behavior, and hierarchical structure. It has a rich history having
its scientific roots in physics, mathematics, and evolutionary biology [9, 27, 31-34]
and builds upon open systems theory2 [35] and also on often forgotten Cellular
Automata (CA) [36].
CAS theories are based on the fundamental logical properties of the behavior of
non-linear and network feedback systems, no matter where they are found [32]. CAS
are considered as collections of individual agents with the freedom to act in ways that
are not always totally predictable, linear, and whose actions are interconnected so that
one agent’s actions changes the contexts for other agents. Commonly cited examples
include financial markets, weather systems, human immune system, colonies of
termites and organizations [27, 31, 37, 38]. Complexity science challenges traditional
(linear, or Newtonian) science and management routines on organizational behavior,
and the key principle of this perspective is the notion that “at any level of analysis,
order within a system is an emergent property of individual interactions at a lower
level of aggregation” [32].
Complexity science is not a single theory or proposition. We recognize three
phases (not necessarily time dependent) of its development and accumulating
knowledge. Phase 1 is based in mathematics and can be traced down to adaptive
tension and first critical values of imposed energy in physical systems [39]. The
second phase is due to scholars from the Sante Fe Institute and focus on CAS and
dynamic, non-linear behavior and interacting agents [27, 34, 40]. Recent complexity
endeavors also focus on scalability and power laws that govern natural and social
phenomena. This can be considered a third phase [41, 42].
A number of authors have stated that the science of complexity can be considered a
valuable instrument to cope with organizational and IS/IT changes in non-linear
turbulent environments [31-33, 37]. As whole entities and with respect to their
mutually interdependent parts, they go through a series of adaptations/re-adaptation
cycles [9]. This idea applies particularly well to the development, continuous
adaptation and alignment of EA in organizations operating in turbulent environments.
2 Complexity science and CAS thinking search for generative simple rules in nature that
underpin complexity and do not embrace the radical holism of systems theory.
A meta-framework for Efficacious Adaptive Enterprise Architectures 5
EA can be considered as a hierarchical, multilevel system, comprising aggregation
hierarchies, architecture layers and views [17] and resembles common elements and
behaviors [31, 37] of CAS: (1) transformative and coevolving [27], (2) massively
entangled [43], (3) emergent and self-organizing due to aggregate behavior from the
interaction of the systems components or agents [27, 43] defined at various scales and
layers and respond to environmental changes using internalized rule sets that drive
action [44].
We employ the basic thought that EA adaptation needs a ‘holistic’ and ‘complex’
theoretical framework that fits the diversity of organizational components and
interactions among the many agents that are involved in the practice using EA.
3 Framework development process
Our approach is exploratory of nature and follows Simon’s view of a ‘science of
design’ and hence the initial stages of the design science method [45, 46]. As such,
we employ an incremental development process whereby knowledge is produced by
constructing and evaluating (EA) artifacts which are subsequently used as input for a
better awareness of the problem [47] and hence to serve human purposes [48].
This research focusses on ‘building activities’ within the general design science
methodology in a first attempt to design a conceptual meta-framework for efficacious
EA adaptation. Therefore, this study pays considerable attention to link the
articulation of the theoretical position and existing baseline work. In their conceptual
analysis, the authors ensured quality and validity through the use of complementary
validation methods, i.e. extensive literature review (as a first step in the development
process), re-usage of baseline work and incremental reviews as the final steps of this
research. That is to say that evaluation (Peffers et al. divide what others call
evaluation into two activities, demonstration and evaluation [45, 46]) and
communication are currently out of scope.
As baseline work, we build on prior research from McKelvey and Benbya [9, 10]
and 1st principles of efficacious adaptation drawn from natural, social systems, i.e.
applying tension, improvement of requisite variety/complexity, change rate, modular
(nearly decomposable) design, positive feedback/ fostering coevolution:, causal
intricacy, complementarity3 and coordination rhythm. Any of these interdependent
principles gives an organism, species, or organization adaptive advantage [9]. These
principles are discussed in more detail in section 5. Having none of them is a disaster;
having all particularly feeds adaptive and synergetic success [10]. Synergetic success
particularly suits the notion of strategic alignment, i.e. equilibrium of different
organizational dimensions, and external fit as strategy development that is based on
environmental trends and changes [49-51]. This can be considered a first pillar of our
meta-framework.
