FAKULTAT F¨ UR INFORMATIK¨ - TUM · Management (EAM) dwell upon balancing these partly contradictory targets. One central task ofEAMis the management of the Application Landscape

FAKULTAT FUR INFORMATIKDER TECHNISCHEN UNIVERSITAT MUNCHEN

Masterarbeit in Wirtschaftsinformatik

Modeling Application Landscapes asDynamic Systems

Josef Maximilian Burger

FAKULTAT FUR INFORMATIKDER TECHNISCHEN UNIVERSITAT MUNCHEN

Masterarbeit in Wirtschaftsinformatik

Modeling Application Landscapes as Dynamic Systems

Modellierung von Anwendungslandschaften alsdynamische Systeme

Author: Josef Maximilian BurgerSupervisor: Prof. Dr. Florian MatthesAdvisor: Alexander W. Schneider, M.Sc.Date: September 30, 2013

I assure the single handed composition of this master’s thesis only supported by declaredresources.

Munich, September 30, 2013 Josef Maximilian Burger

Abstract

Today’s markets claim high flexibility, fast reaction rates, and increased precision fromorganizations. These requirements emerge from a highly competitive market due to glob-alization and technological advancements. Particularly information systems need to keeppace with the times and adapt to posed defiances in a timely manner while ensuring con-tinuous viability also during transitional periods. In the scope of Application Landscapes(ALs) and at the present stage Enterprise Architecture Management allocates methods andmodels solely focusing on either structural change or Enterprise Architecture (EA) evolu-tion. Employing system theory on ALs suggests an at least equally important influenc-ing factor on the outcome of change: the system’s behavior. Even though the theory isrenowned, an application to ALs has not been realized yet. Existing approaches to modelsystem dynamics in general are introduced and applicability subsequently discussed –whether it concerns inside dynamics or the behavior as a reaction to environmental influ-ences.

An example dealing with the latter, orientor theory, is described in more detail, furtherdeveloped, and employed in a combination with the theory of complex adaptive systemsto the AL under the assumption of the System of Systems (SoS) approach. It is derivated,that just as the whole is more than the sum of its parts, managing such a system takesmore than shifting all components together. Consequently, measurability and determina-tion of orientors by respective capabilities is outlined before breaking down the overallsystem into its subsystems to finally increase decision making and support AL evolutionby providing the best possible conditions and guidance in respect of SoS theory.

This thesis reveals the limitations of existing models and shows that theory lacks a holisticmodel on dynamics. Nonetheless, depending on the purpose of the model general or spe-cific models are applicable. Furthermore it is shown, that manageability can be increasedthrough a deepened understanding of system dynamics and that the ability to influencebehavior of the AL is restricted.

Even though outcomes are never well and truly predictable, knowing about external in-fluences through applying presented model increases the awareness of coherences andnecessities when changes in the system are vital. The ability to explain dynamics in ALsand the importance of behavior in such a system involving humans, enables managementto apply the right measures at the right time at the right place without losing sight of pos-sible consequences from internal or external reactions. Presented theory can help to findthe right breadth and depth of dividing ALs into its subsystem and to apply managementmeans such as EA principles according to suitable clusters for an increased viability of thesuprasystem, in between control and self-organization.

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Contents

Abstract vii

List of Figures xi

List of Tables xiii

Nomenclature xv

1 Struggling for Life: from Biology to Information Systems 11.1 Time and Money as Motivational Aspects to Examine Dynamics . . . . . . . 31.2 On the Lacking Application of Dynamics Theories to EAM in Existing Works 6

1.2.1 Dynamics and Change Management . . . . . . . . . . . . . . . . . . . 61.2.2 Models of ALs and EA Evolution . . . . . . . . . . . . . . . . . . . . . 8

1.3 Design Science for Questions on Conflating Dynamics with Modeling for ALs 10

2 Models, System Theory, Cybernetics, and Evolution: Applied Theories 152.1 Modeling Real World Problems: Requirements, Qualities, and Benefits . . . 152.2 The Origin, Classification, and Nesting Property of Systems in General . . . 192.3 Behavior in Dynamic Systems is Omnipresent but Least Understood . . . . 222.4 System Environment and Complexity . . . . . . . . . . . . . . . . . . . . . . 252.5 Complex Adaptive Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3 Approaches to Model Behavior in Systems and Applicability to ALs 333.1 Distinguishing from Existing Structural Models . . . . . . . . . . . . . . . . 333.2 Modeling Behavior in Systems: Feedback, Loops, and System Archetypes . 343.3 Modeling Behavior in Systems: Activity Diagrams . . . . . . . . . . . . . . . 393.4 Components to Model Behavior: Inside and Outside the System . . . . . . . 413.5 Seeing the Bigger Picture: Leaving Inside Behavior to Enter the Environment 46

4 Exogenous Influences Driving the System’s Behavior 494.1 Elaborating and Classifying Six Different Environmental Influence Factors . 49

4.1.1 Normal State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504.1.2 Scarce Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.1.3 Variety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524.1.4 Other Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 534.1.5 Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544.1.6 Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

4.2 Applying Orientor Theory to Deal with Classified Environmental Influences 564.3 Describing System Behavior as an Interplay of Basic Orientors . . . . . . . . 62

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Contents

5 Breaking Down ALs into Subsystems 655.1 From the System to Individual Subsystems in AL . . . . . . . . . . . . . . . 655.2 Examining Subsystem Behavior in the AL by Applying CAS Theory . . . . 665.3 Capabilities Determining Orientor Satisfaction in AL . . . . . . . . . . . . . 70

6 Managing in the AL: Condition Providing and Subsystem Guiding 756.1 Providing the Best Possible Conditions for the Subsystems to Self-Organize 75

6.1.1 Aggregating Subsystem Orientor Manifestations . . . . . . . . . . . . 756.1.2 Assessing Orientors for Different Forms of IT Organization . . . . . 776.1.3 Clustering and Choosing the Best Suitable Form of IT Organization . 84

6.2 Applying EA Principles to Guide in Subsystems . . . . . . . . . . . . . . . . 84

7 Summary, Critical Reflections, and Future Work 937.1 Recapitulation of Raised Research Questions and Critical Reflection . . . . . 947.2 Further Development and the Outlook on Future Work . . . . . . . . . . . . 96

References 99

x

List of Figures

1.1 Holling’s Four Ecosystem Functions . . . . . . . . . . . . . . . . . . . . . . . 71.2 The Co-Evolving / Viable System Path by Kandjani et al. . . . . . . . . . . . 91.3 Layers from EA Structure to IT Success . . . . . . . . . . . . . . . . . . . . . 101.4 Hevner’s Three Cycle View of Design Science Research . . . . . . . . . . . . 12

2.1 Guizzardi’s Model Qualities: Lucidity and Soundness . . . . . . . . . . . . . 162.2 Guizzardi’s Model Qualities: Laconicity and Completeness . . . . . . . . . . 172.3 Krogstie’s Eight Model Qualities . . . . . . . . . . . . . . . . . . . . . . . . . 182.4 Basic Graphical Representation of a System . . . . . . . . . . . . . . . . . . . 212.5 Beer’s Viable System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262.6 The Co-Evolution Path Model by Kandjani et al. . . . . . . . . . . . . . . . . 29

3.1 From an Ideal to a Biased Feedback . . . . . . . . . . . . . . . . . . . . . . . . 353.2 System Archetype: Reinforcing Feedback with Delay . . . . . . . . . . . . . 363.3 System Archetype: Balancing Loop . . . . . . . . . . . . . . . . . . . . . . . . 363.4 System Archetype: Drifting Goals . . . . . . . . . . . . . . . . . . . . . . . . . 373.5 System Archetype: Shifting the Burden . . . . . . . . . . . . . . . . . . . . . 373.6 System Archetype: Escalation . . . . . . . . . . . . . . . . . . . . . . . . . . . 383.7 System Archetype: Fixes that Fail . . . . . . . . . . . . . . . . . . . . . . . . . 383.8 System Archetype: Tragedy of the Commons . . . . . . . . . . . . . . . . . . 393.9 Structure, Behavior, and Dependencies in UML Semantics . . . . . . . . . . 403.10 Exemplary UML Activity Diagram with Interruptible Activity Region . . . 403.11 Reducing a Multitude of Interrelations to Two Links Connecting Elements . 45

4.1 Bossel’s Six Basic Characteristics in Two Layers Surrounding ALs . . . . . . 504.2 Bossel’s Basic Orientors Towards Characteristics of the Environment . . . . 574.3 Different Levels of Orientor Satisfaction . . . . . . . . . . . . . . . . . . . . . 63

5.1 Evolution of the EAI Towards a Living Organism . . . . . . . . . . . . . . . 675.2 Capabilities Facilitating Orientor Manifestation . . . . . . . . . . . . . . . . . 73

6.1 Aggregation of Subsystems’ Orientors Involving Environmental Influences 766.2 Mapping EA Principles’ Influence on Orientors . . . . . . . . . . . . . . . . . 91

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List of Tables

1.1 Applying Seven Design-Science Research Guidelines by Hevner et al. . . . . 13

2.1 General Classification of Systems According to Different Properties . . . . . 20

6.1 Assessing Orientor Manifestation in a Centralized IT Organization . . . . . 796.2 Assessing Orientor Manifestation in a Decentralized IT Organization . . . . 816.3 Assessing Orientor Manifestation in a Federal IT Organization . . . . . . . . 83

xiii

Nomenclature

AL Application LandscapeBEAMS Building Blocks for Enterprise Architecture Management SolutionsBPR Business Process ReengineeringBU Business UnitBYOD Bring Your Own DeviceCAS Complex Adaptive SystemsCE Complexity of the EnvironmentCEO Chief Executive OfficerCLD Causal Loop DiagramCMMI Capability Maturity Model IntegrationCPM Critical Path MethodCPU Central Processing UnitsCS Complexity of the SystemCSF Critical Success FactorEA Enterprise ArchitectureEAI Enterprise Application IntegrationEAM Enterprise Architecture ManagementGB Governance BoardGRC Governance, Risk Management, and ComplianceIS Information SystemsIT Information TechnologyITIL Information Technology Infrastructure LibraryITSM Information Technology Service ManagementKPI Key Performance IndicatorM&A Mergers and AcquisitionsMOF Meta-Object FacilityOMG Object Management GroupOWL Web Ontology LanguageOS Operating SystemPDCA Plan-Do-Check-ActPM Project ManagementPPM Project Portfolio ManagementR&D Research and DevelopmentRDF Resource Description FrameworkRQ Research QuestionSE Software EngineeringSaaS Software as a ServiceSFD Stock and Flow Diagram

xv

Nomenclature

SLA Service-Level AgreementSOA Service-oriented ArchitectureSoS System of SystemsUML Unified Modeling LanguageVSM Viable System Model

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1 Struggling for Life: from Biology toInformation Systems

Competition creates change. Change implies challenge. Whoever adapts first or best tothe environment is a winner in a challenge, hence has an increased probability of survival,ideally success. In most major disciplines like economics, physics, ethnology, and biol-ogy these competitions can be found. The latter is an admittedly obvious example whentalking about survival but it is indeed a good starting point. Darwin’s competition is thewell-known theory about the struggle for life, where he introduced the term of “NaturalSelection” (Darwin, 1859, p. 61). During the evolution, “any variation [...] if it be in anydegree profitable to an individual of any species [...], will tend to the preservation of thatindividual” (Darwin, 1859, p. 61).

Existence of competition is given through the finite nature of resources. While in Darwin’scase it’s first and foremost about food and water, today’s economical competition is aboutthe resources customers, knowledge and information, all driven by globalization. Thechallenge consequent upon this globalization is to meet emerged requirements like shortproduct life-cycles and limited time-to-market, to leverage the capabilities like “productivecapacity, cultural creativity, and communication potential” (Castells, 2011, p. 72), withinthe boundaries of technical feasibility, political regulations and ethical values. Throughcountless developments in the information age we are living in, information sharing andglobal collaboration is made easy and efficient. So as to cope with the requirements of glob-alization and informationalization, for most enterprises, time is more crucial than space.Consequently, prediction and adaptation of a company to the constantly evolving marketsituation is a major capability and a Critical Success Factor (CSF) (Ulrich & Lake, 1991;Wang & Ahmed, 2007).

Information Systems (IS) are undoubtedly major enablers of globalization and informa-tionalization but also of new business processes and new business models (Krcmar, 2010,p. 35). But through its ever-increasing importance, the omnipresent Information Tech-nology (IT) in companies also fosters complexity and makes its management vital to ul-timately increase profits – not only for large enterprises. Profitability can be achievedthrough an increase in IT effectiveness or IT efficiency (Helbig, 2012, p. 45). While thelatter is attained through reducing IT costs, the former is reached either by diminishingthe company’s total costs or increased sales by e.g. establishing new products or servicesor tapping into new markets by reaching more customers. Both objectives are interdepen-dent and closely related to business goals. IT management and Enterprise ArchitectureManagement (EAM) dwell upon balancing these partly contradictory targets. One centraltask of EAM is the management of the Application Landscape (AL) (Buckl, Ernst, Matthes,& Schweda, 2009a, p. 1) which this thesis focuses on. Usually, the bigger an enterprise is

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1 Struggling for Life: from Biology to Information Systems

the larger its AL is. Another influencing factor on the size of the AL is its heterogene-ity: greater variety (e.g. in business processes, products) leads to an increased demand ofapplications and interfaces. A large and heterogeneous AL usually results in augmentedcomplexity which makes its management more time- and resource-consuming. ALs aresubject to more or less regular changes due to arising new technologies, business processre-engineering or amendment of laws. Any change in an AL has effects on more thanjust one application and often includes and affects many other applications, interfaces andstakeholders (Buckl, Matthes, & Schweda, 2010; Ernst & Schneider, 2010; Lankhorst, 2005;Zachman, 1997).

Traditional models and analysis of ALs primarily make use of a structural description ofthe applications, for example their relationships and interfaces, supported business pro-cesses and who is responsible for which application. Modeling an application landscape’sstructure at the right degree of abstraction or granularity is sufficient for a green fieldapproach. Taking above mentioned evolving markets and technologies into account, aconstant change in ALs makes models necessary, which also describe the additional di-mension of time-evolution. Down to the present date, no process exists, which updatesmodels of landscapes automatically (Farwick et al., 2010, p. 35).

While this time-evolution – which according to Luenberger (1979, p. 1) is nearly synony-mous with the term ’dynamic’ – explains the first part of the goal, to model ALs as dynamicsystems, the second part – the system – still remains. A distinct and clear description ofhow a system in this modeling context is defined is given in Chapter 2. But in order totouch on the topic of modeling systems and to grasp the goal of this thesis it is important toknow that in a general sense a system consists of interacting elements, has a definite borderand a surrounding environment with which the system may interact through interfaces.A company’s AL can be deemed as such and adding the above mentioned time-evolutionthrough external or internal developments, it can be considered a dynamic system.

The difficulty faced lies in finding the least complicated useful model, in other words todevelop a model that the business benefits from and still is resource-sparing and not toosophisticated. In accordance with Stachowiak (1973) a good model has three defining char-acteristics: mapping, reduction and pragmatics. With the reduction characteristic whichcalls for leaving out attributes that are irrelevant for the model user, we get back to the AL,which can be seen in many different ways and modeled in any size and granularity. Theformer varies depending on which and how many elements and interfaces the modeledsystem contains. Granularity differs as an AL can be looked upon as one system or as aSystem of Systems (SoS) and so on. For this reason, it is important to define the desiredgranularity and whether the landscape should be modeled as a set of black boxes in aservice oriented view or more detailed with all applications.

To sum up, this work tries to find the least complicated useful model of ALs as dynamicsystems. Therefore, the system, its elements, boundaries and environment, the right sizeand abstraction of the model and the dynamic aspect of an ever-changing AL adaptingto business needs, technological conditions and altered laws are discussed. Eventually,the dynamic model should spotlight effects of changes in ALs and, for example, businessdecisions on the realization of projects. Unlike in Darwin’s natural selection, where variety

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1.1 Time and Money as Motivational Aspects to Examine Dynamics

origins from defects and happen by accident, companies have to continuously work ontheir evolution in order to survive and ideally be at the top of the food chain.

1.1 Time and Money as Motivational Aspects to ExamineDynamics

Without any doubt, the ultimate goal of the vast majority of businesses is profit. Unlessa company is not in the happy position of running its business in a closed system like onan isolated island, having a monopoly or having good relations within an oligopoly, profitis subject to competition. Depending on the market scope of the business, according toPorter (2008, pp. 11-18) a segmentation, differentiation or cost-leadership strategy can bechosen to deal with this competition. Accordingly adjusted IT strategy contributes to thesuccess of the business strategy as it may either focus on IT effectiveness through supportof specialization or variety or focus on IT efficiency through cutting costs (Helbig, 2012,p. 45). One major means of aligning IT with business is the Enterprise Architecture (EA)with a gaining importance correlating with the growth of competition and needs to change(Bernus, Nemes, & Schmidt, 2003, p. 1; Luftman, 2003; Schekkerman, 2005, p. 16; Winter &Fischer, 2007, p. 5; Zachman, 1997).

While competition can be seen as the main cause to change, events from different sourcesare the triggers of single change initiatives. In keeping with their origination, these triggerscan be divided into external and internal events. External events include fairly long-termdevelopments like political, cultural or legislative changes as well as short-term triggersfrom emergence of new technologies via hacker attacks through to bankruptcy of a sup-plier. Internal events refer to triggers originating from the engagement of key-personnel,business process re-engineering or repercussions of projects (Bernus et al., 2003, p. 312;Breu, 2010; Rouhani & Nikpay, 2012). Not every trigger (e.g. the bankruptcy of a supplier)results in the necessity to amend the EA and Bernus et al. (2003, pp. 312ff.) focus on po-tential effects on the enterprise rather than the scale. Triggers with potential effects are –what he calls – significant events and described as those events “having consequences thatthe company cannot deal with using its present management and operational capabilities”(Bernus et al., 2003, p. 313). Ideally, every significant event causes change in order to dealwith the possible consequences, minimize risks, keep the business running and maintainthe market share. Despite the distinction of competition as cause and significant eventsas triggers, both are not independent. What is more, both can be represented through apositive feedback cycle: competition produces change which in turn increases competitionagain.

Any company which cannot keep up with the requirements and consequences of constantchange will not prevail in the long run. Change is “the biggest challenge facing the modernEnterprise” and EA the “determining factor [...] that separates [...] the survivors from theothers” (Zachman, 1997, pp. 1f.). Subsequently, challenges of change are outlined as wellas opportunities motivating to care about change and ultimately profit from it.

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Challenge: existence of competitive exclusion

Adhering to the picture of the struggle for life and surviving in a competitive environmentas Zachman (1997) did, another analogy originating from biology is standing to reason.In fact, the intersection of ecological and economic analysis is considerable here. Lookingat open competitions like the share of a company in its market or the share of a speciesin its territory – both underlie the ongoing need to change and specialize as both systemswill never reach equilibrium (Costanza, Wainger, Folke, & Maler, 1993, p. 545). It’s not justthat there is no final goal like equilibrium to be aimed at, but what is more – the principleof competitive exclusion is applicable here, saying that only the most fit can survive in acommon environment. In the case of a biological system with the fight for food and otherresources, similar species only coexist over a long period of time, if they

evolve distinct differences in their food and habits. Each of the species tendsto occupy a unique ecological niche so it does not directly compete with otherspecies. (Luenberger, 1979, p. 328)

Fighting for customers or other resources and revisiting the strategies of Porter (2008), thesame applies for companies having either to occupy a unique economical niche (differenti-ation or cost-leadership) or delimit the common environment through creation of artificialborders (segmentation).

