a meta-framework for efficacious adaptive enterprise...

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

[email protected]

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

2

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

4

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.


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.

6

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


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

8

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].


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.

10

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


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.

12

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.


References

1. Wegmann, A., The Systemic Enterprise Architecture Methodology (SEAM). Business

and IT Alignment for Competitiveness. 2002.

2. Brown, S. and K. Eisenhardt, The art of continuous change: Linking complexity

theory and time-paced evolution in relentlessly shifting organizations. Administrative

Science Quarterly, 1997. 42(1): p. 1-34.

3. Eisenhardt, K.M. and J.A. Martin, Dynamic capabilities: what are they? Strategic

management journal, 2000. 21(10-11): p. 1105-1121.

4. Teece, D.J., G. Pisano, and A. Shuen, Dynamic capabilities and strategic

management. Strategic management journal, 1997. 18(7): p. 509-533.

5. Wernerfelt, B., A resource-based view of the firm. Strategic Management Journal,

1984. 5(2): p. 171–180.

6. Duncan, N.B., Capturing flexibility of information technology infrastructure: A study

of resource characteristics and their measure. Journal of Management Information

Systems, 1995: p. 37-57.

7. Wilkinson, M., Designing an ‘adaptive’enterprise architecture. BT Technology

Journal, 2006. 24(4): p. 81-92.

8. Hoogervorst, J., Enterprise architecture: Enabling integration, agility and change.

International Journal of Cooperative Information Systems, 2004. 13(03): p. 213-233.

9. Benbya, H. and B. McKelvey, Toward a complexity theory of information systems

development. Information Technology & People, 2006. 19(1): p. 12–34.

10. Benbya, H. and B. McKelvey, Using coevolutionary and complexity theories to

improve IS alignment: a multi-level approach. Journal of Information Technology,

2006. 21(4): p. 284–298.

11. Vessey, I. and K. Ward, The dynamics of sustainable IS alignment: The case for IS

adaptivity. Journal of the Association for Information Systems, 2013. 14(6): p. 283-

311.

12. Merali, Y., T. Papadopoulos, and T. Nadkarni, Information systems strategy: Past,

present, future? The Journal of Strategic Information Systems, 2012. 21(2): p. 125-

153.

13. Bernard, S.A., An introduction to enterprise architecture. 3rd Edition ed. 2012:

AuthorHouse.

14. Ross, J.W., P. Weill, and D. Robertson, Enterprise architecture as strategy: Creating

a foundation for business execution. 2006: Harvard Business Press.

15. Jonkers, H., et al., Concepts for modeling enterprise architectures. International

Journal of Cooperative Information Systems, 2004. 13(03): p. 257-287.

16. Janssen, M. Framing Enterprise Architecture: A metaframework for analyzing

architectural efforts in organizations. in Coherency Management: Architecting the

Enterprise for Alignment, Agility and Assurance. 2009. Authorhouse.

17. Winter, R. and R. Fischer. Essential layers, artifacts, and dependencies of enterprise

architecture. in Enterprise Distributed Object Computing Conference Workshops.

10th IEEE International. 2006. IEEE.

18. Bell, M., SOA Modeling patterns for service-oriented discovery and analysis. 2010:

Wiley Online Library.

19. Zachman, J., A framework for information systems architecture. IBM systems

journal, 1987. 26(3): p. 276-292.

20. Tamm, T., et al., How does enterprise architecture add value to organisations.

Communications of the Association for Information Systems, 2011. 28(1): p. 141-

168.

21. Greefhorst, D., H. Koning, and H. Van Vliet, The many faces of architectural

descriptions. Information Systems Frontiers, 2006. 8(2): p. 103-113.

14

22. Ren, M. and K.J. Lyytinen, Building enterprise architecture agility and sustenance

with SOA. Communications of the Association for Information Systems, 2008. 22(1):

p. 4.

23. Foorthuis, R., et al., A theory building study of enterprise architecture practices and

benefits. Information Systems Frontiers, 2015: p. 1-24.

24. Zimmermann, A., et al., Collaborative Decision Support for Adaptive Digital

Enterprise Architecture. 2015.

25. Janssen, M. and G. Kuk. A complex adaptive system perspective of enterprise

architecture in electronic government. in Proceedings of the 39th Annual Hawaii

International Conference on System Sciences. 2006. Hawaii: IEEE.