A second pillar of our meta-framework for EA efficacious adaptation, builds upon
an integration among common architecture frameworks [52]. This pillar identifies
3 This complementarity principle does not occur in the original work of McKelvey. We add this
principle based on its longstanding tradition and its impact on modern economics and
business management.
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some well-known characteristics and commonalities from previous architectural
frameworks, i.e. (a) hierarchical components (thus architectural levels) and (b) the
type of information (sometimes referred to as domains). Within this integrated
framework each architectural layer serves as a container of capabilities that is not
fixed, but flexible. Therefore, any capability can be added on each of the architectural
layers. Even so, capabilities can span several architectural layers. The complexity
science lens extends this second pillar and adds new concepts such as co-evolution,
self-organization, edge of chaos and historicity and time dependence, which enrich
old systems thinking concepts [53].
As part of the design methodology, our first endeavor was to extensively review
literature on complexity science, EA and adaptation creating common ground on
existing architectural frameworks and gaps/voids in current literature. Building
concepts – as an objective for a solution – based on the above pillars is part of this.
Subsequently, initial concepts and building blocks of our framework, i.e. an initial
design artifact, where then critically reviewed. These critical reviews concerning the
artifact’s desired purpose, functionality and its layered architecture were taken into
consideration within the first iterations of the framework design and development
step.
Figure 1 displays our high-level conceptual 2EA framework. It consists of (I) four
architectural layers, contains (II) EA capabilities distributed among the various
architectural layers and (III) three clusters, or dimensions, containing the eight
principles of efficacious adaptation. The latter will be addressed in section 5.
Capabilities at each layer provide services to higher-level capabilities. Other relevant
architectural elements, such as governance, strategy and requirements, the EA
development process and business value monitoring are omitted form this framework
for scope and the purpose of the current paper.
The following two sections describe the four architectural layers in more detail and
applies the 1st principles of our meta-framework.
4 Dynamics at architectural layers of 2EA framework
Co-evolutionistic ‘lenses’ are valuable for integrating both micro and macro-level
evolution within a unifying framework, incorporating multiple (hierarchical) levels of
analyses and contingent effects [54, 55]. Our meta-framework consists of four
architectural, connected and interrelated layers with capabilities. We discuss the four
layers briefly and highlight main challenges.
Enterprise environment level
At the highest level of our meta-framework, enterprise actors (e.g. supply chain
partners and customers) interact as part of independent businesses (or business units),
and co-evolve with the organizations ecosystem. Since many organizations need to
deal with the dynamics of stakeholders and environmental uncertainties, this level is
especially interesting for what Zarvic and Wieringa [52] call ‘networked business
constellations’. From an EA capability perspective this level typically deals with
managing customers, suppliers, governmental institutions, i.e. the enterprise
A meta-framework for Efficacious Adaptive Enterprise Architectures 7
ecosystem. Key challenges for organizations are to deal with the dynamics of
ecosystem management concerns, i.e. collaboration with partners, legislation
concerns, new developments in technology, variance in requirements, competitors in
the ecosystem etc.
Fig. 1. 2EA framework.
Enterprise
The ‘Enterprise’ level defines how an organization operates in terms of organizational
structure (and relationships among various units and divisions), its operating model
(i.e. level of process integration and standardization) and business processes. All with
the objective of delivering value (i.e. product, services or both) to a designated client,
partner or market. Some challenges are encapsulating functionality, alignment of
business processes with IT-functionality, plug-and-play components and services (e.g.
business processes, end-2-end value chains) that enterprises can use to instantly
response to meet specific customer and environmental demands.
Enterprise system
The ‘system level’ contains capabilities related to data and applications. The focus is
on leveraging heterogeneous and loosely coupled IT containing different interrelated
information and data applications. This layer also covers the data and information
ecosystem; the infrastructure that encompasses data sources, transformation &
integration and reporting & analysis. Main challenges include standardizing
interfaces, API’s and enterprise-wide service levels (not necessarily SLA’s), loosely
coupled IS/IT components offered to the ‘Enterprise level’. Managing data
consistency (i.e. dealing with authentic sources for each data and information object),
plug-and-play software packages and managing IT-flexibility of the enterprise
application landscape.
Infrastructure
This bottom layer, Infrastructure, consists of all capabilities dealing with the technical
network, operating systems, hardware and (middleware-)service elements needed to
facilitate the higher architectural layers [52]. Infrastructure architecture capabilities
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govern the way in which the infrastructure is designed and efficacious deployed in
practice. Challenges at this layer include continuous security management,
networking, virtualization, scalability, usage of open standards, connectivity,
portability and allowing end-users with the organization to use the (self-)services
wherever they are in physical space.