Opportunity: influencing change

In order to escape competitive exclusion, enterprises have, in contrast to species, the op-portunity to influence their fate because their competitive environment is faster, more pre-dictable and reactive. Firstly, in contrast to the slow process of genetic evolution, whereoccupying a ecological niche takes thousands of years, evolution of economy and espe-cially technologies is considerably faster (Costanza et al., 1993, p. 551). This speed is both,a threat and opportunity for companies at the same time as they have to keep chang-ing in order to survive but they can also reap the rewards of successful changes in short-or medium-term – but surely within a lifetime. Secondly, the adaptation to a changedmarket environment is, to a certain extent, foreseeable and not subject to anomalies in en-zymatic cleavages. So in contrast to species which are highly dependent on contingencyand the mercy of nature, companies can actively trigger change processes. In addition tothe general speed and active participation, the third upside of a economic competition asagainst ecology is the reactivity or sensitivity with which the market responds positivelyor negatively to amendments. While, for example, consequences of human activities onbiodiversity and the survival of species can only be fully realized after a considerable timelag of several decades (Dullinger et al., 2013), an altered economic environment affectsa company’s success within a narrow time frame. This yields in the abilities to assesscausality easier and to assign effects to causes. With causality in turn, the ability to testand afterwards observe the results arises which is barely possible neither in biological testenvironments nor in nature, where extinct species can – as of today – not be restored.

Challenge: people do not like change

Aforementioned necessity to adapt to the environment is also mentioned by Bernus et al.(2003), who emphasize the importance of the transition method, stating that

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1.1 Time and Money as Motivational Aspects to Examine Dynamics

change is necessary for the enterprise to retain its competitive advantage, thechoice of the transition method is the one management ofter finds to be themost difficult. It is this choice which often determines the future success of anenterprise. (Bernus et al., 2003, p. 318)

But even though it is necessary and change in enterprises underlies aforementioned op-portunities, it has downsides, too. One downside affects stakeholders as any change in-volves risk and uncertainty and people generally like none of the two. But taking risksand bearing with uncertainty is inevitable which is why stakeholders have to be part inthe whole change management process. So in addition to analyzing explicit actualities,like the necessity for change or the management and effects of changes, involving peopleand considering tacit notions, concepts and requirements are at least as important (Bernuset al., 2003, p. 319). Often, the challenging part of change in EAs is convincing stakeholder“to adopt new systems, to change their processes, to optimize processes”(Matthes, 2013)rather than conducting the actual change of the system. The majority of people, who donot seek risk and uncertainty, prefer staying in their comfort zone of routines and avoid ne-gotiating, learning, establishing or losing responsibilities (Bernus et al., 2003, pp. 255ff.).

Opportunity: EAM as leadership tool

Communication can overcome these perils of stakeholders defending the status quo anddenying change. Therefore, EAM contains suitable tools to communicate the status quo(or as-is state) and future developments like depiction of the target state or planned statesof an EA (Buckl, Ernst, et al., 2009a, p. 3; Lankhorst, 2005, pp. 67ff.). Models of ALs, for ex-ample, are viable communication instruments and can be used for a better understandingof dependencies, explain necessities to change and thus increase acceptance.

Challenge: static focus of existing approaches

Three main goals are assigned to EAs, which is documentation of the as-is architecture,support the design of a to-be architecture and to facilitate the transformation from the cur-rent state to the target state through scenarios and roadmaps (Buckl, Ernst, Matthes, &Schweda, 2009b, p. 1; Fischer, Aier, & Winter, 2007, p. 2). Reading these three main goalsand the description of a transformation process one would expect the need for dynamics.There are many EA frameworks which can be used to meet standards, adhere to laws andregulations, and align business with IT. But so far, concerning models of ALs, most ap-proaches bring the static description into focus rather than the dynamic behavior. Thesestatic descriptions are important, for example, for enterprises to manage distributed sys-tems, analyze supported business processes by an application or identifying accountableorganizational units (Buckl, Ernst, et al., 2009a, p. 6). But in contrast to dynamic behav-ior, they lack important information like stakeholders, dependencies between people oroperating delays which are nowadays also needed to cope with constantly changing re-quirements.

Recapitulating, the motivation for this thesis is tripartite. Modeling ALs as dynamic sys-tems firstly supports EAM to find solutions to deal with the increasing demand of changeto survive competition. Secondly, such a model can be used as a communication tool toexplain time-evolution, the repercussions of change and thereby enhancing acceptance of

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stakeholders. And thirdly, add monetary value to the company through reduction of risksand uncertainty and at best foster IT efficiency and IT effectiveness.

1.2 On the Lacking Application of Dynamics Theories to EAM inExisting Works

Following the outline of motivational aspects for this thesis, this section describes workswhich are related to the topic of modeling ALs as dynamic systems. These works eitherallocate concepts and theories which are used subsequently, apply a similar research ap-proach or utilize comparable analogies. Some of these works have already been addressedas a source of motivation for this thesis while others haven’t been introduced yet and are ei-ther used as supportive input or to describe the scope of this thesis and dissociation fromthose works. Subsequently, two different categories are used to classify related works:general change management with dynamics and evolution of EA models.

1.2.1 Dynamics and Change Management

Following a general definition of Hornby (2000, p. 394), dynamics either describes “theway in which people or things behave and react to each other” or “a force that produceschange, action or effects”. When applying the dynamics aspects to system theory, SystemDynamics Society (2013) delineates the notion more precisely. Their approach also con-tains the time aspect and an endogenous, behavioral view but additionally also delineatescontinuous quantities with feedback loops and circular causality, as well as stocks withinflows and outflows – all for the purpose of defining and solving problems dynamicallyand implement change in accordance with insights from modeling the system’s dynamicsaspects.

A general introduction to dynamic systems is also given by Luenberger (1979) who de-scribes time-evolution of linear and non-linear systems and explains derived phenomena.Luenberger (1979) defines continuous and discrete time-evolution, describes possible ma-nipulations like open-loop control or feedback control and explains the need of observ-ability and controllability to ensure a minimum stability. Although originating from andhaving a strong focus on mathematics, Luenberger (1979) cites examples of important dy-namic systems and the application of the concepts introduced. Among those are systemsin genetics, epidemics, interacting populations, energy in mechanics or stability of compet-itive economic equilibria. Picking up on nonlinear aspects and feedback loops, Costanza etal. (1993) describe these two aspects together with strong interactions between parts as thecharacterization for complex systems and try to get to the bottom of modeling ecologicaleconomic systems. Notwithstanding that their main focus lies on evolutionary aspects ina biological sense, the authors’ approach to modeling can be used in any complex system.They make use of Holling’s principles, who describes the trade-offs of modeling betweenrealism, precision, and generality. Maximum generality is provided by the Holling model,

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1.2 On the Lacking Application of Dynamics Theories to EAM in Existing Works

which distinguishes between the states exploitation, conservation, release, and reorgani-zation which are classified by two variables: connectedness and stored capital as depictedin Figure 1.1.

Figure 1.1: Holling’s four ecosystem functions,Source: Holling (1992)

These four states differ in their degree of connectedness and stored capital, as well asthe speed of the transition between the states differs, depicted through the density of thearrows (Holling, 1992, p. 481). Costanza et al. (1993) use this model and apply it to an eco-nomic context adding entrepreneurs to exploitation, rigid bureaucracies to conservation,political upheavals to release and abundant natural resources to reorganization (Costanzaet al., 1993; Holling, 1992). This, for one, is an exemplary related work using a widely ac-cepted model from biology and applying it to a general description of complex economicecological systems.

Another approach dealing with dynamics in the area of IT is undertaken by Kramer andMagee (1990) who develop models and methods for dynamic change management inSoftware Engineering (SE). The evolving philosophers problem, how the authors call it,is about the well-known dining philosophers problem by Dijkstra (1971) which the authorsextend by a dynamics aspect where a change in the system like adding or deleting oneelement shall be possible without deactivating the whole system and to keep it runningas constant as possible. Consequently, Kramer and Magee (1990) extend their model fromindependent elements (dining philosophers problem) to dependent elements with an ex-ample of a printer server and other systems with cyclic and mutual dependencies. Theydeveloped models to handle changes without loss of information and maximum trans-parency. Their approach can be seen as related work as the structure of their system issimilar to ALs and the objectives of dynamic change management described by Kramerand Magee (1990) strongly correlates with the goal of this work:

• changes should be specified in terms of the system structure.

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• change specifications should be declarative.

• change specifications should be independent of the algorithms, protocols, and statesof the application.

• changes should leave the system in a consistent state.

• changes should minimize the disruption to the application system.

To achieve these objectives, for instance, an element has to reach a consistent state andall dependent elements have to be quiescent throughout the change process. The modeldeveloped in this thesis is designed to manage these change processes and to increasetransparency.

1.2.2 Models of ALs and EA Evolution

This second part within related works depicts approaches, having developed models ormethods for evolving ALs or EAs. One approach, which is dealing with change-driven SEconducted by, for example, Breu (2010) or Farwick et al. (2010), calling for living models.These living models are supposed to synchronize models of EAM with the actual run-time environment through further development of roundtrip-engineering. Although thisapproach is also developing models for dynamic EAs, automatically updated models arenot within the scope of this thesis. Another temporal aspect of ALs or EAs is modeled inseveral works of Buckl, Ernst, et al. (2009a). Here, the focus lies on the transition of anas-is state via planned states to one target state of an EA. These transitions are achievedthrough projects, which are modeled and saved with timestamps (e.g. planned for, enddate) to increase traceability of management decisions and check whether the desired out-come has been attained and if the project was completed within the expected time frame.The findings of comparing predictions with actual results can consequently be used forplanning future amendments within the EA and support project management. Followingthis information model depicted as a class diagram, Buckl, Ernst, et al. (2009b) also delin-eate visualizations like a roadmap plan focusing on the time variable in order to supportbusiness decisions and increase transparency. The deployment of these models and visu-alizations will be of help for this thesis, even though the scope is narrowed from EAs toALs and the historization aspect is not in focus here. What was omitted by the previousworks is any kind of stakeholders in the modeled system but is included in the followingwork by Ernst and Schneider (2010) who also describe roadmaps for EA evolution to sup-port project management likewise. Developed EA DEPENDENCY GUIDED PROJECT MAN-AGEMENT consists of four steps in an iterative cycle: dependency identification, businesssupport migration, joint scheduling and Critical Path Method (CPM) recomputing (Ernst& Schneider, 2010, p. 259). Even if many parts like project scheduling are out of scope forthis thesis, their approach, especially dependency identification is essential for modelingdynamic systems in the area of ALs. Therefore, not only rather obvious dependencies likeinterfaces and technical dependencies have to be identified, but also rollout, retirement,and organizational dependencies. In particular, these organizational dependencies play amajor role in order to model the dynamics aspect of ALs provided that evolution in ALs isnot achievable without people such as IT personnel.

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1.2 On the Lacking Application of Dynamics Theories to EAM in Existing Works

Additionally, a further important aspect of dynamic systems is the environment in whichthe system evolves. Here, Kandjani, Bernus, and Nielsen (2013) picture a co-evolution pathmodel describing the need of EAs to find an optimum degree of complexity to outlive com-petition. Constant change is the result of a “mix of deliberate and emerging processes”(Kandjani et al., 2013, p. 3859) and according to the authors, enterprises are not designedsystems but complex adaptive living systems and thus in need of applying cybernetics ap-proaches. Different system states (co-evolving, inefficient, vulnerable and non-viable) aredescribed which derive from the degree of disparity between the Complexity of the Sys-tem (CS) and the Complexity of the Environment (CE) as depicted in Figure 1.2.

Figure 1.2: The Co-evolving / Viable System Path,Source: Kandjani et al. (2013)

A constantly changing environment requires constant changes in the EA and there existsno such thing like a final equilibrium. But dynamic or temporal equilibria can be reachedand therefore a certain degree of requisite variety is needed to be achieved and maintained(Kandjani et al., 2013). On that account, communication and feedback loops are essentialto support ongoing alignment and this again involves people and not solely automatedprocesses. Kandjani et al. (2013) state that

when living organisms (such as people) are part of a system, their actions arenot completely dictated by the system they are part of, nor are they necessarilyguided by logic. Power relations, survival, self-interest, group-interest, valuesystems, culture, etc. are all participating in determining how a system ’playsout’, in other words, however logical the design of a system may be, relying onthe logic of processes is insufficient. (Kandjani et al., 2013, p. 3866)

Even though the authors’ focus lies on the overall system state of EAs, conclusions can bedrawn for the smaller focus of ALs.

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1.3 Using Design Science for Questions on Conflating Dynamicswith Modeling for ALs

Considering the motivational aspects and the already existing related works, most workson EAs only take structural aspects into account to develop models and methods for busi-ness IT alignment. As of today, the time-aspect of ALs is narrowed down to their evolution,for example from an as-is state via planned states to a target state. This evolution over timecan be seen as a description of dynamics but is still comprised of snapshots. Change withinthe architecture respectively between two snapshots is first and foremost the result of ITprojects. Often, these changes do not result in the outcome which was expected beforeand one reason for this is not having considered other important influencing factors on theresult.

In order to elaborate the Research Questions (RQs) which are answered in this thesis, theexisting research gap with lacking consideration of behavioral aspects of the system AL issubsequently described. Essentially, ensuing delineation is pictured in Figure 1.3.

Figure 1.3: Layers from EA Structure to IT Success

Four interdependent layers depict the way from the structural level comprised of the staticarchitecture of the system AL towards the intended IT success. From left to right, an evo-lution is indicated through a time-line from an as-is situation towards a to-be state of thesystem. Usually, management decisions intend to foster this evolution through changesand by measuring outcomes. This is where the behavioral layer is omitted as it is the leastunderstood part of systems.

Decisions to improve the alignment like in project management are mostly based on chang-ing structures or structured processes without consideration of the influences of and im-pact on the behavioral aspect. This is indicated by the arrow connecting the goal to im-

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1.3 Design Science for Questions on Conflating Dynamics with Modeling for ALs

prove a Key Performance Indicator (KPI) with changes (usually projects) on the structurallevel without consideration of the behavioral layer. But understanding behavior is essen-tial to eventually explain business IT alignment, because “the design of organizationalstructure only has a limited effect on performance levels achieved” (Child, 1972, p. 16).

But how can behavior be determined? The connection between structure and behaviorand the importance of the latter is described in the scope of model testing by Forresterand Senge (1979), who assert, that it is important for behavior-reproduction as well asbehavior-prediction to show, that behavior is a necessary consequence of model structure.To determine this correlation, exogenous input variables have to be excluded in such tests.The behavior driven by system dynamics of dependent or independent elements within orfrom outside the system and depicted as a black box in Figure 1.3 is least understood andits effects on success of a system is underestimated. So far, only the structure level of theAL is used to explain the degree of process support and finally business IT alignment.

What is not part of Figure 1.3, but equally important, are other influencing factors driv-ing the behavior of the system AL. Excluding exogenous input variables as Forrester andSenge (1979) do, to determine that behavior is a result of the underlying structure, impliesthe importance of environmental influences and that they have to be modeled as well tofully understand system behavior. While there is a focus on external influences in thisthesis, another barely calculable factor is not addressed in-depth: psychological aspectsof human decisions and behavior of social systems, which can be counter-intuitive anddecisions can be wrong, especially “when people are faced with complex and highly inter-acting systems” (Forrester, 1971, p. 1).

Down to the present day, no work has modeled ALs as dynamic systems. Therefore, theRQs for this thesis have been developed which are outlined in the following and examinedby the means of subsequently described methodology.

Research Questions Previously mentioned motivational aspects led to the research gapwhich is described through Figure 1.3. Narrowing down this gap and bringing light intothe darkness of the depicted black box of dynamic behavior is the goal of this thesis withfollowing RQs:

1. RQ 1: Does understanding the application landscape’s dynamics increase its manageability?

2. RQ 2: Where and to which degree is the application landscape’s behavior influenceable?

3. RQ 3: Which means are applicable to model dynamics of and within application landscapes?

Design science to approach open questions As opposed to behavioral science whichtries to explain or predict human or organizational behavior, design science is appliedwhenever innovative artifacts are developed to extend existing boundaries of human andorganizational capabilities (Hevner, March, Park, & Ram, 2004). In virtue of developingan artifact embodied in a new model, design science is applied to guide this thesis. Subse-quently it is explained, how design science is applied here based on their original work ondesign science (Hevner et al., 2004, p. 83). It contains a framework with seven guidelines

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which is used to explain adherence to the idea of design science. Even though the origi-nal work is used for the description, Figure 1.4 depicts a further development of Hevner(2007) to help classifying the guidelines with the environment and the knowledge base.The original work can be used in accordance with the three cycle view.

Figure 1.4: Hevner’s Three Cycle View of Design Science Research,Source: adapted from Hevner (2007, p. 2)

Two major influencing factors on the actual design science research can be seen on ei-ther side. Firstly, the environment which consists of people, organizations, technical sys-tems, and problems & opportunities. These constituents are needed to collect require-ments and conduct field testing and is thus describing the relevance cycle of the researchwork. Secondly, the knowledge base comprises scientific theories & methods, experience& expertise, and already existing meta-artifacts. By using the knowledge base and apply-ing grounding theories to design the new artifact, the latter is finally returning to thesefoundations to increase the knowledge base. This is illustrated by the rigor cycle. The in-ner cycle describes the actual design science process as an iterative cycle of building andimproving design artifacts or processes through evaluation. Table 1.1 depicts seven guide-lines, taken from Hevner et al. (2004) and their original work on design science. Whilethe guidelines and the descriptions are unaltered, the second column refers to the actualrealization within this thesis.

Subsequent theory part is outlining grounding theories and definitions to create a mutualunderstanding of the basis for this thesis and thus strengthens the research rigor in regardto the construction of the model and also adds to problem relevance.

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1.3 Design Science for Questions on Conflating Dynamics with Modeling for ALs

Guideline and Description RealizationGuideline 1: Design as an ArtifactDesign-science research must produce aviable artifact in the form of a construct,a model, a method, or an instantiation.

The viable artifact of this thesis is a model forALs and their dynamics aspect.

Guideline 2: Problem RelevanceThe objective of design-science researchis to develop technology-based solutionsto important and relevant business prob-lems.

Problem relevance has been outlined throughthe description of the existing business prob-lem in the motivation part and the outline ofexisting works which are related to this thesisor embody grounding theories.

Guideline 3: Design EvaluationThe utility, quality, and efficacy ofa design artifact must be rigorouslydemonstrated via well-executed evalua-tion methods.

Before being able to conduct evaluation withpractitioners, presented models have to befurther developed. Thus, evaluation is lim-ited to a descriptive method, using informedargument and scenarios. (Hevner et al., 2004,p. 86)

Guideline 4: Research ContributionsEffective design-science research mustprovide clear and verifiable contribu-tions in the areas of the design arti-fact, design foundations, and/or designmethodologies.

With the design artifact as a general modelwhich is applicable under different circum-stances and design foundations leading tothe model, this work delivers two contribu-tions to the knowledge-base.

Guideline 5: Research RigorDesign-science research relies upon theapplication of rigorous methods in boththe construction and evaluation of thedesign artifact.

Rigorous methods are applied throughoutthe development of the model from its con-struction to its evaluation. An applicable andgeneralizable artifact is thus created by an ef-fective use of the knowledge base. Therefore,literature review and application of ground-ing theories is used in this thesis.