26. Proper, E. and D. Greefhorst, The roles of principles in enterprise architecture. 2010:

Springer.

27. Holland, J., Complex adaptive systems. Daedalus, 1992. 121(1): p. 17–30.

28. Nightingale, D.J. and D.H. Rhodes. Enterprise systems architecting: Emerging art

and science within engineering systems. in MIT Engineering Systems Symposium.

2004. MIT.

29. Urbaczewski, L. and S. Mrdalj, A comparison of enterprise architecture frameworks.

Issues in Information Systems, 2006. 7(2): p. 18-23.

30. Azevedo, C.L., et al., Modeling resources and capabilities in enterprise architecture:

A well-founded ontology-based proposal for ArchiMate. Information Systems, 2015.

54: p. 235-262.

31. Dooley, K., A complex adaptive systems model of organization change. Nonlinear

Dynamics, Psychology, and Life Sciences, 1997. 1(1): p. 69–97.

32. Stacey, R., The science of complexity: an alternative perspective for strategic change

processes. Strategic Management Journal, 1995. 16(6): p. 477–495.

33. Anderson, P., Complexity theory and organization science. Organization Science,

1999. 10(3): p. 216–232.

34. Kauffman, S.A., The origins of order: Self organization and selection in evolution.

1993: Oxford university press.

35. Boulding, K., General systems theory-the skeleton of science. Management Science,

1956. 2(3): p. 197–208.

36. Wolfram, S., A new kind of science. Vol. 5. 2002: Wolfram media Champaign.

37. Holden, L., Complex adaptive systems: concept analysis. Journal of Advanced

Nursing, 2005. 52(6): p. 651–657.

38. Burnes, B., Kurt Lewin and complexity theories: back to the future? Journal of

Change Management, 2004. 4(4): p. 309–325.

39. Prigogine, I. and I. Stengers, The end of certainty. 1997: Simon and Schuster.

40. Gell-Mann, M., What is complexity?, in Complexity and industrial clusters. 2002,

Springer. p. 13-24.

41. Andriani, P. and B. McKelvey, Perspective-from Gaussian to Paretian thinking:

causes and implications of power laws in organizations. Organization Science, 2009.

20(6): p. 1053-1071.

42. Newman, M.E., Power laws, Pareto distributions and Zipf's law. Contemporary

physics, 2005. 46(5): p. 323-351.

43. Morel, B. and R. Ramanujam, Through the looking glass of complexity: the dynamics

of organizations as adaptive and evolving systems. Organization Science, 1999.

10(3): p. 278–293.

44. Plsek, P., Redesigning health care with insights from the science of complex adaptive

systems. Crossing the quality chasm: A new health system for the 21st century, 2001:

p. 309–322.

45. Peffers, K., et al., A design science research methodology for information systems

research. Journal of management information systems, 2007. 24(3): p. 45-77.


46. Venable, J., J. Pries-Heje, and R. Baskerville. A comprehensive framework for

evaluation in design science research. in International Conference on Design Science

Research in Information Systems. 2012. Springer.

47. Hevner, A.R., et al., Design science in information systems research. MIS Quarterly,

2004. 28(1): p. 75–105.

48. March, S.T. and G.F. Smith, Design and natural science research on information

technology. Decision support systems, 1995. 15(4): p. 251-266.

49. Chan, Y.E. and B.H. Reich, IT alignment: an annotated bibliography. Journal of

Information Technology 2008. 22: p. 316–396.

50. Luftman, J. and R. Kempaiah, An update on business-IT alignment: “a line” has

been drawn. MIS Quarterly Executive, 2007. 6(3): p. 165–177.

51. Van de Wetering, R. and R. Batenburg, Towards a Theory of PACS Deployment: An

Integrative PACS Maturity Framework. Journal of digital imaging, 2014. 27(3): p.

337-350.

52. Zarvić, N. and R. Wieringa. An integrated enterprise architecture framework for

business-IT alignment. in Proceedings of the CAISE'06 Workshop on Business/IT

Alignment and Interoperability (BUSITAL 2006). 2014. CEUR.

53. Mitleton-Kelly, E., Complex systems and evolutionary perspectives on organisations:

the application of complexity theory to organisations. 2003: Elsevier Science Ltd.