The above multi-level challenges can be dealt with using principles of co-
evolutionary dynamics – set forward by McKelvey [9, 10]. We will discuss these
principles next.
5 Applied principles of efficacious adaptation
Principles of efficacious adaptation are generative forces driving adaptation in
organisms and organizations. These principles are elementary drivers for efficacious
adaptation of EA in organizations, in the same vain as they are drivers for Information
System development [9], IS-alignment [10], technology based new ventures [56],
among other applications.
The authors combined these eight principles of the 1st pillar into three main clusters,
dimensions, i.e. (1) Co-evolutionary tension and change, (2) Organizational
complexity and (3) Complementarity and interactional complexity. This classification
is based on extensive literature study, conceptual analysis and design. In practice
however, these clusters might not be exclusive. The authors had to balance between
recognizing the details of practice and complying the need for overview and
limitation. Based on the existing body of knowledge and a profound theoretical
approach, three clusters is adequate from both a scientific and practical perspective.
The clusters are:
(1) Co-evolutionary tension and change
I. Prigogine’s Adaptive Tension: theory set forward by Prigogine and Stengers
[39, 57], among others, – the cornerstone of the European School of
Complexity Science – and concerns environmentally imposed tensions
(energy differentials) that stimulate adaptive order creating for the system as
a whole. This is also known as the dissipative structures theory.
II. Maruyama’s Deviation Amplification Theory: concerns the principle of
coevolution via positive feedback initiated by Maruyama [58]. Mutually
causal relationships can amplify an insignificant or accidental start, that may
lead to order creation among agents or modules depending on different
(insignificant) initial conditions.
III. Fisher’s Change Rate: the third principle concerns the relationship between
variation and adaptation set forward by Fisher [59]. Fisher postulated that the
process of adapting to a changing environment speeds up the rate that usable
genetic variation becomes available. Higher internal rates of change offer
adaptive advantage in changing environments.
(2) Organizational complexity
IV. Ashby’s Law of Requisite Variety: foundational law of system complexity
stating that external variety can be managed by matching it with a similar
degree of internal complexity [60].
A meta-framework for Efficacious Adaptive Enterprise Architectures 9
V. Simon’s Modular Design: this fifth principle is set forward by Simon [61]. It
means that complex systems consisting of nearly decomposable subunits
tend to evolve faster, increase the rate of adaptive response and tune towards
stable, self-generating configurations.
(3) Complementarity and interactional complexity
VI. Lindblom’s Causal Intricacy: concerns the processes of parallel interaction
and mutual adjustment among heterogeneous interconnected groups (each
having their own agenda) facing complex and uncertain choice and action
situations [62].
VII. Edgeworth’s Complementarity Theory: complementarity theory assumes that
the individual elements of a strategic planning process cannot be individually
optimized to achieve a better performance [63, 64]. Consequently, the impact
of a system of complementary practices will be greater than the sum of its
parts because of synergistic effects.
VIII. Dumont’s Coordination Rhythm: the dynamic rhythm principle stems from
Dumont’s [65] initiating study of Hindu society. Dominance oscillates
between Brahmin and Rajah (i.e. religion vs. secular forces) as the need for
warfare comes and goes. In organizations, this manifests itself as entangled
dualities such as centralization-decentralization, exploitation-exploration
[66] and implicit vs explicit knowledge [67].
The next section discusses the above principles of our meta-framework.
5.1 Co-evolutionary tension and change
Adaptive Tension. The tension concept is particularly relevant to the practice of EA
since energy disparities may cause a phase transition; new order creation and thus
efficacious enterprise capabilities. This perspective facilitates organizations with a
dynamic interplay of coevolving capabilities, mechanisms and effects across all layers
of our meta-framework. In the EA context this means fostering tension between EA
capabilities along the four defined architectural levels as a drive for adaptation.
Tension drivers include e.g. the continuous adoption of new (modular) capabilities,
competition between capabilities (i.e. survival of the fittest), enterprise-wide cost
cutting etc. This co-evolutionary perspective, set in motion by tension, could spin-off
into new product and service offerings based as a result of new order.