Guideline 6: Design as a Search ProcessThe search for an effective artifact re-quires utilizing available means to reachdesired ends while satisfying laws in theproblem environment.

The goal of this thesis is the development ofan applicable and useful model satisfying theneeds of the environment. The developmentof such a model is invariably in need of sev-eral iterations and as such a search process.

Guideline 7: Communication of Re-searchDesign-science research must be pre-sented effectively both to technology-oriented as well as management-oriented audiences.

As this thesis is published to the general pub-lic, this guideline is inherently satisfied.

Table 1.1: Applying Seven Design-Science Research Guidelines,Source: adapted from Hevner et al. (2004, p. 83)

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14

2 Models, System Theory, Cybernetics, andEvolution: Applied Theories

Following the introductory part of this thesis with the motivational aspect and delineationof related work, this chapter is about finding a general understanding of the concepts un-derlying the development of the model describing ALs as dynamic systems. To build abasis with clear definitions and scopes, the most important concepts are outlined subse-quently. This includes some mature concepts, which are thoroughly researched as well asemerging theories which also derive from practice rather than being deeply entrenchedin theoretical research. In both cases, existing theory and definitions of the concepts areoutlined and constitute the main part of the research rigor, which was previously out-lined as part of the design science approach, which is applied here. For the constructionas well as the evaluation of the artifact, grounding theories have to be used (Hevner etal., 2004). Major parts of the following notions, including models, system and system of sys-tems, system dynamics or dynamic systems, system environment and complexity, and ComplexAdaptive Systems are later on used for the development of the model equating to the arti-fact. Minor parts of notions, including model will also be of help to eventually evaluate theartifact. The chapter is structured in a concept-centric approach as proposed by Websterand Watson (2002, p. 17), so whenever relevant literature is contributing to more than onesubsequently mentioned concept, authors may appear several times.

2.1 Applying Models to Real World Problems: Requirements,Qualities, and Benefits

As the developed artifact is comprised of a model, the general definition, the notion andthe requirements of a model are essential for the development and evaluation of this the-sis’ outcome. Past works attending to models and modeling techniques classes amongthe mature theories, providing a wide base of groundings. Relevant characterization ofmodels made in dictionaries usually equate a model with “a simple description of a sys-tem, used for explaining how sth works or calculating what might happen, etc” (Hornby,2000, p. 819). Even though this description already uses the notion of a system, which isdelineated thereupon, it is as such not yet necessary for the following review.

Naturally there exist models in countless fields of application, in both, physical and non-physical environments. In the subsequently outlined works, the focus lies on conceptualor system modeling. Conceptual or system models can be classified as abstract modelswithin the nonphysical share of models and are used for the development of the descrip-tion of ALs. Therefore, the following works derive from the field of conceptual or system

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2 Models, System Theory, Cybernetics, and Evolution: Applied Theories

modeling, including requirements and frameworks and at the most have a focus on orconnection to IS and EAs.

One prominent approach to impose requirements on a model is undertaken by Stachowiak(1973, pp. 131ff.), who describes three characteristics:

Representation A model is always a model of something, namely a surrogate or repre-sentation of natural or artificial originals, which can be models themselves.

Reduction A model commonly does not capture all attributes of it corresponding origi-nal, but only those, which are relevant for the model creators and/or model users.

Pragmatism Each model is made for a distinct time frame, a dedicated purpose, and cer-tain users.

As the developed model is designed to be applied in real world examples of ALs, it satis-fies by definition the requirement of representing originals. Reduction is achieved throughomission of rather unnecessary details like programming language or price of an applica-tion and focusing on important features like dependencies and users. Depending on thededicated purpose and intended users of the model, these attributes vary. In order to findthe least complicated useful model, the pragmatism requirement has to be defined firstand the reduction requirement can be taken care of thereafter. This is discussed, i.e. inSection 4.2 where environmental influences are subsumed to six characteristics in order toabstract from the real world.

Another attempt specifying requirements to define the quality of a model is published byGuizzardi (2005). To ensure a highly qualitative model, both, representation of the originaland interpretation of the model have to be injective as well as surjective. The followingfour concepts describe the combinations of the original and it’s model and are exemplarydepicted in Figure 2.1 and Figure 2.2.

Figure 2.1: Lucidity and Soundness as Model Qualities for Representation,Source: adapted from Guizzardi (2005, pp. 30ff.)

Lucidity or injective representation: every entity in the model represents at most one en-tity of the original. Otherwise the model entails construct overload, which is undesiredas it needs additional knowledge or causes ambiguity.

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2.1 Modeling Real World Problems: Requirements, Qualities, and Benefits

Soundness or surjective representation: every entity in the model represents at least oneentity of the original. Otherwise the model entails construct excess, which is undesiredas these entities remain meaningless and users can’t map the model’s entity to theoriginal, hence, there is no clarity of the model.

Figure 2.2: Laconicity and Completeness as Model Qualities for Interpretation,Source: adapted from Guizzardi (2005, pp. 30ff.)

Laconicity or injective interpretation: every entity from the original interprets at mostone entity in the model. Otherwise the model entails construct redundancy, which isundesired as it produces uncertainty for users.

Completeness or surjective interpretation: every entity from the original interprets atleast one entity in the model. Otherwise the model entails construct incompleteness,which is undesired as entities from the original would have to be omitted throughlack of power of the model.

While these four components by Guizzardi (2005) are designed to determine the qualityof a representing model, the framework of Krogstie (2002) includes eight parts and is ap-plicable to measure the quality of a design model. This framework is depicted in Figure2.3 and comprises the following qualities. The descriptions are mainly adapted from thesynopsis of Buckl (2012).

• Physical quality: Does the model capture the modeler’s domain knowledge?

• Empirical quality: Is the model readable and understandable with a low error fre-quency?

• Syntactic quality: Does the model conform to a modeling language?

• Semantic quality: Does the model cover the modeled domain?

• Perceived semantic quality: Does the model correspond to the users’ knowledge aboutthe domain?

• Pragmatic quality: Can the model be interpreted by the model users?

• Social quality: Does the model facilitate user discussion on the domain?

• Tool quality: Can the model be interpreted by a modeling tool?

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Figure 2.3: Krogstie’s Eight Model Qualities,Source: adapted from Krogstie (2002, p. 3)

These quality aspects of Krogstie (2002) and Guizzardi (2005) are already considered dur-ing the development of the model but will primarily be used and described later in thedesign search process – especially for refinement and evaluation purposes. But dwellingon the bottom part of Figure 2.3, the requirements on and the choice of the modeling lan-guage has to be further examined as it embodies an essential part of every model.

Therefore, Karagiannis and Kuhn (2002) define a metamodel for modeling languages. Forthis purpose, they outline three essential characteristics to describe modeling languages:syntax, semantics and notation. While the syntactic and semantic quality have alreadybeen introduced in the previous framework, notation has not been mentioned yet. Someworks equate notation with syntax and only distinguish between syntax as “a possiblyinfinite set of elements that can be used in the communication” and semantics as theirmeaning (Harel & Rumpe, 2000, p. 3). Even though a dissociation of the three characteris-tics is not always easy as they are contingent on each other, it is important to describe anddevelop them individually. Especially because metamodeling is first and foremost used todefine the abstract syntax of a modeling language whereas the semantics or the meaningrequires (implicit) knowledge of the modeled domain (Kent, 2002, p. 12). But before goinginto detail about metamodeling, the three characteristics syntax, semantics and notationsare outlined subsequently, following the approach of Karagiannis and Kuhn (2002).

Syntax can either be defined through a grammar or a meta model and contains the descrip-tion of possible elements and applicable rules to create the model or the actual instance.Grammar-based definitions of a syntax include term or graph rewriting systems and are

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2.2 The Origin, Classification, and Nesting Property of Systems in General

easy to use and tool friendly but often lack power and cannot delineate all concepts ofthe modeled domain. Therefore, grammar-based syntax definitions are not suitable forconceptual modeling and thus not applied in this thesis. However, the second possibilityof describing the syntax through a meta model is applicable as it eradicates the disad-vantages of a purely grammar-based approach. Through a higher abstractness, not onlythe elements can be described but also their relationships to each other. Therefore, an-other modeling language has to be applied, which may admittedly confine the precisionof the model, but if chosen and applied accurately, it saves the modeler from reinvent-ing the wheel. Examples of such meta modeling languages, depending on their degreeof abstractness, are Meta-Object Facility (MOF) or Unified Modeling Language (UML) forobject-oriented languages and Resource Description Framework (RDF) or Web OntologyLanguage (OWL) for general knowledge representations (Buckl, 2012).

Semantics can also be defined in several ways. More precisely, the semantic domain canbe defined and described whereas the second part of semantics, the semantic mapping isdone by the model user. The semantic mapping is the connection between a syntacticalconstruct, a relationship and the semantic domain and represents the actual meaning inthe context. In descending order of formality, the semantic domain of a model, its ele-ments and the relationships can be defined algebraic, axiomatic, operational, denotationalor textual. Depending on the requirements on a model like precision, formality or safety,the choice of the most suitable semantic definition can be made.

Notation represents the visualization of the modeling language and thus incorporates thegraphical elements into the model. Again, a differentiation between two major approachesis possible: static and dynamic notations. Static notations are limited to defining visual-ization elements or constructs such as vectors, lines and boxes and as such do not considerthe state of a construct. On the other hand, dynamic approaches are able to model statesthrough separating representation and control and are, as a consequence, more sophisti-cated. While the representation part is equivalent to the static approach, the control partadditionally contributes rules to query states of the model and to influence representationsaccording to the state of model (Karagiannis & Kuhn, 2002).

Following the definition, the notion, quality measures and the requirements on models andmodeling languages in general, the notion and scope of a system is defined subsequently,to increase the understanding of the subject matter of modeling a system.

2.2 The Origin, Classification, and Nesting Property of Systemsin General

As the term system can already be found in ancient Greek literature, until now, there existcountless definitions and explanations of what a system is and what it is composed of. Inthe literary sense meaning “composition” or “whole compounded of several parts or mem-bers” (Liddell & Scott, 2013), the term has been further developed and used in nearly everydiscipline, such as for physical, cultural or economic systems. Self-evidently, the choice ofmentioned works dealing with theories about systems is mostly restricted to those with

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Class Example

Sourcenatural weather system, organic systemman-made mechanical system, social system

Behaviorstatic logical system, number systemdynamic economic system, political system

Entiretyoverall atomic system, planetary systempartial subsystem, system of systems

Interactionopen human-machine system, economic systemclosed laboratory experiment, designed formal systemisolated energetically closed physical system

Stability(meta-) stable cybernetic system, predator-prey systemunstable some chemical systems, negative feedback systemindifferent ecosystem, evolution system

Table 2.1: General Classification of Systems According to Different Properties

an information system’s background. A further developed but unspecific explanation of asystem is the assertion, that “a system is a collection of entities and their interrelationshipsgathered together to form a whole greater than the sum of the parts” (Boardman & Sauser,2006, p. 118). In order to advance to more specific definitions, the enumeration in Table2.1 shows an overview of possible classifications of a system, largely adapted from GablerVerlag (2013). Each kind of classification is outlined in more detail within this chapter.

Source What most general definitions of systems have in common is, that a system con-sists of elements, which together form a collective entity for some purpose. Theelements are interrelated, interdependent, or interacting within a given boundary,surrounded by an environment and are either naturally emerged (e.g. organic sys-tems, planetary systems) or man-made (e.g. mechanical systems, logical system)(Dictionary.com, 2013; Gabler Verlag, 2013). A for most systems in different disci-plines applicable – and thus abstract – model of a system, is depicted in Figure 2.4.

Interaction Another classification of different systems can be made when looking at thetype of boundary of the system to its environment:

Open systems have at least one element interacting with, interrelating with or inter-depending on at least one other element or system of the surrounding environment.These are called edging elements and in the context of IS describe an interface. Incontrast, closed systems have no edging element and thus no relationship with anyother element or system outside its boundaries. Real world systems are almost neverclosed. In systems theory and laboratory experiments, for example, closed systemscan be artificially designed or produced but still, rarely reach the status of an actualclosed system. Only in physics, there exists another class: isolated systems. Here,closed systems are the class in between open and isolated systems, where energy isstill transferred to the environment but where no transfer of matter is happening. In

20

2.2 The Origin, Classification, and Nesting Property of Systems in General

Figure 2.4: Basic Graphical Representation of a System

this case, only the isolated system has no interaction beyond its boundaries: neithera transfer of energy nor of matter (Pidwirny, 2006). This special case of physics is notrelevant for this thesis, as the terms isolated and closed can be used likewise in thecontext of IS.

Entirety One mature definition describing an open, dynamic and man-made system isgiven by Broy (2012) in the context of SE, precisely defining that a system has

• a system boundary, defining what is part of the system itself and what is outsideof the system,

• an interface (determined by the system boundary), defining

the kinds of interaction between the system and the environment is possible(static/syntactic interface),

the behavior of the system seen from outside (interface behavior, dynamicinterface, interaction view), and

• an inside setup consisting of

the structure and division in subsystems (architecture),

the states and state transitions (state view).

• The interaction and state views are build upon a data model.

• Views can be documented by means of feasible models.

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Here, the architecture is described through the structure and the breakdown of thesystem into its subsystems. This means, that Broy (2012) does not consider the en-tirety classes of previously mentioned classifications and thus ascribes a subsystemstructure to every relevant system. This can be justified as, by general understand-ing, there is almost no overall system apart from controlled laboratory experiments.Every real system constitutes one part in another system and is a part or subsys-tem of an overall system. In contrast to very large system which do not containsubsystems, this type of architecture leads to the notion of a SoS. Even though asubsystem is mainly independent and emerged or built for a specific purpose, it alsocontributes to a certain extent to an overall system by interacting or interrelatingto other elements within this so called suprasystem. Defining such a SoS and thusdistinguishing them from “very large and complex but monolithic systems”, Maier(1998) introduces five characteristics. Operational independence of elements requires,that the elements or subsystems can continue functioning for their own purpose,even if the SoS is disassembled. Managerial independence of elements requires, thatsubsystems do actually operate independently and not only can, when the overallsystem disappears. Evolutionary development assumes, that a SoS evolves over timethrough adding, removing, and modifying functions and subsystems and does notappear as a whole construct at one time. Emergent behavior is ensured, when a func-tion of the overall system is not performed by one specific subsystem but emergingfrom interactions between several subsystems or elements. This is partly describedin the aforementioned definition, where interrelationships form a whole greater thanthe sum of the parts. Geographic distribution is defined to be large, meaning, that onlyinformation can be exchanged throughout the overall system and “not substantialquantities of mass or energy” (Maier, 1998, p. 3).

There are two remaining classifications of a system, namely behavior and stability, whichhave not been explained yet but are separately outlined in the following Section 2.3 andSection 2.4.

2.3 Behavior in Dynamic Systems is Omnipresent but LeastUnderstood

Distinguishing between static and dynamic systems is a classification that, due to its im-portance, is taking its own section. Following, not only the differences between the twotypes of systems are outlined, but also examples and definitions of dynamic systems asthis thesis has a focal point on behavioral aspects of systems. In order to grasp the notionof dynamic systems it is helpful to look at the opposite: static systems. Every system orig-inating from nature is dynamic and only a few man-made systems can be seen as staticones. This includes, for example, aforementioned and theoretically closed systems as inlaboratory experiments and in physics as well as number systems and logical systems inmathematics (Gabler Verlag, 2013). These static systems are all man-made and are not sub-jected to any change over time, even though they might make way for other static systemssuch as a switch from Roman numerals to Arabic numerals. In contrast, dynamic systems

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2.3 Behavior in Dynamic Systems is Omnipresent but Least Understood

can be characterized by change over time. Depending on the field of study or the contextin which the system is situated, diversified causes, triggers, regularities, procedures andseverity of change processes are observable. Before describing the dynamics aspect of ALsto develop the model in this thesis, which is done in subsequent Chapter 3, the followingexamples mostly originate from other disciplines, where the dynamical aspect is a relevantpart to study and understand these systems.

There is a need to understand a system including its structure and behavior but there existsa lack of knowledge even of those systems surrounding people, such as their social systemthey live in, the physical systems they work with, political systems they are governedby, and economic systems they are living of. “Man adapted to systems without feelingcompelled to understand them” (Forrester, 1969, p. 1). A dynamic system is comprisedof its structure and its behavior (Broy, 2012; Forrester, 1994; Graham, 1977; Luenberger,1979) where its structure is easier to depict, to change and to understand but its behaviorat least equally important. The difficulty often arising in understanding dynamic systemsunderlies in the fact, that both parts, structure and behavior, are correlated but one cannever explain the other one entirely (Graham, 1977, p. 27) and this causes non-linearitywhich makes it by far harder to understand for people than linear relationships (Dorner,1995). Probably for this reason, the focus of models in all different kinds of disciplinesfocus on the structure of the system rather than on its dynamics aspect which incorporatesthe rather unstable behavioral aspect.

An approach of Boulding (1956, p. 208) in general system theory describes the “unwill-ingness of science to admit the very low level of its successes in systematization, and itstendency to shut the door on problems and subject matters which do not fit easily intosimple mechanical schemes”. He describes behavior as restoring preferred states of lowerindividuals who are distorted by environmental influences and introduces nine levels ofsystems with ascending sophistication:

1. Framework level: static structure.

2. Clockwork level: simple dynamics – predetermined and necessary motions.

3. Thermostat level: control mechanism or cybernetics.

4. Cell level: open system or self-containing structure.

5. Plant level: genetic-societal division of labor, such as different cells.

6. Animal level: specialized sense organs and increased self-awareness, knowledge andmobility.

7. Human level: interpretation of symbols, awareness of history and self-consciousness.

8. Social organization level: roles and interrelations of individuals.

9. Transcendental level: ultimate, absolute, and inescapable unknowables.

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Without going into detail of each level it is important to determine today’s knowledge ofthe different levels of systems. While the first two levels are quite well-known and studied,level three and four are by far not fully developed. From level four, “life begins to differ-entiate itself from not-life” (Boulding, 1956, p. 203). He states, that anything beyond thislevel is still almost complete mystery to system theory and says, that only God can make atree. As of today, advances such as in biology and medicine are by far more sophisticatedbut concerning the entirety of systems at level five or higher, behavior still remains in themist of mankind’s knowledge.

Getting back to all levels, the behavioral aspect within a system may origin from eitherconscious decisions or from intrinsic, unconscious or random changes of direction made byelements within the system. Responsible for those conscious decisions in dynamic systemsare mainly people, for example to increase profit in economics, to gain reputation in sociallife or to maintain a political system.

Not all of these behaviors aim at changing the structure of the system but what these con-scious decisions have in common is, that they require feedback from the system. So as todecide in favor or against certain measures, firstly, the system has to react and give feed-back to the element or person performing the action, and secondly, knowledge of bygoneaction and resulting feedback is needed to heighten predictability of outcomes. Other ex-amples making use of feedback are, for instance, physical or biological systems such asin water regulation, heating or growth of bacteria. Admittedly, in these cases, the termconscious is not applicable but cause and effect are correlated and actions are predictabledepending on the feedback of the system or the environment, even though feedback mightbe biased by an open window, in the example of the heating. On the other hand, intrin-sic, unconscious or random decisions can sometimes be influenced by feedback but aremostly not. One example with feedback is evolution theory, where the environment givesfeedback to individual systems and their elements (species) of what (new) feature is animprovement and increasing the possibility to survive. Here, missing immediacy makes itimpossible for elements (species) to adapt their behavior to feedback.