54. Van Den Bosch, F.A., H.W. Volberda, and M. De Boer, Coevolution of firm

absorptive capacity and knowledge environment: Organizational forms and

combinative capabilities. Organization science, 1999. 10(5): p. 551-568.

55. Lewin, A.Y. and H.W. Volberda, Prolegomena on coevolution: A framework for

research on strategy and new organizational forms. Organization science, 1999.

10(5): p. 519-534.

56. Han, M. and B. McKelvey, Toward a social capital theory of technology-based new

ventures as complex adaptive systems. International Journal of Accounting &

Information Management, 2008. 16(1): p. 36-61.

57. Prigogine, I., Thermodynamics of irreversible processes. 1955: Thomas.

58. Maruyama, M., The second cybernetics: Deviation-amplifying mutual causal

processes. American scientist, 1963: p. 164-179.

59. Fisher, R.A., The genetical theory of natural selection: a complete variorum edition.

1930: Oxford University Press.

60. Ashby, W., Principles of the self-organizing system. Principles of Self-organization,

1962: p. 255–278.

61. Simon, H.A., The architecture of complexity. General systems, 1965. 10(1965): p. 63-

76.

62. Lindblom, C.E., The science of" muddling through". Public administration review,

1959: p. 79-88.

63. Milgrom, P. and J. Roberts, The economics of modern manufacturing: technology,

strategy, and organization. The American Economic Review, 1990. 80(3): p. 511–

528.

64. Milgrom, P. and J. Roberts, Complementarities and fit strategy, structure, and

organizational change in manufacturing. Journal of Accounting and Economics,

1995. 19(2–3): p. 179–208.

65. Dumont, L., Homo hierarchicus: The caste system and its implications (trans. M.

Sainsbury, L. Dumont, and B. Gulati). 1966, Chicago: University of Chicago Press.

66. March, J.G., Exploration and exploitation in organizational learning. Organization

science, 1991. 2(1): p. 71-87.

67. Nonaka, I., A dynamic theory of organizational knowledge creation. Organization

science, 1994. 5(1): p. 14-37.

16

68. Davis, G.B., Strategies for information requirements determination. IBM systems

journal, 1982. 21(1): p. 4-30.

69. McKay, D. and D. Brockway, Building IT infrastructure for the 1990s. Stage by

stage, 1989. 9(3): p. 1-11.

70. Frenken, K., A fitness landscape approach to technological complexity, modularity,

and vertical disintegration. Structural Change and Economic Dynamics, 2006. 17(3):

p. 288-305.

71. Weick, K.E., Educational organizations as loosely coupled systems. Administrative

science quarterly, 1976: p. 1-19.

72. Levinthal, D.A. and M. Warglien, Landscape Design: Designing for Local Action in

Complex Worlds. Organization Science, 1999. 10(3): p. 342-357.

73. Clune, J., J.-B. Mouret, and H. Lipson. The evolutionary origins of modularity. in

Proceedings of the Royal Society of London B: Biological Sciences. 2013.

74. Mintzberg, H., Patterns in strategy formation. Management Science, 1978. 24(9): p.

934–948.

75. Edgeworth, F., Mathematical physics: an essay on the application of mathematics to

the moral sciences. 1881, London: C. Kegan Paul & Co.

76. Black, S.E. and L.M. Lynch, How to compete: the impact of workplace practices and

information technology on productivity. Review of Economics and statistics, 2001.

83(3): p. 434-445.

77. Wagter, R., et al., Dynamic enterprise architecture: how to make it work. 2005: John

Wiley & Sons.

78. Van de Wetering, R., R. Batenburg, and R. Lederman, Evolutionistic or revolutionary

paths? A PACS maturity model for strategic situational planning. International

Journal of Computer Assisted Radiology and Surgery, 2010. 5(4): p. 401–409.

79. Mikalef, P., A. Pateli, and R. Van de Wetering. IT flexibility and competitive

performance: The mediating role of IT-enabled dynamic capabilities. in Proceedings

of the 24th European Conference on Information Systems (ECIS). 2016.

80. Davis, J.P., K.M. Eisenhardt, and C.B. Bingham, Developing theory through

simulation methods. Academy of Management Review, 2007. 32(2): p. 480-499.

a meta-framework for efficacious adaptive enterprise...

Documents