Deviation Amplification. Our main aim to apply this principle is, that deviation
amplification via positive feedback mechanisms pushes the idea that self-organizing
agents (or heterogenic) capabilities can create significant new structures to create a
better overall functioning and thus adaptation, see also [9]. Mutual cause and effect
relationships allow for small instigating events (sometimes accidents) to spiral into
complex new adaptive structures that efficacious deal with turbulence in the
environment. This principle can be applied to all the layers of our EA meta-
framework. This principle should be governed as a mechanism for incremental EA
design that fosters emergence of new architectural capabilities and generate new
innovative beneficial relations among capabilities.
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Change Rate. Adaptation is enhanced by the rate of internal change. We know this
by Fisher [59] who found a link between variation and adaptation. This principle is
particularly applicable to high velocity practices and environments. When translated
to the practice of EA, it comes down to enabling continuous change of the EA
spanning processes, people, technology and assets and thus increasing its absorptive
capacity. Adaptation cannot proceed faster than the rate that usable and innovative
variation becomes available, e.g. new knowledge, services, innovation.. Therefore,
organization need to propagate a continuous interaction process [68] and maintain
short-term focus relations between capabilities assuring tension, disequilibrium, phase
transition and new order [Cf. 56].
5.2 Organizational complexity
Requisite Variety. For systems to remain viable, it needs to generate the same
degree of internal variety as the external variety it faces in the environment [10, 60].
This requirement appears in EA literature as the capacity to integrate and connect
dissimilar data and information structures, IS/IT and business capabilities as well as
the ability of the EA to generate technical variety sufficient to fit changing
environmental conditions, see also Benbya and McKelvey [10]. As such, it requires a
technical infrastructure that enabled the foundation of capabilities upon which the
enterprise business depends [69] a characteristic referred to by Duncan as flexibility
[6]. For organizations to efficiously adapt to the environment and remain viable, a
moderate amount of ‘epistatic’ relations are most suitable [Cf. 34, 70].
Modular Design. Modular design dates back to Simon’s theory of near
decomposability, i.e. his design principles for modular systems and ‘loose coupling’
[61, 71]. As a defacto standard [7], modularity within the EA infrastructure layer
allows organizations to integrate disparate and geographically distributed systems
across various hierarchical layers. Modular design in terms of our meta-framework
refers to the extent to which it is possible to add, modify, and remove any capability
with ease and with no major overall effect for practice. This approach enables
organizations to decompose its enterprise architecture into atomic capabilities with
very few strong with most relations among the EA capabilities being short-term.
Reducing interdependencies among capabilities leads to robust designs that result in
relatively stable and predictable behaviors [72]. Following this principle and recent
research on the nature of modularity [73], organization should continuously
decompose their EA into modular capabilities.
5.3 Complementarity and interactional complexity
Causal Intricacy. In hierarchical systematics, based on Lindblom [62] ‘means and
ends’, causal influences may be multidirectional and have multilevel effects, i.e.
downward, upward, horizontal, diagonal and intermittent [9]. In the context of our
meta-framework, following this principle means that the interaction among the
capabilities is not a static and deterministic process. Instead, it should foster co-
evolution and dynamic interaction across the layers, focusing on both technical
(functionality, interoperability, interfaces etc.), business and organization
A meta-framework for Efficacious Adaptive Enterprise Architectures 11
requirements that continuously change and are in motion [9]. Moreover, EA – from a
complex adaptive systems perspective – cannot be perceived and deployed as a
‘static’ artifact. Expected and unexpected changes need to be made because of
continuous adaptation to environmental turbulence. Strategic planning for EA can be
interpreted as a combination of intended, unintended and unrealized strategic routes.
This reminds of the classic, but still highly cited, vision of Henry Mintzberg on
strategic planning [74].
Complementarity. The theory of complementarity that was initially introduced by
Edgeworth [75]. Milgrom and Roberts [63, 64] proposed that some organizational
activities, practices are mutually complementary and so tend to be adopted together.
Following this logic, complementary practice results will be greater than the sum of
its parts because of the synergistic effects of bundling practices together. Black and
Lynch [76] argue that work practice need to be implemented in conjunction with other
complementary (best-)practices. For EA this translates into the adoption and usage of
complementary best-practices models, principles and guidelines on each layer. For
instance, the usage of open standards (on infrastructure level) and modular plug-and-
play software components (on enterprise system level), or data models (on enterprise
system level) and loose coupled business processes and services (on enterprise level)
as part of the EA capabilities.