Even if immediacy of feedback is given, missing knowledge and intelligence would leavepredictability of decisions in an uncertain state. And what is more, even if immediacy,knowledge and intelligence are available, other circumstances like narrowed choice, biasor feelings would often result in non-desirable decisions or behavior. But despite the miss-ing immediacy of feedback in biological evolution, behavior is still far from random andelements (species) often act according to intrinsic knowledge or copy behavior uncon-sciously.

Compared with these feedback systems, sometimes also referred to as closed system, mostsystems do not have feedback. Those systems, also referred to as open systems, cannotbe governed as the past is not known and possible future outcomes of behavior uncertain.Forrester (1969, p. 5) mentions the examples of an automobile, not knowing where it isgoing or a watch, not observing its own inaccuracy at their basic configuration. These sys-tems are not self controlled but can nowadays often be equipped with either automatedfeedback such as cruise control for cars or radio control for watches or by introducing aperson into the system, accelerating the car or adjusting the watch manually. No matter

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2.4 System Environment and Complexity

if an action is caused by manual or automated feedback and whether the action is appro-priate, providing feedback for a system makes future measures’ outcome more predictableand thus the whole system more manageable.

But manageability often fails as size or dynamics of systems can easily be underestimatedor interrelations and dependencies may be forgotten or even not considered on purpose.For example, people want to kill insects and consequently also destroy bird populationsor want to get somewhere faster, use a car and consequently produce and breathe in moresmog (Buckley, 2009, p. 457). Each and every outcome of a cause may result in anothereffect and causal chains can be followed up further and further. Not only to ecology thefirst law “everything is connected to everything else” (Commoner, 1996, p. 177) applies butthese causal chains might not always lead somewhere. Going a bit further and stating thenot so obvious, Hardin’s law says that “you can never do merely one thing” (Hardin, 1963,p. 80) and that every action has side-effects. Real examples of underestimated dynamicsof systems prove, that Hardin’s law is not always considered thoroughly enough whenaiming at one goal and acting shortsightedly.

In 1935, when sugar cane plantations in Queensland, Australia had poor crop due to thedistribution of cane beetles, humans brought the cane toad from Hawaii to the sugar caneplantations to get rid of the grey backed cane beetle. Here, the end justified the mean,as apart from the disregarded fact, that cane toads are not good climbers and the beetlesstayed at the top of sugar canes, people were furthermore not aware, that they introducedone of the world’s worst invasive and poisonous species (Urban, Phillips, Skelly, & Shine,2008). Cane toads have barely any natural enemies and those who try to feed on themdie from the toads’ poisonous skin. Ultimately, the toads reproduced and spread uncon-trolledly over the country and killed native animals such as bees, snakes or crocodiles aswell as domestic animals – and they still do. The effects to Australia’s flora and faunacaused by the introduction of the cane toad can hardly be measured but the reason for fail-ure is not having considered the system’s dynamics, or the behavioral aspects over time.

Other ecological examples include the shrinking of and destruction of fauna in the Aral Seadue to Soviet irrigation projects causing salinization since the 1960s or loss of biodiversityand extinction of species due to large-scale monocultural farming (Altieri, 1999; Micklin,2007). In many other disciplines other than ecology these non-considerations of dynamicslead to unintended results and side-effects.


Considering the definition of a system, one major part is its boundary and the relation toits environment. As a consequence, observing the environment and especially the devel-opment over time is crucial to fill blanks of understanding dynamic systems and decisionmaking within these systems. As previously mentioned, this thesis is not about describingthe evolution of a system such as the AL or the EA itself, but a constantly changing envi-ronment and increased complexity also effects the behavior of elements in the system.

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Adding another definition of the term system but with a focus on complexity, Costanza etal. (1993, p. 545) see systems as

groups of interacting, interdependent parts linked together by exchanges ofenergy, matter, and information. Complex systems are characterized by strong(usually non-linear) interactions between the parts, complex feedback loopsthat make it difficult to distinguish cause from effect, and significant time andspace lags, discontinuities, thresholds, and limits.

While parts of the internal complexity of a system such as cause and effect, feedback loops,and time lags or delays have already been outlined previously, the reasons for the com-plexity and adaptation to the environment has not been discussed yet. This is impor-tant as complexity and constant change in a system is related to the behavior of elementsin the system. One approach to generalize and to provide a framework for the descrip-tion of complex systems is published by Beer (1984). He describes such systems throughfive subsystems – so to say a system of systems – which is known as the Viable SystemModel (VSM). Three systems are used to manage everything inside the boundary of thedescribed system and two systems are dealing with the outside, or the environment. In-side the system, operation, coordination, control focus on the present situation whereas out-side the boundary, planning and identity aim at future developments. Each system is atleast connected to two other systems as depicted in Figure 2.5.

Figure 2.5: Viable System Model,Source: adapted from Beer (1984)

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In order to adapt to the constantly changing environment, the five systems have to be fullyfunctioning as well as their interconnections. A more detailed description of the systems isoutlined subsequently in the context of EAM as published by Buckl, Matthes, and Schweda(2009), where a company’s EA constitutes the observed system.

System one: operation The first system dealing with the inside of the EA is taking careof the operational issues such as realization of IT projects and thus changing thestructure of the architecture. In general, system one is performing key transformationprocesses in an organization.

System two: coordination Every information that is needed to dovetail the activities ofsystem one is incorporated in system two. In the context of EAs, the harmonizingof projects and communication between all stakeholders from business and IT areessential for coordination purposes and often underestimated. A lack of communi-cation and coordination is one major factor of influence increasing the gap betweenthe actual behavior of a system and the intended behavior only considering the struc-ture.

System three: control Setting up rules, rights and responsibilities of system one, sys-tem three conducts monitoring and control functions of the organization. Systemthree and system one are directly connected as they are closely related and givingfeedback, ideally without intermediaries. System three keeps track of the processeswithin system one and is able to steer these processes through, for example, obser-vation of KPIs in the context of EAM as depicted in Figure 1.3.

System four: planning Monitoring the outside of the organization is achieved by systemfour. As shown in Figure 2.5, the planning system has a focus on the environmentsurrounding the company and its development. Often, this system is taken by de-partments such as business development, research and marketing with the task toforesee changes and threats in and adapt to the environment.

System five: identity Responsible for governance or policy decisions, system five is incharge of steering the organization and for the whole system to stay viable. It’s align-ing the organization to the values and policies set through balancing demands ofevery other part of the system as well as demands arising from the environment. Inorder to achieve this balance, system five is the only system that is directly connectedto and in this way acquiring feedback of every other subsystem.

Related to EAs, the first three systems depict the operative management tasks and are incharge of running the company, whereas systems four and five mainly attend to strate-gic EAM and are in charge of changing the enterprise (Buckl, Matthes, & Schweda, 2009,p. 3).

Especially management of systems four and five lack generalizability and additionally,feedback from other (sub-) systems can be delayed or biased. While delays can still bemanaged through reduction or consideration, biased feedback is mostly not taken intoaccount as it may result in a very complex structure where trying to understand or evenmeasure a bias often seems not worth the time. Among these biases is, what this thesis is

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concerned with first and foremost, the behavioral aspect of a system and its subsystemswhich results in the black box of Figure 1.3.

One key concept of such a viable system is that it lives and aims at surviving within itscontext or environment comprised of other viable systems. Additionally, a viable system isrequired to adapt to its environment by changing its structure accordingly to the demandsof an evolving context. If it manages to do so and is able to interpret progress correctlyand in time, the organization can reach or maintain a stable state. Therefore, learning ofpast developments and applying it to new situations is important to actively adapt to theenvironment or sometimes even actively influence or change the environment (Golinelli,Pastore, Gatti, Massaroni, & Vagnani, 2011).

An explanation with visualizations for adaptation to the environment and its complexityis given by Kandjani et al. (2013). Their co-evolution path model provides an explanation ofwhy and how an enterprise has to change its structure and behavior in order to survivein a constantly increasing competitive market. Reasons for the increased competition liein globalization, global players as well as highly specialized enterprises and increaseddemands by customers. This trend exacts companies to shorten times to market, specializein niche markets and reduce costs. But, according to the authors, enterprises are complexadaptive living systems rather than designed systems which calls for a deeper understandingof the system’s cybernetics and the development over time rather than a one-off conceptionof an enterprise’s static structure. Applied measure to explain a system’s viability is thedisparity between the system’s and the environment’s complexity as shown in Figure 2.6.Or, exemplary and in other words: whether specialized demands by customers can befulfilled by the flexibility of the enterprise. In general, complexity may have multifariousdimensions such as a complex structure, a complex behavior, complex control, complexmanagement or any other relevant view of the system (Kandjani et al., 2013, p. 3861). Theyfurthermore point out, that

in order for a system to dynamically achieve and maintain requisite variety andto be in dynamic equilibrium, the system requires communication channelsand feedback loops.

These include, among others, attenuation and amplification mechanisms, which have beenpreviously discussed as balancing and reinforcing feedback loops. Reinforcing feedbackmay lead to instability of the system or heterostasis and consequently the organization isnot able to survive. Only homeostasis through a balancing or negative feedback loop leavecritical variables in a stable state and enables the enterprise to co-evolve with its environ-ment and to survive competition unscathed. Applying this to the previously introducedsystem archetype of balanced feedback in Figure 3.3, the desired state of the organization’scomplexity is the complexity of the environment. When both, the CE and the CS, are equal,the system is in static equilibrium but has to sense when changes in the environment hap-pen which ask for increasing or decreasing the own complexity. If the system holds morecomplexity than its context does, some behaviors can not be differentiated by the environ-ment and as such results in unnecessary costs. In this case, the CS can either be reduced(e.g. by elimination of variety) or the CE can be augmented (e.g. by penetrating the mar-ket with new goods or services). If the CE is higher than the CS, the context in which the

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2.5 Complex Adaptive Systems

Figure 2.6: The Co-Evolution Path ModelSource: Kandjani et al. (2013)

enterprise acts is able to produce more states which the enterprise cannot keep up with.Here, the system has to attenuate the effects and try to co-evolve with the environment. Tosum up, a stable state, equilibrium or homeostasis can only be achieved through constantchange processes and adaptation to the conditions given by the environment. One ap-proach to this adaptation are Complex Adaptive Systems (CAS), which are subsequentlyoutlined.


Introduced by Holland (1992), his theories of CAS have been applied in several fields ofstudies, showing the applicability and abstractness of his model and the underlying the-ories. Also because by that time, he was a professor of psychology, electrical engineeringand computer science shows, that his findings are not solely intended for one specific re-search, such as explaining ecological phenomena, but for various purposes. CAS compriseeverything, what has previously been outlined: system theory, complexity of a system,complexity of the environment, evolution of the environment, dynamics of and between asystem or a SoS as well as control theory or cybernetics. When thinking of EAs or ALs, onewould usually not think of these systems as dynamic and adaptive systems but rather asa static structure serving to fulfill specific purposes in a finite time frame. Holland (1992)also makes a distinction between between man-made systems and natural systems as itis described in Chapter 2.2 as the source. Adding to the examples of man-made systems

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mentioned in this thesis, such as mechanical systems or social systems, he mentions air-planes and bridges and as natural systems he exemplary lists economies, ecology, immunesystems and the brain – in addition to the weather or organic systems itemized here. Buthe points out, the man-made systems are easier to manage, as equation-based methods,for example when designing an airplane wing, are more precise than trying to predict anoutcome when applying a measure affecting a whole economy. But before pointing out,why theories of CAS are useful for ALs and the goal of this thesis, there is a need to clarifyfurther details of CAS and their behavior.

CAS are described as those systems involving an evolving structure which is not based onspontaneous change but where changes happen in order to adapt to an evolving environ-ment. Rarely, a whole system changes in its entirety. Mostly, a single component or a groupof component parts adapt to their surrounding, which include both, the system’s environ-ment as well as other components within the system. This evolving structure makes thosesystem, what Holland (1992) describes as moving target. What all these previously men-tioned systems have in common is a structure that has to evolve in a long-term process inorder to satisfy this moving target.

Three characteristics that constitute CAS are mentioned by Holland (1992): evolution, ag-gregate behavior, and anticipation. Firstly, evolution of a system can be seen as the ability toimprove the system or parts of it by learning. Secondly, aggregate behavior is what waspreviously mentioned as parts of a system, that gathered together “form a whole greaterthan the sum of the parts” (Boardman & Sauser, 2006, p. 118). The interactions of the partsform an aggregate behavior, which is especially interesting to grasp in order to be able tomodify the overall behavior of, in this case, things like overall IT effectiveness. The thirdand least understood characteristic of CAS is anticipation. If a part of a system or a sub-system is able to generate rules by collecting feedback, it is able to anticipate reactions toactions.

But anticipation, learning and the development of rules in CAS are difficult, because of alack of generalizability due to the fact, that there is no final endpoint to reach but a constantattempt to follow the moving target. Thus, developed rules have to be changed perma-nently in order to provide for adaptation and to guarantee the aforementioned evolvingstructure. For the purpose of doing so, two procedures have to be provided to the system:rule discovery and credit assignment.

So as to deal with new situations and adapting to a moving target, it is important for asystem to find strong rules and avoid nonsense rules. According to Holland (1992), suchstrong rules can be best found by combination or crossing of above-average rules, immedi-ately suggesting the use of the picture of genetic algorithms, where descendants of above-average parents are more likely to survive (Darwin, 1859), whereas nonsense or weak ruleswill not become prevalent in the long term but will be sorted out. The second procedureof credit assignment is needed to reward good rules or good performances which makethe system get better and evolve into the direction of the moving target. If a rule performsabove-average it has to given more weight for future decisions. So even if the there is nofinal endpoint or equilibrium to reach, rules that have performed well in the past are morelikely to do so in the future, too (Holland, 1992).

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While the idea of rule discovery and credit assignment remains, more than a decade later,Holland (2006) lists four major features of CAS: parallelism, conditional action, modularity,and adaptation and evolution.

• Parallelism is always a part of these systems, as its parts, its subsystems, or whatthe author also names agents are acting simultaneously and every agent is busy im-proving his own situation and only partly being concerned about other agents in thesystem.

• Conditional action is needed in order to maintain or create new rules and thus actingaccordingly whenever an agents receives a signal or feedback from its environmentor other agents.

• Modularity saves the agent from the need to create new rules for every possible situ-ation in the future. Instead of doing so, rules are combined to building blocks and ifthey have been useful in the past, they are most likely useful in novel situations.

• Adaptation and evolution is essential to survive the competition in a perpetually novelenvironment. Adaptation and evolution remain the same challenge as in the originalwork of the author: that is to say, the rule discovery problem and the credit assign-ment problem, which have to be solved for a further development of the agents andeventually the whole system.

His high-degree abstraction model has been used to describe miscellaneous problems aspredicting changes in global trade, preserving ecosystems or encouraging innovation indynamic economies (Holland, 2006). Local optima in these systems are temporarily possi-ble but as improvements are always feasible due to an ever changing environment, a globaloptimum or equilibrium is not achievable (Holland & Miller, 1991, p. 365). Works usingCAS to characterize systems and to derive conclusions range from ecosystems and thebiosphere (Levin, 1998), competition in supply chains (Langdon & Sikora, 2006), supplynetworks (Choi, Dooley, & Rungtusanatham, 2001), a local heroin market (Hoffer, Boba-shev, & Morris, 2009) to EAs in electronic governments (Janssen & Kuk, 2006). They makeuse of the model of CAS in order to explain a system’s behavior over time in the context ofenvironmental evolution and

to determine the degree to which system features are determined by environ-mental conditions, and the degree to which they are the result of self-organization.(Levin, 1998, p. 1)

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3 Approaches to Model Behavior in Systemsand Applicability to ALs

Following the introductory part with the motivational aspects and theories and modelssupporting the development of a model in the context of an evolving environment, thischapter comprises the actual description of available models as well as necessary compo-nents to increase the understanding of the behavioral and dynamics aspect of ALs. Butbefore going into detail about dynamics, the definition of the system and its boundarieshas to be developed first to clarify the scope of this thesis. In defiance of the high degree ofabstractness, the targeted general application of the model and the universality of appliedsystem theory, the scope and the borders of the modeled system and what is essential todevelop further insights have to be set as accurately as possible to heed the conditions ofALs with their purpose and technical orientation and not losing the focus by drifting toofar into psychological aspects and trying to explain human behavior in all its psycholog-ical, logical or sociological facets. This would admittedly be hubris as, with the words ofBoulding (1956, p. 206), “it may be doubted whether we have as yet even the rudimentsof theoretical systems” to explain these facets. Thus, there are clearly no endeavors to gobeyond Boulding’s level four – the open system or self-containing structure at the cell level(cf. Section 2.3) – but actually mainly taking care of an even more narrowed down scopeby concentrating on level two – the simple dynamic aspect and keeping equilibrium. Todefine the scope and borders of the observed AL in its organizational environment, fol-lowing aspects of the system are described in more detail: elements within the system,environment, boundary, viewpoint and behavior. But first, a short recapitulation of themotivational aspect is necessary to call up the purpose of the model.

3.1 Distinguishing from Existing Structural Models

In order to set the scope of the model, the problem setting and the purpose are importantto determine the system representation – as the “best model is the simplest one that fulfillsits specific purpose” (Bossel, 1994, p. 36). Focusing on mainly structural aspects of ALs,which current EAM is doing, is not sufficient to understand the outcome when changesare being made in the AL or if the environment changes in which it is situated. One reasonfor not considering the behavioral aspect of the system is that it may seem theoretical andabstract. Additionally, the daily routine of making decisions in order to align business andIT takes place in a setting, where time is money and the time to market of products andservices is a CSF and consideration of influencing factors, such as dynamics aspect, wherethere will always remain some degree of uncertainty, seems not worth the effort. This

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3 Approaches to Model Behavior in Systems and Applicability to ALs

uncertainty comes naturally as behavior is considerably more subject to variation than thestructure, but it is not the purpose of this thesis to eradicate the uncertainty by modelingevery single aspect. Doing so would incorporate modeling every possible state of anysubsystem at any moment in time which would also be opposed to a model’s reductioncharacteristic. In contrast, it is the purpose of the model to allocate a consistent descriptionof ALs with all their dynamics aspects. This gives the opportunity to model and talk aboutthe system’s behavior and, by applying general system theory, which is used in other fieldsof studies as well, parallels can be drawn.

Models can be used to design, to describe, to explain, and to simulate and here, the focuslies on the explanation of behavior. There already exist (meta-) models for designing thestructure of ALs or for descriptive purposes. But there is a need to satisfy all levels, notonly on the structure but on the behavior of such a system.

Following examples describe approaches, which provide surroundings to model behav-ioral aspects in a more general sense with no declared relation to EAs. Firstly, Causal LoopDiagrams (CLDs) are introduced in Section 3.2 to explain qualitative feedback, loops, andsystem archetypes as well as a short reference to a quantitative approach – Stock and FlowDiagrams (SFDs). Secondly, one example of behavioral UML diagrams is outlined in Sec-tion 3.3, which are intended to explain decisions and sequences of actions under certainconditions: activity diagrams. Thirdly, an own approach is presented in Section 3.4, bystarting to compile necessary elements of a new model on dynamics in ALs. Eventually, asummary of models describing inside dynamics is given in Section 3.5 with a transition tothe environmental influences.

3.2 Modeling Behavior in Systems: Feedback, Loops, and SystemArchetypes

Dynamics in systems have been described by models already, although they have not beenapplied in big scale for ALs. In this section, feedback, loops, and CLDs depicting systemarchetypes are introduced, which may serve to explain and determine the reason for asystem’s behavior. Compared with approaches like SFDs, which can be applied to explainquantitative phenomena, the hereby introduced means are to be seen against the backdropof qualitative explanations. Underlying theory is largely coined by the works of Senge(1990) and Sterman (2000).

As mentioned above, one vital distinction of systems is, whether they receive feedback ornot. This reaction to an action can either be indifferent, reinforcing or balancing. Reinforc-ing, or positive feedback amplifies a trend triggered by the underlying action whereas abalancing, or negative feedback is ideally closing gaps between current states and targetstates. On condition that a system is capable of adjusting its actions to incoming feedbackand ideally knowing its past state and a desired state, these actions and reactions result inloops of either an increase to infinite or a decrease to zero in the reinforcing case or getting

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3.2 Modeling Behavior in Systems: Feedback, Loops, and System Archetypes

to a target state in the balancing case. Without consideration of the usefulness or applica-tion of feedback loops and apart from theory and laboratory experiments, there are alwaysother circumstances to contemplate which bias these feedback loops.

Firstly, delays may cause under- or overestimation of means or inappropriate frequency ofactions, especially if the connection between action and reaction is not known in more de-tail. Or, in other words: “cause and effect are not closely related in time and space” (Senge,1990, p. 63). Secondly, and already mentioned above, every action in practice results inmore than one effect or reaction due to Hardin’s law, describing, that it’s not possible todo merely one thing. Figured as a model, this would represent every action as an elementwith presupposed injectivity. Thirdly, as systems in practice are never closed, other ele-ments within the system as well as environmental changes influence the state or behaviorof an element. Thus, every reaction is not caused by only one action but biased by otherinterrelations. In a model, a reaction could be depicted as an element with presupposedsurjectivity.

Figure 3.1: From an Ideal to a Biased Feedback

Contrasting comparison is depicted in Figure 3.1, where the ideal feedback is shown onthe left side whereas the biased feedback on the right side incorporates all three previ-ously mentioned circumstances, exemplary depicted with one delay, two resulting side-effects and two additional elements influencing the targeted state. Combining these threedifferent distortion factors with positive and negative feedback loops results in behavioralpatterns of a system – so called system archetypes. As introduced by Senge (1990), sys-tem archetypes are patterns explaining the irregularities in non-ideal feedback cycles andare usually depicted as CLD. There exist more than the subsequently depicted systemarchetypes, but not all of these patterns are usable in the context of ALs.

Reinforcing Loop with Delay This basic archetype, depicted in Figure 3.2, has a positivereinforcing loop where the increase of state 1 leads to an increase of state 2 and viceversa or a decrease of state 1 leads to a decrease of state 2. Through its nature ofreinforcement, these cycles often lead to exponential growth or decline. Reinforcedloops, also occurring in other archetypes are denoted by a black cyclic arrow witha plus in the middle. Delays can be part of any cycle but are not introduced herebecause they do not affect reinforcing loops’ behavior crucially. Examples of rein-forcing feedback are avalanches or distribution of viruses.

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Figure 3.2: System Archetype: Reinforcing Feedback with Delay

Balancing Loop Precondition of a balancing loop as shown in Figure 3.3 is the existenceof a desired state. Corrective action is undertaken to effect the current state in orderto reduce or eliminate the gap between the desired and the current state. In this case,delays of responses can produce undesirable results if they are not known to the ele-ment taking corrective action. Balancing loops, also occurring in other archetypes aredenoted by a black cyclic arrow with a minus in the middle. Examples of balancingloops are staying upright while riding a bicycle or keeping a temperature in a roomwith a heating.

Figure 3.3: System Archetype: Balancing Loop

Drifting Goals Also known as eroding goals and illustrated in Figure 3.4, describes thecomposition of two aforementioned balancing loops undermining each other. Again,a desired state and a current state hold a gap with the original goal to achieve the de-sired state. While in the one balancing loop, corrective action is undertaken to reducethe gap, the second loop at the same time describes the pressure to adjust the desiredstate. Adjusting or reducing the desired state, and thus closing the gap, consequentlyreduces the need of corrective action and finally results in an equilibrium with a re-duced, drifted or eroded goal other than the originally desired state. Drifting goalsoften occur, when problems need to be solved within a short time-frame. Examplesincorporate sliding limits of environmental pollution or reducing personal goals toincrease the possibility and frequency of personal achievement.

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3.2 Modeling Behavior in Systems: Feedback, Loops, and System Archetypes

Figure 3.4: System Archetype: Drifting Goals

Shifting the Burden As the aforementioned archetype of drifting goals, this structurealso includes two balancing cycles and is quite well-known in operational IT or inprojects. A problem symptom exists, which could be eliminated through a funda-mental solution and the cycle thus be brought in an equilibrium. But as the fun-damental solution cycle is equipped with a delay and higher investments, a symp-tomatic solution is preferred, which reduces the problem symptom quicker but doesnot eliminate the problem entirely. Additionally, symptomatic solutions produceside effects, for example in dependent elements, which decreases the perceived needof implementing a fundamental solution. These side effects change the structure oftwo balancing cycles to one reinforcing loop. Examples of the shifting the burdenstructure are drug addiction (using drugs to cope with a problem instead of dissolv-ing it) or waste problems (building more landfills instead of introducing a funda-mental recycling concept).

Figure 3.5: System Archetype: Shifting the Burden

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Escalation Similar to the archetype of shifting the burden, this structure also consists oftwo balancing loops but result in one reinforcing loop when reacting to each other.When the results of A compared to B increase, B is more likely to do more correc-tive action, which increases his results and consequently decreases the results of Arelative to B. In turn, A undertakes more corrective action and increases his resultscompared to B, the latter then increasing his corrective action. Escalation occurswhen every element presumes that there can only be one winner and escalation canonly be held back through cooperation between A and B. Examples of this structureare arms race of different countries or decreasing prices between low-cost suppliers.

Figure 3.6: System Archetype: Escalation

Fixes that Fail An originally balancing loop, that is intended to fix a problem or to close agap between a desired and a current state becomes a reinforcement cycle when fixesproduce unintended consequences. Often, a delay makes unintended consequencesnot visible at first and lets the fix appear a suitable measure to solve a problem.But when unintended consequences occur, the problem is increased or other prob-lems are generated. Examples are saving costs on maintenance to save money or theaforementioned example of introducing toads to kill bugs.

Figure 3.7: System Archetype: Fixes that Fail

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3.3 Modeling Behavior in Systems: Activity Diagrams

Tragedy of the Commons This structure as depicted in 3.8 is based on a limited resource,which is used and shared by at least two elements. It is best described by an exampleof Hardin (2009), where the limited resource is a pasture open to herdsman A andB. To maximize their individual gains, every herdsman adds animals, as every newanimal contributes positively to his sales figures. The negative effect of overgrazingthe commonly used pasture is only a fractal (here: one half) of the positive effect, asit has to be accounted for by all herdsmen. Especially the delay in the negative effectof the total activity results in a selfish behavior of each herdsman and can resultin non-usability of the entire pasture because of overgrazing. Another example isexploitation of fish stocks through overfishing. The structure of the tragedy of thecommons can be avoided through communication and regulation.

Figure 3.8: System Archetype: Tragedy of the Commons

For completion of the list of possibly applicable system archetypes, the following patternsare mentioned but not explained or depicted in detail as they are only of limited use forthis thesis: accidental adversaries, limits to success, success to the successful, and growthand underinvestment.

3.3 Modeling Behavior in Systems: Activity Diagrams

More common models, especially in the area of IS and SE, to design and examine struc-ture and behavior are gathered under the UML and published by the Object ManagementGroup (OMG). With the current version being 2.4.1 – which was published on August 6th,2011 – several types of diagrams are described to be used as standardized visualizationsto depict an original or planned system. Naturally, for this thesis, the structural modelsare not of interest but the ones focusing on behavior. There are seven different diagrams,that claim to model behavior: use case diagram, timing diagram, state machine diagram,sequence diagram, interaction overview diagram, communication diagram, and activity

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diagram. Here, the latter is further examined as it is the closest to what we perceive asapplicable to model behavior of ALs. What the OMG considers as behavior is depicted inthe schema in Figure 3.9.

Figure 3.9: Structure, Behavior, and Dependencies in UML Semantics,Source: Object Management Group (2011, p. 11)

These three layers are used in the UML to describe the hierarchy and dependencies ofstructure and behavior in their semantics model. The bottom layer depicts the structuralfoundation. “This reflects the premise that there is no disembodied behavior in UML –all behavior is the consequence of the actions of structural entities” (Object ManagementGroup, 2011, p. 11). The behavioral semantic base is depicted as gray box and includesinter-object behavior and intra-object behavior which result in actions. These actions areperceived as “the fundamental units of behavior in UML”. The higher-level formalisms –activities, state machines, and interactions – contain these actions. As mentioned before,activities are in the focal point here. An example of a UML activity diagram is pictured inFigure 3.10 – adapted from the specification by the OMG itself.

Figure 3.10: Exemplary UML Activity Diagram with Interruptible Activity Region,Source: Object Management Group (2011, p. 393)

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3.4 Components to Model Behavior: Inside and Outside the System

When perceiving behavior as the OMG does, these activity diagrams are applicable to de-scribe or design the activities. While the example in hand could easily be applied to amore AL-specific issue with a process from Project Management (PM) or software devel-opment, this small and general example suffices to describe the advantages and disad-vantages of the approach, which is eventually done in Section 3.5 together with the otherapproaches.

Starting from the initial node on the left, the first action is triggered, which is the receiptof an order. Following decision node determines whether the subsequent action is to closethe order and finish the activity in case of order rejection or whether the order is filledin case of order acceptance. Deciding upon acceptability of an order can be perceived asbehavior. If the order is filled, it is consequently shipped while an invoice is sent. Only ifpayment has succeeded and is accepted, the order is closed and the activity finalized. Onespecial feature of this activity diagram is the interruptible activity region, depicted throughthe dashed box with an accept event action, which does not contain an input arrow andmay occur at every point in time till the last activity node within the box is finished. Inthis case, when an order cancel request is raised, every activity node and behavior insidethis box is terminated. This exception of the usual activity flow is introduced to show, thatrather unusual behavior may be induced by special events, which can be modeled throughsuch an activity diagram.

Shown activity diagram only contains a fraction of possible elements, however, the mostimportant components of the current version are used. It should be mentioned, that activ-ity partitioning using multidimensional swim lanes (e.g. role horizontally and location orplant vertically) is also an important part but for reasons of clarity left out as such a parti-tioning does not alter the evaluation of the diagram and its applicability to model behaviorand dynamics.

At examination of such diagrams as introduced in UML, the question comes up, if suchactions and activities are able to explain all sorts of behavior of and within the system. Ifsuch a model is constructed in a elaborately manner it is able to explain causal chains andevents determining many behavioral aspects but open questions remain, especially whenconsidering the environmental influences and dynamics within the system and the factthat most often people play an important role within activities, where behavior is neitherpredictable, nor easy to standardize, nor controllable in its entirety.

Before evaluation of the existing approaches, which is done in Section 3.5, the followingSection 3.4 is elaborated to determine, which components of a model are crucial to explainthe dynamics of a system, from inside and from outside the system.

3.4 Components to Model Behavior: Inside and Outside theSystem

In order to determine the necessities on a model to explain the system AL and its behav-ior, the following components have to taken into consideration: elements, environment,boundary, viewpoint, subsystems and connectors. In contrast to the previously mentioned

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existing approaches such as CLDs, SFDs or diagrams of UML which pay heed to behavior,this is – in a way – the green field approach.

Elements For the moment, without consideration of the behavioral aspect but with a solefocus on structural items, the following elements are considered to be the essentialparts of the system in order to model ALs as dynamic systems. The behavior is mod-eled later on through connections between these elements such as influences anddependencies. The first and most obvious element in the system are applicationsthemselves. Applications within an organization can be divided into groups accord-ing to their origin (self-made, department-wide, in-house IT or foreign), ownership(bought, leased, Software as a Service (SaaS)), expediency or strategic relevance (crit-ical, supportive (such as office suites) or none) and their architectural layers (fromplatform software for an operating system to individual user-written software). Buteither way, no part of a division can be left out as even individual, self-made spread-sheet templates do affect the behavior of the system. This is in accordance with thedescription of the term AL as “the entirety of the business applications and their re-lationships to other elements” (Buckl, Ernst, et al., 2009a, p. 1). Looking at the ALin its entirety may result in extensive models but narrowing down applications toone specific kind of software would omit important influencing factors. Applica-tions and their relationships to each other are already a challenge while designingor understanding the structure but do not suffice an holistic approach to understandthe behavior of the system. Therefore, the second element in the model are persons,which – as discussed later in this thesis – introduce a big source of dynamics. Peoplecan also be divided into distinct groups such as their role as end-users, developers ordesigners or according to their origin, e.g. the organization’s own personnel or ex-ternal stakeholders such as customers or consultants. But to satisfy the needs of themodel being developed, no such distinction can be made or a group left out. Here,a more abstract approach has to be applied: a person is part of the system if he isin immediate connection of any kind to at least one application on a regular basis.Exemplary, people within the organization not using or depending on any softwarebelong to the environment rather than to the system as they do not affect behaviorsubstantially or not on a immediate basis. But even if they do, e.g. a cleaner pullingthe plug of a server by accident, they need to be removed from the system due to theirregularity of such events.

Environment After determination of what is part of the system, it would be easy to say,that everything but the elements in the system is the environment. Even though,“everything is connected to everything else” (Commoner, 1996, p. 177) and siftingthrough several layers of environment for a connection would always lead to a result,it is not target-aimed when looking at the specificity of ALs.

These layers of environment are, by analogy, the SoS approach and the fact, thatevery system has subsystems and that every system is part of a suprasystem. Here,two layers are examined in more detail as the environment of the company (which isin turn the environment of the AL) is also relevant to increase explanatory power.

The necessity of the importance of explaining the environment has already been out-

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lined, as the environment shapes the system and adaptation to the environment en-sures survival of the system. As this is the case for many different environments, theapproach of Bossel (1994) with six basic characteristics of an environment and orien-tation theory is used here to depict the environment in greater detail. Part of theseare the (1) normal environmental state, among which the actual environmental statemay vary up to a certain degree, (2) scarce resources, that are needed for the system’scontinuity but not available immediately or unlimited, (3) variety, such as differentsources, processes or alternative inputs. (4) Variability is the fluctuation of the nor-mal state, which can be substantial and random, (5) change is the (mostly long-termand non-return) alteration of the normal state, and (6) other systems, which are ableto change the environment significantly and in turn affect the system itself (Bossel,1994). The tangible manifestations of each characteristic are elaborated in Section 4.1and depicted in Figure 4.1.

Notwithstanding that environmental influences will later on be subsumed to guar-antee a higher degree of abstractness of the model, knowing the original factors ofenvironmental influence allow a more holistic understanding of the interaction ofthe system with its environment at the boundary.

Boundary Distinguishing what is part of the system and what is outside has been de-scribed before through the description of the inside elements and the outside envi-ronment. But it is not sufficient to assign structural membership or non-membershipas the behavior at the boundary or the interaction between the system and its envi-ronment has to be considered in this context as well. By behavior at the boundary,one could also speak of the environment’s influence on the system and vice versa.There are in fact only two kinds of factors influencing the system’s behavior: firstly,aforementioned exogenous influence of the environment on the system and secondly,the endogenous factor, comprised of e.g. internal feedback loops, often also termedeigendynamics (Bossel, 1994, p. 28).

Looking closely at the system itself, it is not feasible to determine, whether the ex-ogenous or the endogenous influencing factors have a bigger impact in general. Butwhen looking closely at the environment, one can say, that exogenous influence onthe system is bigger than the effects of eigendynamics on the environment.

Preceding outline of the environment, in which an AL operates, could be used toexplain the exogenous factors of influence by examining each of the six characteris-tics of an environment, as depicted in Figure 4.1. But in order to satisfy the reduc-tion characteristic of models and to meet the requirements of generality of systemstheory, a higher degree of abstractness is necessary. As such, the basic orientors asintroduced in Chapter 4.2 can help to condense the multiplicity of environmental in-fluencing factors with direct reference to the six characteristics and ending with anoverall orientor satisfaction. As these orientors already bear upon behavior, e.g. howto increase stability in the face of external variability, they are not yet introduced forthe structural description at the boundary. Of course, action or reaction is mostlydirected towards a specific circumstance or event, but for this model, even less pre-cision is necessary to describe the interaction at the boundary.

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Viewpoint Different viewpoints can result in many different models to answer the pur-pose intended. To clarify and narrow down the uses of this model, it has to be men-tioned here, that the focus of this thesis and thus the viewpoint of the model lieson the behavior of the system AL and not on its structure, interfaces, geographicaldistribution, etc. Not to get confused with existing architectural frameworks: by thewording viewpoint, no reference to any other framework using the same notion suchas the architectural description of IEEE and ISO/IEC (2007).

More precisely, this model is about the dynamics of as well as within the system.Studying the system as a black box, describing the behavior from an outsider’s viewand how the system behaves in its environment (organization, competition) in orderto survive is discussed as the first viewpoint in Section 4.2. However, another impor-tant focus here lies on the insider’s view, or treating the system as a white box, fromthe boundary inwards – including the boundary itself and the interaction at it. Afterelaboration of the environment and the black box view, breaking down the AL intoits subsystems with the help of the SoS approach is done in Chapter 5.

Subsystems Behavior is composed of observable actions of and interactions between el-ements to serve the mediate system purpose. As previously mentioned, one vitalsystem characteristic is, that every system pursues a specific purpose – intentionallyor not, with the help of its subsystems. Self-evidently, every subsystem has its ownpurpose – at the minimum self-preservation. Knowledge about the purpose of a sys-tem can usually be used to explain its behavior. One essential reason for dynamics isthe interaction and behavior of subsystems within the model as, when consideringthe connectedness and variety of the subsystems, the whole is more than the sum ofits components. In order to understand this dynamics aspect, the subsystems of theAL have to be studied.

To distinguish between different subsystem and to be able to draw clear borders,business processes are used to build subsystems serving the purpose of fulfillingsuch a business process as its (sub-) system goal. Understanding the behavior ofthe system AL is not entirely possible when concentrating on only few business pro-cesses. Thus, no clear distinction can be made separating, for example, strategicallyimportant from supportive or automated from non-automated processes. Any pro-cess, having at least one connection to an previously mentioned, relevant applicationor at least one connection to a person depending on or interacting with such an ap-plication is relevant to explain behavioral aspects of the system – even if they do notanswer the purpose of supporting business goals immediately. Each of these rele-vant processes depicts a subsystem whereas all the other processes can be shifted tothe environment of the system.

Each subsystem then consists of aforementioned elements, at least one person andone application but usually of many persons and several applications, which to-gether serve the system purpose business process. In order to achieve the goal, everyelement within the subsystem is connected to at least one other element in the sub-system by at least one subsequently outlined connector. No self-sufficient elementcan be found in a subsystem. For the sake of completeness, elements of subsystems

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can again be subsystems, but in this thesis one level of subsystems in the AL is suffi-cient and ensures reduction characteristics of the model.