Coordination Rhythm. In business management literature and other disciplines
(from biology to sociology), ‘dynamic rhythm’ manifests itself as entangled dualities
such as fast-flow, centralization-decentralization and exploitation-exploration [66],
implicit vs explicit knowledge [67], offensive vs defensive strategies [77],
incremental vs. revolutionary IS/IT development steps [78] and planning vs
emergence [74]. We argue that for organizations to be adaptive, they should support
both interaction and entangled ‘top-down development’ versus ‘bottom-up autonomy’
of EA capabilities development and deployment in practice. In fact, these poles
‘complement’ each other to bring about an irregular basis for adaptation of EA. Han
and McKelvey [56] argue in fact that when organizations manage the duality of a
moderate number and short-term ties, they are in essence managing: “..the process of
parallel interaction and mutual adjustment, and coordinating a collection of diverse
attributes of ties carrying different rhythms”.
6 Conclusion and outlook
Using a complexity science ‘lens’, we postulate that EA’s and its development
should not be regarded as ‘static’ and ‘homogeneous’ blueprints of organizations.
Accordingly, a theorized framework for Efficacious Adaptive Enterprise
Architectures, the 2EA framework, is proposed. The framework ties eight long
standing principles of adaptation across three clusters to EA. Our main contribution,
is the application of these principles and the dynamics across the various architectural
layers of our framework. Organizations that embrace these principles in EA practice
are better equipped to deal with both the internal and external dynamics. This extends
the current EA body of knowledge.
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In practice, the framework guides organizations to adapt their EA in alignment
with the turbulent environment, organizational dynamics and constantly changing
stakeholder interests. To make this possible, we suggest that organizations should
explicitly identify and execute improvement activities in alignment with each of the
eight principles of the 2EA framework. The meta-framework can then I. be used to
leverage current, the ‘as-is’ EA of the organization (i.e. descriptive perspective), and
future EA capabilities within the enterprise, the new EA (i.e. prescriptive
perspective), IIa. be used as a grounded checklist and analysis tool to systematically
identify improvement areas dealing with EA challenges and IIb. assist EA decision
makers to make better investments and deployment decisions (i.e. explanatory
perspective). This can systematically be done by incrementally assessing the current
situation, a ‘to-be’ state and determining the fit-gap and thus improvement activities.
Therefore, an effective EA development strategy should be complemented by a
process for continuous development of EA, since strategic enterprise objectives, goals
and demands continuously change within organizations.
Despite its comprehensiveness, our theorized framework has of course some
limitations. First, the 2EA needs to be applied to a number of cases in order to
evaluate it and to allow for critical reflection. This is in accordance with the design
science method [46]. The current framework does have a plausible theoretical
foundation and provides various opportunities for further research. Furthermore, the
question ‘how organizations can truly apply these adaptive principles in practice and
benefit from them’ is currently omitted and beyond the scope of this work. This is
also a topic for further research. This can be done for instance using expert sessions,
interviews and focus groups. Also, our 2EA framework currently does not explain
how it facilitates – or is related to – the Dynamic Capabilities View (DCV) [3, 4].
DCV emerged as an influential perspective in strategic management and IS literature.
It explains how firms differentiate and compete, while simultaneously evolving and
reconfiguring their operations in order to remain competitive. A recent study in fact
empirically demonstrated that characteristics of a firms IT architecture can facilitate
(IT-enabled) dynamic capabilities [79].
This study is the first of its kind that applies principles of efficacious adaptation, to
the multi-layered EA practice. Outcomes of this study support and guide enterprise
architects, IT-managers and CIO’s with day-to-day environmental, organizational and
stakeholder challenges. It positions their work and efforts into a holistic perspective
and through a complexity lens. Future research can benefit from our work to
understand the nature of efficacious adaptation of enterprise architectures. This paper
lays a foundation for further research in this imperative domain that can focus on
validating the framework’s premises using empirical data and potentially agent based,
modeling, NK fitness landscape modeling and other simulation techniques to
generalize outcomes [34, 70, 80]. It is our ambition to extend the application of the
2EA framework and also focus on qualitative and co-evolutionary aspects in a
networked business ecosystem setting, modeling efficacious adaptive EA’s, define
architectural complexity measures (e.g. modularity, agility), and synthesize EA
strategies following the concepts of EA fitness landscapes. These matters, among
others, are currently under investigation.
A meta-framework for Efficacious Adaptive Enterprise Architectures 13
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