Connectors Within every (sub-) system, comprising elements are interrelated, intercon-nected or interdependent in various ways. Elements in every subsystem are, as pre-viously mentioned, persons, applications, and other subsystems. When looking atactual instances of ALs and the people involved, there is a multitude of connectionsthat can be found between the different manifestations of elements. Examples are isresponsible for, develops, waits for, authorizes, uses, and includes but in order to fulfill thereduction characteristics and gain insights from abstraction, these possible links arereduced to two connectors: depends on and influences.

Depends on means, that the dependent element does not function properly or doesnot function at all if the other element is not reliable, not working or not existing.If, as a consequence, the dependent element does not function properly, the systempurpose (or business process) cannot be fulfilled. Mutual dependence is possible, asintroduced later.

Influences means, that an element is able to alter system variables or states of theinfluenced element. Here, it is not important whether the influence is exercised in-tentionally or not or whether it has positive or negative intention or impact. Mutualinfluence is possible, which is also explained in the following.

Every other connection which is possible between, persons, applications, and sub-systems in the AL can and has to be assigned to one of these two connectors. Asan example, a mapping of imaginable relations or connections to the two connectorsdependence and influence is shown in Figure 3.11.

Figure 3.11: Reducing a Multitude of Interrelations to Two Links Connecting Elements

Presented edges linking two elements are directed as a differentiation is necessaryto explain behavior more specifically, whether an element is dependent on some-

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thing or if has influence on another element. Therefore, it would not be sufficientto use undirected connectors, just to state that there is any relation among two ele-ments. Following five situations may occur when assigning the two connectors to arelationship:

1. Regular: The most common assignment. If A coordinates B, then A influences B.

2. Inversion: A change in the direction of the edge has to be undertaken. If Aexcludes B, then B depends on A.

3. Double unidirectional: When the depiction of a relationship requires both con-nectors. If A uses B, then A depends on B and B influences A.

4. Single bidirectional: When more than one relationship can be found betweentwo elements and there is at least one inversion. If A authorizes B and B supportsA, then A and B both depend on each other.

5. Double bidirectional: When more than one relationship can be found and bothconnectors are needed. If A uses and maintains B, then A and B both depend oneach other and influence each other.

Especially bidirectional edges and formation of circles in larger settings are strongindicators for system archetypes as introduced in Section 3.2.

3.5 Seeing the Bigger Picture: Leaving Inside Behavior to Enterthe Environment

Undoubtedly, existing approaches as introduced in this chapter are useful to explain be-havior to a certain extent but each one lacks applicability when it comes to the employmentin a big scale for ALs.

CLDs and SFDs are useful to explain behavior, why things happen and comprise goals(such as in balancing loops) as well as non-linearity. The major downsides of these ap-proaches are the extremely high effort to develop a model, even for a relatively small scope.One example showing this effort is mentioned by Sterman (2000, pp. 55ff.). Without goinginto great detail, Ingalls, a shipbuilding company argued with its contractee, the US Navy,about what caused major delays and additional costs. As Ingalls saw the reasons in con-stant design changes which were claimed by the Navy and no traditional PM tool was ableto quantify the effects of such changes, they developed a SFD and found several positiveand negative feedback loops. Eventually, several thousands of equations were necessaryto explain the coherences. They conducted dozens of interviews, gathered a massive datacollection which resulted in a huge model. This model was used to arbitrate between thetwo parties and as the Navy criticized the model and asked to change it, it resulted ineven more support for Ingalls claim, that the Navy is responsible for the delays. This leadsto the second drawback of CLDs and SFDs: both are not only hard to develop, but alsounderstanding these models requires profound understanding and is not applicable foreach and every stakeholder. Eventually, Ingalls’ effort to develop the model was rewarded

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3.5 Seeing the Bigger Picture: Leaving Inside Behavior to Enter the Environment

as the Navy recognized that they were financially liable for the diverse overruns, but itremains a very specific example which is not applicable to any other issue equally.

Coming back to the approaches of the OMG providing diagrams to model behavior intheir UML and in particular presented activity diagram in Figure 3.10. In contrast to theprevious approach it is comprehensible but nonlinear phenomena cannot be modeled cor-rectly and a high degree of reduction leads to oblivion of many influencing factors as wellas possible effects. And even if the model contained swim lanes and if it was determined,which action is undertaken by whom, it would still lack the ability to depict all aspects ofbehavior. For example, what happens if invoice sending fails due to a server error? What ifa competitor reduces price – does the company accept more orders than before? If an orderis rejected, does that have a negative influence on future orders or on other outstandingorders? If an order is accepted, may it lead to exhaustion of employees? Does a new orderlead to the necessity of developing a new product and increase offered variety? The lackof a general direction pointing in such diagrams and the open questions that remain whenlooking at behavior do not support the use of UML diagrams to model dynamics.

Under consideration of the previously introduced green field approach, with several influ-encing factors from inside and outside the system as well as behavior at the border withdifferent viewpoints and infinite possibilities of granularity when looking at subsystems,the development of an all-embracing model explaining the dynamics of and in ALs shallbe regarded as an unpromising endeavor – at least at this juncture. But as the green fieldapproach describes a differentiation between the inside and outside of the system and, inorder to gain insights into the system’s dynamics, the following chapter starts with de-scribing the importance of the environment and its influence on behavior.

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4 Exogenous Influences Driving the System’sBehavior

An organisation cannot evolve or develop in ways which merely reflect thegoals, motives or needs of its members or of its leadership, since it must alwaysbow to the constraints imposed on it by the nature of its relationship with theenvironment. (Sadler & Barry, 1970, p. 58)

Considering preceding chapter, this statement with a focal point on the whole organiza-tion also holds true for a narrower scope – the AL. Hence, this chapter deals with externalinfluences on the system and its reaction to satisfy the requirements imposed by its envi-ronment. Section 4.1 describes a way to subdivide environmental influencing factors intosix distinguishable classes and elaborates examples in an organizational context as well asin the scope of ALs. Subsequently, Section 4.2 focuses on how to deal with these outsideinfluences by application of orientor theory. Finally, Section 4.3 refines the dependenciesand the interplay of the different orientors dealing with external effects and how actionsor reactions towards on direction may affect others.

4.1 Elaborating and Classifying Six Different EnvironmentalInfluence Factors

In accordance with the importance of the environment, this chapter is elaborating andfurther developing the influencing factors on the AL. To classify and to structure the en-vironment of the system, aforementioned model of Bossel (1994) is elaborated here andspecified through alleging examples or manifestations within each of the six basic char-acteristics: normal environmental state, scarce resources, variety, variability, change, andother systems (cf. Section 3.4).

As to satisfy the system of systems thought, examples are given for two layers of environ-ments – or two suprasystems in which ALs are situated. The first and immediate layerembracing the AL is the organization or enterprise. This organization is itself surroundedby its suprasystem, in the broad sense, the competition or market. Reasons why two lay-ers of environment are taken into consideration here do not solely serve the purpose ofdepicting a system of systems but also show a sizeable amount of interaction at the borderof the organization. Whenever the organization’s environment alters one of the six charac-teristics, it leads to conscious or unconscious alterations in the organization, which in turnaffects the AL. The organizational layer as the “in between” environment can either am-plify or mitigate the necessity for the AL to adapt or reaction may even result in feedback

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4 Exogenous Influences Driving the System’s Behavior

loops or other system archetypes. Following description of the six basic characteristics alsoexemplifies such phenomena encountered during the transition.

Examples of the outside layer are mainly adapted from Bossel (1994), as system theoryhas been applied to organizations already, whereas the mapping of examples of the innerlayer embracing the AL to the six characteristics is self-developed supported by denotedsources. The entire setup of the two environmental layers surrounding the AL is depictedin Figure 4.1.

Figure 4.1: Bossel’s Six Basic Characteristics of Environments Applied to Two LayersSurrounding ALs,

Source: partly adapted from Bossel (1994, pp. 233ff.)

4.1.1 Normal State

The normal state encompasses variables among which the actual environmental state mayvary up to a certain degree. To ensure existence and to fulfill its purpose, the system hasto function under these basic variables. Allowed or required variation of the normal stateis correlated e.g. to the size of the organization and the spread of different plants, distri-bution over several countries or on different continents. To not get confused by the termvariation in combination with normal state: the normal state may vary when looking atdifferent organizations or markets but if we only consider one organization, the normalstate is rather stable and irregular influences from outside are either classified as variety,

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4.1 Elaborating and Classifying Six Different Environmental Influence Factors

variability or change, all of which are described afterwards. Using the analogy of a math-ematical forecasting model, the normal state would correspond to the mean value.

Environment of the Organization An organization’s competitive environment holds sev-eral normal states, which it has to deal with. It comprises the language that is spoken,a distinct location and area surrounding it with its given conditions, the political sit-uation which may influence the organization, the economic background as well associal factors such as distinct attitudes of the people in this environment. As justmentioned, this normal state necessitates variation to a certain degree dependingon its size and distribution. Larger companies, which, for example, plan in Europe,produce in Asia, and distribute in America have a considerably higher degree ofvariation in languages, political environments or attitudes than small businesses.

Environment of the Application Landscape Surrounding variables of the normal stateaffecting the AL are the corporate form and policies concerning security and safety,associates and their attitudes as well as the building and the size and diversifica-tion of the organization. Regarding the variation of the normal state and previouslymentioned size of the enterprise, the same holds true for the AL.

Transition While the influence of the normal state of the inner circle on the outer circleis low, it is high during the transition from the competitive environment to the AL.The location as well as the statute and law affect the corporate form, the languagespoken has an influence on number and kind of new associates, the attitudes of thesurrounding people have a bearing on the attitudes within a company and the legalenvironment also controls security policies and the size of the company.

4.1.2 Scarce Resources

In order to survive, a system is in need of several resources coming from outside the systemand some resources are not immediately available when needed or only at high costs.Securing scarce resources is one of the main competitions between systems and may, underoutlined circumstances in Figure 3.8, lead to the tragedy of the commons in the worst-casescenario.

Environment of the Organization Looking at the organization as a system and the en-vironment holding resources also leads to such a competitive market, where onlyconsiderable effort allows organizations to satisfy its demand for electricity, water,or loans, and usually even scarcer: workers and raw materials. Here, an influenceof the normal state is apparent as it strongly affects the degree of scarcity of: waterdepending on the location in the world; raw materials depending on the product;power depending on energy-intensiveness of the industry.

Environment of the Application Landscape The organization embracing the AL providesa similar competitive environment, where actions have to be taken by different de-partments and stakeholders to secure scarce resources. In the context of ALs and

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their related IT department it is crucial to e.g. hoard knowledge through skilled per-sonnel, capture time for developments and being provided with enough budget forlicenses and projects.

Transition Again, the major impact acts inwards when looking at the availability of scarceresources. An organization which does not have enough skilled workers will nothave enough knowledge to align applications to business or business to applications.If financial means exist, projects can be advised by external people. But if the orga-nization fails to acquire sufficient loans at the bank, a shortage in budget and timeleads to lower quality and thus less business IT alignment. This in turn leads to lesscustomer satisfaction and may result in less loans – a disastrous positive reinforcingloop.

4.1.3 Variety

While the normal state varies when looking at different markets or organizations but in astable manner, the variety ideally pivots on this normal state and offers action alternativesbut also demands awareness and adaptation. Variety of the environment is generally sub-stantially bigger than inside the system and every new challenge imposed by a new varietyfrom outside must be dealt with, often by adjusting through increased variety inside thesystem – the co-evolution (Kandjani et al., 2013) in accordance with Ashby’s Law, that onlyvariety can cope with variety (Ashby, 1956, p. 207). Using the analogy of a mathematicalforecasting model, the variety would correspond to the standard deviation.

Environment of the Organization Applying the characteristic of variety to an organiza-tion’s environment, i.e. the competition layer, suggests (re-) action of the organiza-tion when confronted with (additional) variety – either by increasing its own variety,evading the challenge or postponing it. Examples for this variety are various sourcesof materials, energy or workers or different processes of transportation or produc-tion but also variation in products and services offered by suppliers, several kinds ofcompetitors and finally customers from a multitude of industries.

Environment of the Application Landscape Inside the organization, which embodies theenvironment of the AL, variety can be found at the human level. Employees havedifferent skills, personalities or abilities to communicate. Normally, in our model,people are part of the system AL, but only those having intermediate contact with theAL. Here, employees must not necessarily have any interaction with an applicationbut still could influence the AL as part of its environment, for example when gather-ing information from workers for the knowledge base or when conducting BusinessProcess Reengineering (BPR). Further sources of variety affecting the system AL arethe great number of ways and processes to achieve the same goal, flexibility concern-ing where to work (on the road, at home, at the customer’s, in other countries) andfinally the availability of different Operating Systems (OSs) and usage of a multitudeof devices in the organization and trends like Bring Your Own Device (BYOD) (Burt,2011).

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Transition The influence of variety works both ways in the characteristic of variety (cf.Section 2.4) but mostly does from the outside layer to the inside. If new kinds ofcustomers or customers from new industries or other countries need to be satisfied,the employees’ skills and the variety of processes and locations where to work has toincrease unless the organization does not want to accept the challenge. And if newsources of workers arise, for example, through an introduction of a new universitycourse, the organization needs to seize the opportunity to be able to increase employ-ees’ skills which is necessary when new competitors enter the market. On the otherhand, if applications are used to establish entirely new services or goods, IS functionas enabler (Krcmar, 2010, p. 35) and consequently elevates the environment’s varietyfrom the inside.

4.1.4 Other Systems

In system theory (cf. Section 2.2) most of the time there is the picture of the system, theboundary, and the environment. Everything outside the system is subsumed as the envi-ronment but in practice, there is the need to tier other systems according to their relevanceto the original system. Especially when observing the behavior of the system, other sys-tems, which are closely interrelated, play a major role.

Environment of the Organization Outside the organization, several other systems havemore influence than average and consequently have to be taken into closer considera-tion to be able to act or react to alterations in a timely manner. Especially customers,competitors and suppliers are important for the continuity of the organization butalso bankers, politicians or city officials may have an impact on the organization. Es-tablishing a tiering of other system is related to the normal state of the organizationas for an ice-cream vendor, the weather system is more crucial than the system stockmarket, whereas an investment banker would not constantly monitor the weatherforecast.

Environment of the Application Landscape Other systems that are important to the ALcomprise the users, servers, and administrators as well as, for example, external soft-ware suppliers. Also, any other department within the organization that may inter-act with applications is relevant and worth being observed more precisely. Especiallywhen considering the VSM and the adaptation to EAM (cf. Section 2.4), suggests thatparticularly the planning system and the identity system, which are both located inthe environment, are of vital significance for the AL. These involve departments suchas business development, research, marketing, general IT management or steeringcommittees.

Transition Influences can again be found especially from the outer layer to the inner layerwhich again emphasizes the openness of the respective subsystems. Suppliers whichare not able to supply goods or services as required demand action of IT manage-ment. Also, a lawyer (setting an injunction suit), a competitor (entering the same

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niche) or a banker (denying a loan) could ask for an amendment of marketing strat-egy or foster Research and Development (R&D), which ultimately has effects on theAL and the people working with it.

4.1.5 Change

Environmental change is basically what is described in Section 2.4 and 2.5 as the evolutionof the environment with the necessity of the subsystem to adapt to it, as CAS do. Thisevolution is usually a gradual change of the environmental state with time but may alsooccur abruptly. But in contrast to variety or variability, the original state is not going tobe reached any other time in the future. Using the analogy of a mathematical forecastingmodel, change would correspond to the trend.

Environment of the Organization Examples of long-term environmental trends organi-zations have to adapt to, include opening of new markets and higher geographicaldistribution due to globalization and a highly competitive and dynamic competitiveenvironment. Capabilities arising through the invention of new technologies can beseen as evolution, as there is barely anyone going back when having adapted a fairlynew but established technology. Another factor pushing change in the environmentof the organization are increased living conditions, inflation and higher incomes, anddemands from people.

Environment of the Application Landscape Changes in the environment of the AL are,for example, new and sustainable business developments and the deployment ofnew business models, where old ones become obsolete. When switching to a Service-oriented Architecture (SOA) in the context of IT or inventing new products and pro-cesses in general, the AL has to maintain its integrity even if it sometimes has tochange its identity. This is also the case when looking at the long-term growth ofthe organization, which by merging with and acquiring other companies issues chal-lenges to the system that has to restructure, adapt to, or integrate other systems toensure long-term viability. With this growth and possibilities enabled by globaliza-tion and free trade areas also comes the trend of a constantly increasing distributionof an organization and in order to gain time advantages, data has to be availableanywhere anytime which, among other things, favors the installation of an externalcloud provider.

Transition Influence of the outer circle on the inner circle is evident, as the organizationhas to adapt to the evolution of its environment. Opening of new markets for exam-ple brings additional customers, working places and suppliers asking for new busi-ness models and communication capabilities inside the organization. Geographicaldistribution of the market, the further development of the Internet and increasedcompetition requires the organization to offer new services, products or distributionchannels and increase efficiency, for example by outsourcing of data, decreasing theproportion of value added or buying in of non-critical IT products and services.

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4.1.6 Variability

Using the analogy of a mathematical forecasting model, variability would correspond tooutliers or extremes. But while outliers are usually dismissed in forecasting, they do playa major role for a system. Environmental variability is anything that happens unexpect-edly with high significance for the system, other subsystems and even their environmentthan the regular variety or change. These effects are uncertain and unstable and can havesevere impact on the viability of the system. Therefore, the system has to ensure securitytowards unstable environmental influences by ascertaining stability and a certain degreeof autonomy even with extreme and improbable values (Sterman, 1994).

Environment of the Organization Considering the market in which the organization op-erates, variability may be embodied by a stock market crash, a jump in oil price, oracts of god. If other systems have not be monitored thoroughly enough or if it wasnot possible, an unexpected and strong competitor or the rise of a disruptive innova-tion could have a severe impact on the organization far from the usual variety, suchas the rise of a multitude of of hard disk drives superseding each other or mediastreaming endangering traditional record industry (Christensen & Overdorf, 2000;Christensen, 1997; Knopper, 2009). Also, elections, new laws or environmental orfinancial regulations can appear suddenly and impose existential threat for businesscontinuity.

Environment of the Application Landscape Variability in the organization having a bear-ing on the AL and may occur through an external server crash or loss of customerdata through intentional damage or by accident such as a lightning taking downAmazon’s cloud service (Donoghue, 2009). A string of layoffs within the organiza-tion can also happen unexpectedly as well as grave decisions by the organization’smanagement, for example establishing new values in Governance, Risk Manage-ment, and Compliance (GRC) or carving out parts of the company without an interimphase. If management provides enough time for the system to react, this variabilitywould rather be classified as a change or evolution as previously mentioned.

Transition The impacts of variability of the outer layer to the organizational layer andfinally to the AL can be severe and are often amplified when going inwards. Anact of god can lead to a server crash, lack of electricity, or loss of crucial data of theorganization. Amendment of laws or the installation of new regulations, such as Sol-vency II (CEIOPS, 2010; European Commission, 2013) or Basel III (Basel Committee,2010; Haerle et al., 2010) can effect an organization gravely and lead to a transfer ofsuch challenges inwards by new rules, restructuring, and heavily altered necessitiesto conform to new standards.

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4.2 Applying Orientor Theory to Deal with ClassifiedEnvironmental Influences

Following the elaboration of environmental factors of influence with the help of the clas-sification into the six characteristics, this section deals with the behavior of the system ALand how it acts or reacts to fulfill its system purpose and survive and ideally grow overtime. This viewpoint treats the AL as a black box, and is thus not useful to explain behav-ioral aspects of any elements such as persons, applications or subsystems. This is done inChapter 5. As introduced in Section 2.2, there exists no entirely closed system in practiceand therefore, systems behaving as if they were autarkic are not viable.

Here, the behavior or the alternative actions of the system are aligned to the aforemen-tioned six characteristics to ensure consistency and to seize the further development ofBossel (1994), who also introduced a general approach to explain behavior according tothese environmental characteristics. Introducing six so called orientors, which are definedas the

set of criteria that are relevant for the evaluation of system development [...]that systems (or their managers) use to orient their decisions and actions re-garding the system. (Bossel, 1994, p. 230)

Each orientor is referring to one previously mentioned environmental property as depictedin Figure 4.2. To fulfill the system purpose and to balance the opposing orientors, all ofthem have to be taken into consideration. This interplay is discussed after the separatedelineation of each orientor and possible manifestations or examples for the system AL.Rounded, rectangular icons for each orientor are self-developed and solely serve the pur-pose as visual aids.

Existence Referring to the normal state of the environment, or the organization, the ALhas to maintain its state variables constant with only slight variation to enable func-tioning under the given circumstances. A change of state variables outside the saferange can lead to non-fulfillment of the system goal and withdraw its capability tosurvive. To ensure the existence in the normal environmental state, three require-ments have to be met by the structure as well as the behavior.

Firstly, the system may not show any self-destructive behavior. This is importantin particular, when people are part of the system as they are among the very fewsystems, together with cells, that exhibit self-destructive behavior, from self-harm tocommit suicide (Baumeister, 2003; Raff, 1998). It has to be guaranteed, that subsys-tems have to be willing to survive themselves and to support survival of its suprasys-tem.

Secondly, the safe range of state variables can be exceeded if the system structurefails to work as it should. This happens if necessary communication between el-ements, for example, including and coordinating all stakeholders (Lucke, Krell, &Lechner, 2010) is not available or if important feedback is not provided by a failurein a monitoring system (Oppenheimer, Ganapathi, & Patterson, 2003). If the latter

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4.2 Applying Orientor Theory to Deal with Classified Environmental Influences

Figure 4.2: Bossel’s Basic Orientors Towards Characteristics of the Environment,Source: partly adapted from Bossel (1994, p. 244)

happens and monitoring subsystems is suddenly out of order, the existence in thenormal state is endangered as no necessary action or reaction can be undertaken byelements responsible for a certain system state. Among others, this could lead to vi-olation of stipulated Service-Level Agreements (SLAs) or to unnecessary downtime,which may induce costs, especially in time-critical business, that may harm the sys-tem for a long time (Pascual, Meruane, & Rey, 2008). To ensure the system structure,the connections between elements, such as communication between people, inter-faces used by other applications or fault indication by monitoring systems have tobe supervised and maintained constantly.

Thirdly, the AL has to exclude environmental threats that may jeopardize the sys-tem’s viability. This is not to confuse with environmental variability, where unpre-dictable conditions may also harm the system substantially and where security mea-sures are needed (as introduced later). While harms from variability such as a stockmarket crash or elections have effects on all subsystems of the environment, thesethreats are directed towards one system in particular. Here, external threats are, forexample, hacker attacks, data theft / industrial espionage, but also heat in the serverroom and can be dealt with firewalls, encryption or cooling systems.

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Effectiveness Referring to scarce resources in the environment, effectiveness in securingthese resources is the second orientor. Any system needs at least as much energy asit is able to emit to the environment. This must not necessarily be true for each andevery transaction, but in the long term, only acquisition of more input than outputallows the system to fulfill its purpose. Ideally, an effective measure carried out isalso efficient. But depending on the system, it allows deviations as non-efficientacquisitions can be effective in the long run and negative transaction can – up to acertain point – be absorbed by the system’s resource buffer. So the normal concern ofa system is not maximum efficiency of all processes but a long-term gain and overalleffectiveness of interactions, which at higher level complies with Helbig (2012) in thecase of overall IT goals.

To ensure this mentioned effectiveness, both, the internal system structure as well asits outside behavior have to comply to previously mentioned rules.

Internally, resources have to be distributed wisely and used in an efficient way. In-side the AL, internal effectiveness is characterized by the right distribution of re-sources such as budget, time and knowledge with following examples: R&D alwaysneed to be considered when allocating budgets to succeed it the long run (Bossel,1994, p. 239); planning and scheduling of projects must be adequate to not get intotime-cost trade-off problems (Brucker, Drexl, Mohring, Neumann, & Pesch, 1999);knowledge can be gathered and distributed among employees through collabora-tive knowledge management by installation of wikis or similar knowledge reposito-ries (Buckl, Matthes, & Schweda, 2010; Matthes, Neubert, & Steinhoff, 2011; Krcmar,2010, pp. 623ff.; OLeary, 1998).

Externally, the system has to ensure efficient acquisition of scarce resources. Expe-rience, connections to the environment and other subsystems and timely courses ofaction help to achieve such an efficient behavior. Considering the AL this means,that e.g. expenditures on external applications and services must not exceed theearnings from supported business processes. Also, the workforce and knowledge ofevery new employee must recoup its costs, which is a challenge in EAM in particular,as skilled employees such as experienced architects embody a very scarce resource(Lucke et al., 2010). Another important, and usually insufficient resource is budget– or money in general – which has to be acquired to ensure maintenance and fur-ther development of the AL. Being efficient in obtaining necessary financial meansis often difficult as it often involves considerable efforts at persuasion towards man-agement. Executive support or management commitment regarding EAM is oftenrather low as is often seen as an operational initiative rather than a strategic conceptwith long-term redemption (Kaisler, Armour, & Valivullah, 2005).

Freedom Refers to the variety of the environment and is also called freedom of action. Of-fering variety puts the system to expense and generally contradicts the desire to stan-dardize and therefore needs weighting to the respective interests of the underlyingsystem, where and to which degree standardization is necessary without reducingthe ability to serve environmental variety too much (Kersten, 2002; Liu, Chen, Chan,& Lie, 2008). But in general and recapitulating Ashby’s Law, that only variety can

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cope with variety (Ashby, 1956), any system has to react to an increase of environ-mental variety. Following three kinds of reaction may be suitable for a system toensure its own long-term viability (Bossel, 1994; Gray, Duhl, & Rizzo, 1969, p. 119).

1. The system is able to use its own state function repertoire where an appropriateresponse to environmental variety is already available. When looking at ALs inthis case, no further development or extraordinary effort has to be undertakento satisfy the needs for more variety. Exemplary, requirements of a process arechanged and now more people are allowed to finalize the process. By assign-ing a new role which confers needed rights to the additional people, this canbe done without overly big exertion. Similarly, constraints to data formats orsizes could be suspended or a new KPI could be added to a report by simplyactivating it – as it already existed in the reporting tool.

2. The system needs to adapt to new environmental variety without being able touse its existing repertoire. In order to find the appropriate response, it trans-forms the outside input into something it has responses for. This is the mostcommon form of reaction when thinking of increased variety. For instance,if a new tool is needed to satisfy new customer demands, this can either bebought, leased or self-developed and subsequently integrated according to thesystem structure. Another example includes the multitude of mobile devicesin the company, where the introduction of a new OS requires substantial effortto make applications work and to integrate new devices into everyday work.Also, to remedy deficiencies in offered variety, for example a lack of know-howand products, collaboration with other subsystems is possible through Mergersand Acquisitions (M&A).

This behavior of transforming outside input and delivering the appropriate re-sponse complies with Ashby’s Law and the need to co-evolve with the environ-mental complexity (Ashby, 1956; Kandjani et al., 2013).

3. The system is not able to use its existing repertoire of state functions and is in-capable or unwilling to adapt to environmental variety. To still remain viable,the system then may choose an entirely new environment or reduce outsidevariety by specializing more and finding a (new) niche within the environment.Abandoning the existing environment completely and finding a new one is notapplicable for the system AL itself, but thinkable together with the organization(its suprasystem), which would also have to reduce its variety through special-ization and finding a niche itself. But when it comes to subsystems of the AL orits elements, it is possible by all means, that i.e. people decide upon this thirdchoice. They may surrender because of increased requirements due to environ-mental variety and choose another environment by quitting their job.

Regard Referring to other systems within the environment, the recognition of existenceand anticipation of behavior of surrounding systems is important to survive. Asmentioned before, there are other subsystems in the same environment, which con-stitute an higher-than-average importance to the viability of the system. Conscious

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classification of other systems according to their importance and especially anticipa-tion of their behavior to adjust own action to it, is exclusively available for systemsabove level four (cf. Section 2.3) such as higher animals, humans or organizations.The regard for others is often concentrated on few orientors of the other systems,as for example, an organization is interested in the effectiveness of a competitor, inthe security of a supplier or in the adaptivity of customers. If a system is capableof distinguishing other systems according to their importance (which the AL is), itsbehavior is influenced in following two ways.

Firstly, the system needs a repertoire of capabilities and behavioral patterns to reactaccording to other systems’ behaviors. If, for example, management calls for moresecurity, the AL needs to have responses available which satisfy the need for an in-creased information security. Or if new employees are hired or there is a rise in cus-tomer requests, it needs to have the right measures to guarantee scalability (Kaisleret al., 2005). Building this repertoire and being prepared for action of other systemsinfluences the behavior of the AL.

Secondly, if the system has recognized other systems that are involved and has arepertoire with different possibilities, it must ensure to give the right response ac-cording to its own values and the action of the other system. This can either beachieved through instinct (an animal’s response to whether it is approached by aconspecific or a natural enemy) or through conscious decision with the ability to dif-ferentiate, weighing observations, and finding the appropriate reaction. Ideally, thelatter is applied in the case of ALs. For instance, if the marketing department startsa big promotion for customer acquisition, responses could include that architects,(1) provide for more demand through scalability, (2) transfer possible problems to aprovider who has to deal with it, (3) ask marketing to stop the promotion, (4) remaincalm and observe or (5) stay indifferent.

It still remains a personal choice according to normative values and education, whetherone’s regard for other systems is highly developed or not, especially on a personallevel. But when it comes to organizations or systems such as ALs, lacking regard forothers or egotism is life-threatening.

Adaptivity Referring to the change or evolution of the environment, adaptivity is nec-essary to maintain the repertoire of appropriate responses in new conditions andthus being able to serve the system purpose in the long-term. Without doubt, thereare overlaps with freedom of action and the increase in variety and complexity overtime and also with security concerning variability, but the characteristic (change) aswell as the measure (ensure adaptivity) relate to a long-term development. Whileoccasional misbehavior towards variety – e.g. not having the right responses in therepertoire for new challenges imposed by the environment – does not necessarilylead to severe problems, adaptivity and securing the system’s integrity demandsaccurate behavior – especially because the suprasystems are not going back at anyother point in time. Being heedless of new business models, inventions, such aspreviously introduced trends like service orientation, externalization of data, and amultitude of devices, or not behaving accordingly, may sooner or later end in futility

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of the system AL. To ensure adaptivity, two possible measures can be applied whenconfronted with environmental change: modify the behavior or alter the structure.

Modification of the system’s behavior does not change the system itself and ensuresintegrity especially as response to smaller shifts in the environment. In the case ofALs, this would incorporate measures such as parameter adjustment, i.e. increasethe budget for research and development to keep up with inventions from outsideor offering pay increase for IT staff to to cope with heightened demand or changethe behavior in respect to other subsystems by, for instance, start cooperating with acompetitor.

Alteration of the system structure, however, is a more profound measure that in-volves more planning and decision making to keep up with the environmental evo-lution. This is usually done in larger projects and success of these projects is criticalto the survival not only of the AL but also for the environment as the structure isthe core of the system. Adaptation to environmental change has become one of themost challenging tasks in EAM, as conditions in technology and business change sosteadily and rapidly, that a planned architecture is outdated before it is complete.This is why structural change depicts an ongoing effort (Armour, Kaisler, & Liu,1999; Lankhorst, 2005; Mykhashchuk, Buckl, Dierl, & Schweda, 2011). What is more,through this challenge of ongoing evolution, a very big issue of adaptivity in ALs isthe transition of structure and safeguarding concurrent operation at the same time(Armour & Kaisler, 2001; Kramer & Magee, 1990) and engineering under the condi-tion of uncertainty (Bubak, 2006). Examples of environmental changes and derivedchallenges – where big stakes of the AL are exposed to structural change – are thetransition to SOAs, distributed development, M&A or outsourcing (Assmann & En-gels, 2008; Bieberstein, Bose, Walker, & Lynch, 2005; Nidiffer & Dolan, 2005; Ross,Weill, & Robertson, 2006).

Summing up, environmental changes may alter behavior and the structure of thesystem AL radically and adaptivity is required to guarantee co-evolution and thusprovide stability and long-term viability (Kandjani et al., 2013). While detailed pro-cesses of how to behave in an environmental evolution are not outlined here, it ishelpful to mention conditions by Bossel (1994, p. 241) that foster adaptivity:

1. internal diversity and variety,

2. multiple-use of structural elements,

3. redundant, physically different processes,

4. decentralization and partial autonomy,

5. learning through memory as information storage,

6. building a repertoire of available alternatives and necessary change processes.

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Security Referring to the variability of the environment, security is needed for the systemin order to survive unpredictable fluctuations and events which could have severeimpact on the system’s states. For the system AL it is important, that especially crit-ical processes do not depend on variable or unpredictable outside conditions. Asmentioned above, security can be increased either by creation of relative autonomyor, if this is not possible, by obtainment of stability towards the (possibly influenc-ing) environmental factors the system keeps depending on.

To attain protection from variability in these two ways, following four different mea-sures are possible.

Firstly, by isolation or filtering, the system can be decoupled from unstable influ-encing factors. Not being dependent on influencing factors increases the system’sautonomy and thus security. Concerning the system AL, it should be isolated fromunreliable Internet-, power- or data storage providers. Also, offered as well as usedinterfaces should be reduced to a minimum and (physical) access to the (physical)infrastructure for non-employees has to be denied.

Secondly, by creating buffers and contingency plans, stability can be increased as animpact of variability can therewith be absorbed or made void. Such a buffer couldincorporate scalability of applications and services, mirroring and dispersing of data,and fallback options with alternative suppliers.

Thirdly, by setting up a self-stabilizing structure including control parameters andfeedback control (cf. Section 2.3). In the context of ALs, appropriate testing andmonitoring methods with automated or semi-automated initiation of countermea-sures can help the system to react proportionately and timely. Rather manual andmore abstract feedback control can be provided by application of the Plan-Do-Check-Act (PDCA) management cycle approach by Deming (1986) or – more specific in thecontext of EAs – the Building Blocks for Enterprise Architecture Management Solu-tions (BEAMS) approach by Buckl (2011) (cf. Buckl, Dierl, Matthes, and Schweda(2010)).

Fourthly, by defusing environmental inputs which usually constitute a serious threatfor the system, or by rendering them harmless, the system’s stability and securitycan be heightened. Examples are installation of i.e. lightning arresters to eliminateimpact from weather phenomena or shifting the burden of data safety and securityto an external supplier.

4.3 Describing System Behavior as an Interplay of BasicOrientors

After the introduction of environmental influencing factors with examples shaping thesystem AL, the system’s behavior towards each characteristic (in the form of orientors)showed several opportunities for action how the system may cope with environmental in-put. But, so far, only towards each characteristic individually. Now the question remains,

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4.3 Describing System Behavior as an Interplay of Basic Orientors

how the interplay of the six orientors works and eventually, what the overall system be-havior then is comprised of. This description of behavior is useful for explaining the be-havior of existing systems, the success of proven systems, the causes for failed systems,and also to apply this knowledge for the design of new systems.

Behavior has two aspects here. In order for the system to remain viable it has to ensurea minimum satisfaction of every orientor. Only if this is achieved, it may – providedthat the system is capable of learning and not of invariant structure – focus and developtowards a few orientors. By doing the latter, the system is able to shift itself within theenvironment to a certain extent according to its interests, strategies and norms (Bossel,1994). This is depicted in Figure 4.3, where a minimum satisfaction is characterized bythe inner circle, an increased level by the outer circle and a high level, when arrows gobeyond that level. The distinction between an increased and a high level has not beenintroduced before but is needed here, to show differences of manifestations whether theyare increased but still flexible and displaceable into the opposite direction or whether thelevel is inherently high. An increased or high level of one orientor leads to minimumsatisfaction of the opposite orientor, which is indicated by decreased opacity of the arrowsin the figure but is subsequently outlined in more detail and also illustrated in an examplein Section 6.1.2, where conditions and distinction between an increased and high level oforientor satisfaction is also further examined.

Figure 4.3: Different Levels of Orientor Satisfaction,Source: based on Bossel (1994, p. 247)

Altogether, there are four different levels of orientor manifestation possible: insufficient,minimum, increased, and high. For the sake of completeness, these levels are consideredto be on an ordinal scale. The manifestation of the individual satisfaction of an orientorassessable and controllable by respective capabilities which increase or decrease the re-

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spective orientor. In the case at issue, the capabilities of the AL and its subsystems are,for example, redundancy, modularity or memory utilization. Many of these capabilities,which may determine an orientor are outlined in Section 5.3.

Regarding the minimum satisfaction of every orientor, this has to be provided by everysystem, even from those with invariant structure. Because even in unconscious systems,this requirement is enforced by the environmental evolution, as disregard of an orientorresults in handicaps and thus poses a threat to the continued existence of the system. Whilein invariant structures, such as simple organisms, extinction hits the system with a bigdelay, in self-organizing systems and those with conscious actors (such as organizations),disregard of an orientor can have almost immediate effect. If a system is not able to providethe minimum level of an orientor, above all, it has to attend to appropriate measures (cf.Section 4.2) to get rid of these deficiencies. It is not possible to make up for deficits of oneorientor by excess of another one.

As soon as the minimum level is achieved at every orientor, the system may then focuson single orientors and by execution of appropriate measures to effectuate its behavioralnorms or values (in the case of humans) or its strategy (in the case of ALs). But, as weare dealing with conscious actors, it has to be kept in mind, that these subjective decisionsmay not always conform to what common sense would suggest.

Introduced orientors are – as briefly mentioned before – interconnected insofar as eachorientor has a diametrically opposed orientor, where weighing of interests and awarenessof this trade-off is necessary. This is especially important before undertaking measures toenhance one side, as the opposite may hence fall under the minimum satisfaction limit.

This is where simplicity ceases when looking at systems like the AL as a whole, as thereis no higher authority that is able to steer the whole system towards an orientor, such assecurity, on his own. Only if plenty of subsystems are able to increase their security by ap-propriate measures, the whole system can be shifted within its environment. Undoubtedly,specification of strategies and setting targets is necessary, but whether and how the sub-systems react and change their goals is not fully controllable by one “hand of god”. Themultitude of applications, people and interconnections make the AL a complex system,which is not manageable from an outsider’s view. Not considering inside behavior andthus treating the system as a black box is marginally promising. Management of complexsystems is impossible, but when knowing the inside structure and behavior, managementin complex systems is expedient.

Why the AL cannot be shifted within the environment by one instance lies in the self-organization of its subsystems. This is subsequently outlined in more detail to increasethe understanding of why and how the overall system has to be broken down into smallerunits. Afterwards, it is developed, how these units can be aligned and managed by EAprinciples and how merging the behaviors of the subsystems and the dynamics supportdecisions concerning IT governance and IT strategy to provide conditions for success andlong-term viability.

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5 Breaking Down ALs into Subsystems

The behavior of each individual is explained by the structure and arrange-ment of the lower individuals of which it is composed, or by certain prin-ciples of equilibrium or homeostasis according to which certain states of theindividual are “preferred”. Behavior is described in terms of the restorationof these preferred states when they are distributed by changes in the environ-ment. (Boulding, 1956, p. 201)

In an ideal world, the course of action to ensure a system’s success would just includekeeping an eye on the environment and thus – solely by setting parameters for orientorsand deriving and effectuating needed measures accordingly – maintaining the satisfactionof the system purpose (business IT alignment in the case of ALs).

But just as the whole is more than the sum of its components in systems theory, steeringthe whole takes more than shifting its components altogether.

5.1 From the System to Individual Subsystems in AL

Each and every component or subsystem has its unique environment which it has to adaptto in order to survive. Only if all systems serve their purposes, the overall viability can beensured, unless the system possesses enough robustness. Every environmental changeof the AL is, to a certain extent, forwarded (often filtered or amplified) to every subsys-tem and each “system should be designed to make its own corrections when necessary”(Johnson, Kast, & Rosenzweig, 1964, p. 379). Sometimes subsystems have to radicallychange or give up their identity to ensure integrity and still contribute to the overall sys-tem success. Anticipating an example: while the environment may force an organizationto be more effective in securing resources, a subsystem such as R&D has to do the op-posite and provide adaptivity to develop measures that can be used by other subsystemsto become more effective. Shifting all subsystems toward effectiveness would not lead todesired outcome.

But to understand the dynamics of the underlying subsystem structure, they have to betaken care of individually. Where and how to break down the structure is strongly depen-dent on the distribution of the AL, the industry and other given conditions (Hess, Humm,Voss, & Engels, 2007). This break down can either be based on the given structure of the ALor conducted in an artificial way: Dividing the AL into subsystems by means of its exist-ing structure can be done dependent on (Buckl, Ernst, et al., 2009a; Buckl, 2012; Hanschke,Giesinger, & Goetze, 2013, pp. 164ff.; Hess et al., 2007):

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• business processes,

• organizational units,

• functional areas,

• technology stacks,

• plants,

• countries,

• products,

• markets,

• distribution channels.

Other examples for rather artificial subdivisions that can be undertaken are the applica-tion of the VSM (cf. Section 2.4) and subdividing the AL into these five subsystems orthe approach of Iyer and Gottlieb (2004) which subdivides the system into the process-,information-, infrastructure-, and organization domain.

In order to understand the behavior and needs of these subsystems, it is often not sufficientto look at those systems as a black box either as it has to be broken down into its parts, too.This should be done by the the person in charge of the respective subsystem. Iteratingthis break down is necessary down to the level, where the smallest subsystem is able toexplain the behavior within his environment. The person in charge of multiple subsystemsthen must be capable of weighing and aggregating the orientor manifestations of his sub-systems and by adding his overall understanding of the environment may consequentlyreport upwards.

It is the subsystems and their behavior that make the AL a complex system, where apply-ing the theory of CAS – as Choi et al. (2001), Langdon and Sikora (2006), and Janssen andKuk (2006) did – is standing to reason (Sage & Cuppan, 2001).

5.2 Examining Subsystem Behavior in the AL by Applying CASTheory

To increase the understanding of the system AL and its behavior it is, as just mentioned,important to understand the dynamics of the subsystems as there is no single authorityable to steer the whole system on his own. The same holds true in the theory of CAS,that “refers to a system that emerges over time into a coherent form, and adapts and or-ganizes itself without any singular entity deliberately managing or controlling it” (Choi etal., 2001, p. 352; Holland, 1995). But implications on how to manage within such a systemare deducible. Thus, for one thing, it is subsequently formulated why theories of CASare applicable for ALs as well as their consonances and, for another thing, it is afterwardsoutlined, which general management implications and insights can be derived from thistheory.

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5.2 Examining Subsystem Behavior in the AL by Applying CAS Theory

As introduced in Section 2.5, several researchers in a multitude of disciplines have studiedCAS and applied the theory to gain insights into their respective system’s behavior. Instudying EAs, down to the present day and to the latest knowledge, this has only be doneby Janssen and Kuk (2006) for electronic government and Sutherland and van den Heuvel(2002) for application integration. The latter perceive the evolution of Enterprise Applica-tion Integration (EAI) towards a living organism (cf. Figure 5.1) and applying CAS, whichorigin in biology, as promising for enterprises to “achieve the highest level of integrationthat allows flexible living virtual alliances with loosely coupled aggregated and intelligentemergent behavior” (Sutherland & van den Heuvel, 2002, p. 64). They depict five phases ofthe development of EAI towards this dynamically collaborating business objects from lowto high productivity. Starting from rather simple batch transfer from 1970 to point-to-pointinterfaces in 1990, via tightly coupled web transactions in 1995 towards a lowly coupledwork-flow orientation with static systems at the time of their publication. The same shifttowards increased complexity during the evolution of EAI occurred in the evolution ofALs (cf. Section 2.4).

Figure 5.1: Evolution of the EAI Towards a Living Organism,Source: adapted from Sutherland and van den Heuvel (2002, p. 61)

Even though these approaches are not the very latest and do not match the necessities andcircumstances of ALs entirely, they show the applicability of CAS in the field of EAM. Togain insights and understanding dynamics in ALs and to give up the idea of full control,we focus on general features, characteristics and functioning of CAS as described here.Fully explaining CAS and apply it in every single detail to ALs is out of scope for thisthesis. As mentioned before, there should not be any claims to steer the system as a whole,but to understand the dynamics and necessities of its subsystems in order to be able toprovide a suitable general conditions in which the subsystems may emerge.

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According to Holland (2006), being considered as CAS, requires four major features: par-allelism, conditional action, modularity, and adaptation and evolution (cf. Section 2.5). An ALcan be recognized as such a system:

• Parallelism is satisfied when the system consists of many different parts which actsimultaneously. Depending on the business, distribution, and other circumstances,the AL can be comprised of subsystems according to different domains, countries,processes, products, etc.

An AL can be considered a SoS as they fulfill most of the five characteristics posedby Sage and Cuppan (2001, p. 327):

– well-substantiated purpose and continuous operation when suprasystem is gone

– managed in large parts on their own

– geographical distribution

– evolutionary development

– emergent behavior

• With every component consisting of elements such as people and applications, con-ditional action is provided in every subsystem, whether it may be conscious (hu-man) or not, or whether it is complicated feedback or rather simple if/then struc-ture. For example, ’if a new customer is acquired, then a new record has to be put intothe database’ or ’if a product is out of stock, then remove offer’. Several interlockingsequences of such conditional actions, for example the latter in combination with ’ifa product is out of stock, then order new batch’ and ’if order a new batch, then check otherstocks from same supplier’ are parallel executed programs.

• Groups of such rules depict what Holland (2006) calls subroutines. These allow thesubsystem to handle new situations faster and more successfully as testing new ac-tions all the time. Combining rules, using them as building blocks, and proving theirconvenience is an essential part of learning and anticipation. This modularity in ALsexists in several ways, for example, when new rules are made, that are applicable inseveral subsystems (different countries, for different products) and is generally themeasure to remove unwanted redundancy.

• Every component is able to change according to environmental input and usuallydoes so to ensure its own survival (Johnson et al., 1964). Instead of random varia-tions, the subsystem is able to improve its performance through correct credit assign-ment and suitable rule discovery (by using aforementioned building blocks). Adap-tation and evolution is the “pivotal characteristic of CAS” (Holland, 1992, p. 19)and is essential to survive the competition in a perpetually novel environment. Thisability to adapt and evolve exists for every subsystem, such as domains or divisionsin the AL, which are able to adapt to their individual environment. Whether theyare allowed to do so is a different matter – but to satisfy this fourth feature of CAS,having the ability suffices.

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5.2 Examining Subsystem Behavior in the AL by Applying CAS Theory

Abstaining from a judgment whether having ALs that behave like CAS is inherently goodor bad, but understanding the key principles of CAS enhances the manageability and even-tually success by applying the right strategy and providing the right general conditions forrealization. Four principles, adapted from Pascale (1999), have to be kept in sight whentrying to manage in complex system.

Non-linearity CAS exhibit weak cause-and-effect linkages, and a minor change may re-sult in a huge effect or vice versa. This non-linearity especially becomes visible inthe long term, where “future outcomes are arbitrarily sensitive to tiny changes inpresent conditions” (Gell-Mann, 1995, p. 7), often because changes in one subsystemmay stimulate other changes in other subsystems and often lead to counterintuitivebehavior that can hardly be anticipated (Flood, 1993, p. 26). Similarly, Dorner andSchaub (2002, pp. 235ff.) point out the oblivion of constraints in planing and in-comprehension of exponential or logistic growth of humans. Non-linearity makesguidance an intricate endeavor: “one cannot direct a living system, only disturb it”(Pascale, 1999, p. 3). But to provide a little guidance for change, Plsek (2001, p. 317)points out: “rather than agonizing over plans, the goal is to generate a good enoughplan and begin to observe what happens. Then, modifications can occur in an evolu-tionary fashion.”

Non-equilibrium Achieving equilibrium is not a goal of CAS, because “for a living sys-tem, equilibrium corresponds to death” (Kauffman, 1996, p. 52). “Far-from-equilibriumconditions tend to create a dynamic stability” (Dooley, 1997, p. 78). In nature, onlytwo forces prevent a system from equilibrium: the threat of death and the promiseof sex (Pascale, 1999). Admittedly, applying these forces to the AL and its subsys-tem oversteps the mark, but trying to direct systems towards a final state is a futileundertaking which holds true for ALs and other CAS, for example as “researchersrecognize that economies, like ecosystems, may never settle down into an equilib-rium” (Lansing, 2003, p. 193). And also in organizations, chaos widens the spectrumof option and forces it to “seek new points of view. For an organization to renew it-self, it must keep itself in a non-equilibrium state at all times” (Nonaka, 1988, p. 59).

Edge of Chaos When confronted with a complex task or increased environmental com-plexity, CAS tend to be at the edge of of chaos as a certain degree of instability in-creases the viability of the system. “Generative complexity takes place in the bound-ary between rigidity and randomness” (Pascual et al., 2008, p. 10). Constantly chang-ing conditions leave the AL in a steady state of transition. To find one’s bearings dur-ing increasingly long periods of transitions with near-chaotic behavior is by far morechallenging than describing change “as moving from one equilibrium state (water)to another (ice)” (Pascual et al., 2008). This ongoing transition is a major task of man-aging in the AL by balancing between chaos and equilibrium through co-evolutionwith the environment (Kandjani et al., 2013).

Self-Organization Subsystems in CAS are self-organizing and learning. “Systems that arepushed far-from-equilibrium (at the edge of chaos) can spontaneously self-organizeinto new structures” (Dooley, 1997, p. 77). Self-organization arises from intelligenceof conscious or animate actors and leads to emergent complexity. This complexity

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leads to a non-foreseeability of what will happen as it creates not one future butmany (Pascual et al., 2008). But when looking at organizations or the AL, then “self-organization presents a more appropriate way of transformation than the mere shiftin equilibrium levels that is the legacy of the traditional approach” (Goldstein, 1994).Again, equilibrium has to be avoided in order to self-organize and consequently forthe system to remain viable.

Summing up, these inherent principles of CAS, namely non-linearity, non-equilibrium,tendency towards the edge of chaos and self-organization, show, that managing such asystem and shifting it according to desired orientors by one “hand of god” is not possi-ble.

As most seasoned managers know, the best-laid plans are often perverted throughself-interest, misinterpretation, or lack of necessary skills to reach the intendedgoal. (Pascale, 1999, p. 12)

5.3 Capabilities Determining Orientor Satisfaction in AL

Aforementioned disability to shift and manage a system like ALs from the outside and itsself-organizing characteristics, which limits the possibilities of control, led to the assump-tion, that such a system has to be managed from within. But before providing the best pos-sible conditions and guidance for subsystems, which is done in Chapter 6, the capabilitiesof the system and its subsystems are outlined first. To increase the awareness of the peo-ple in charge and to underline the applicability of introduced orientor theory, exemplarycapabilities of the system are explained and mapped to the respective orientor, which thecapability fosters. Concerning the previously mentioned interplay of the orientors, thesame applies for the capabilities here: fostering one orientor by increasing one capabilitymost likely impedes the opposite orientor. After having assessed the most important ca-pabilities for such a system, they can subsequently be used to determine the manifestationof the orientors, for example, by using particular indicators and models such as CapabilityMaturity Model Integration (CMMI). This is discussed at the end of this section.

During the introduction of orientors (cf. Section 4.2), general, as well as AL-specific exam-ples of behaviors facilitating an orientor’s satisfaction have been used, such as introducinga SOA to adapt to a changed environment or BYOD to increase freedom to cope with va-riety. But in order to enable these behaviors, the following list of examples depicts whichorientor can be satisfied by which capability of the AL.

Existence In addition to the necessity to not show any self-destructive behavior, the sys-tem and its subsystems require the ability to monitor the system states by applicabletools and measures. Another capability is to structure activities, especially businessIT interactions by providing well-elaborated Information Technology Service Man-agement (ITSM) and the possibility to satisfy practices as introduced with Informa-tion Technology Infrastructure Library (ITIL) standards. While especially monitor-ing such as in IT Operations Management is in the focus here as it constitutes a major

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capability to ensure the existence in the normal state, parts of such big frameworkslike ITIL may also increase other orientors. One example is the business continuitymanagement, which is fostering security to cope with environmental variability. Butthe majority of measures in ITIL facilitate the existence orientor.

Effectiveness Capabilities increasing this orientor of the system either have a focus on se-curing scarce resources such as money, time, and knowledge in an effective manneror distributing the available resources inside wisely. One important capability con-cerning the distribution of resources is a carefully thought out Project Portfolio Man-agement (PPM). While single projects, when examined individually, often increaseother orientors, the right distribution of resources (first and foremost budget) to themost important projects and denying vague initiatives does increase the system’s ef-fectiveness. In addition to the choice of the right projects, planning and schedulingof chosen projects and PM in general support the overall effectiveness of the systemby, for example, decreasing idle times and utilizing available knowledge where andwhen it is needed. Another measure to become more effective is the optimizationof required Central Processing Units (CPU) or data storage for applications and thecommunication in between. Recruitment of skilled programmers and architects isalso very important to gather skills and knowledge from outside to become more ef-fective. Consequently, the training of staff as well as knowledge management, suchas installation of a collectively used wiki, is essential to stay effective.

Freedom Environmental variety swaying the system can be of various kind, as intro-duced before. But whether it is about different OSs, devices, products, or services,some general capabilities can be found, which enable the system to increase the man-ifestation of its freedom orientor. Among others, these include the expandability ofapplications and modularity for decreasing variety. If the environment requires aneither greater or smaller variety, the system is able to react in an uncomplicated fash-ion as has the ability to add another feature or service without restructuring andlikewise may switch off functions that have become dispensable without negativeeffects on other processes. Freedom is furthermore promoted by a decentralized de-cision making by people knowing their environment and the needs of the respectiveniche. Decisions upon processes, products, or services by one centralized authoritycan not only decrease freedom but also overwhelm the people in charge when theyare confronted with a strong increase in variety. Also, openness to foreign input byestablishing partnerships with, merging with, or acquiring other systems enables theAL to increase the freedom orientor through increased opportunities to adapt to en-vironmental variety. Similarly, the openness of the system to hire, work with, andintegrate highly diverse people is an important ability to satisfy a high degree ofvariety. This includes different ages, experiences, educations, origins, genders, etc.

Regard Surrounding systems that may have effects on the own continuance have to bedetected and monitored in order to not miss relevant behavior of these other sys-tems. Therefore, the heightened importance of individual other systems has to berecognized, they have to be monitored, behavior should be anticipated as far as pos-sible and appropriate action has to be provided in the own repertoire to always havean answer whenever necessary. Capabilities of the AL to increase the regard orientor

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include the maturity of (business) analytics tools to determine the importance as wellas monitor other system. Especially when it comes to the anticipation of behavior, itis important to have the ability to refer to saved (historical) data to observe a wholeperiod of time rather than a single point in time. These monitoring tasks shouldfurthermore be equipped with sensors to provide seamless responses.

Adaptivity As environmental conditions change over time – not uncommonly in an un-expected fashion – ALs are required to adapt to this evolution. These changes haveto be recognized by the system, classified whether they are relevant, transformedto realizable actions and eventually implemented. Rigid organizational structurescounteract this necessity and therefore, one major capability is to provide flexibleorganizational structures which allow outsourcing, re-organization, and integration.Referring to the people working for and with the AL, they have to be willing to ac-cept changes, modify their own behavior and live an open culture rather than beingstuck in daily routines. Here, capabilities that have been mentioned before, namelyinternal diversity and decentralization also facilitate this orientor. Due to the fact,that the environmental evolution is an ongoing process, decoupling and proceed-ing step by step is a crucial ability to increase adaptivity as current changes may besubjected to new changes before their finalization.

Security Protection from environmental variability can be provided by either defusingharms, filtering them, having the right response in a self-stabilizing structure orby using established buffers. Concerning the right responses, one vital ability forthe system is to implement previously mentioned business continuity managementwhich is able to provide appropriate counteraction in case of mainly unexpectedappearances of potentially serious harms. Another important capability to increasesecurity can be achieved through the establishment of redundancy, for example bymirroring and dispersing data. In fact, many harms caused by variability can bedealt with a thoroughly implemented risk management throughout the whole ALincluding all applications and people that work in or with it.

A summary of previously outlined capabilities which facilitate the respective orientor isdepicted in Figure 5.2. Mentioned and depicted examples are not complete and are in-tended to get an idea, which abilities of the AL could foster an orientor manifestation incase of satisfaction or impeded in case of disregard.

Some capabilities possess the power to increase more than one orientor, such as introduceddiversity of employees, for example, which heightens adaptivity, freedom, and probablyalso the regard for others. Nevertheless, the constraint on the disability to increase twoopposite orientors remains. Also, some capabilities overlap with others as redundancy ismost likely also part of risk management and the same applies for ITIL and ITSM. What ismore, when listing ITIL, for example, it is of course not applicable to every system as theymight use other standards. The capabilities for each AL have to be assessed individuallybut can be derived from the enumeration on hand. Although there exist overlaps and mul-tiple influences on orientors of mentioned capabilities, they can be further examined andused as reliable indicators for orientor satisfaction. In order to measure the manifestationof an orientor, indicators which are directly taken from the examples above are applicable,

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Figure 5.2: Capabilities Facilitating Orientor Manifestation

such as measures on CPU or the diversity of employees. Another possibility is to breakdown capabilities which hold a multitude of indicators, such as ITIL or PM. By further ex-amination of the indicators, more precise assertions can be made which part fosters whichorientor. Some capabilities offer the possibility to use existing or develop maturity modelsto determine how well they have been introduced to the AL and thus show, how muchthey facilitate the respective orientor. Examples to measure such capabilities are the useof maturity models such as the CMMI or determining whether a certification (e.g. ITIL) isawarded or, if it exists, the level of certification.

Weighing and aggregating the determined indicators consequently leads to the assessmentof the as-is orientor manifestation and ultimately to the overall orientor satisfaction. Cer-tainly, introduced capabilities can be used as reference points for management as well,in order to increase or decrease orientor satisfaction. But when recapitulating the prin-ciples of CAS and the theory behind a SoS as introduced earlier, non-linearity and self-organization hamper simple if/then assumptions such as “hiring highly diverse peopleincreases freedom to cope with environmental variety”.

Perceiving and treating ALs as CAS rules out the possibility for one single instance to fullyunderstand and control it. The dynamics within CAS and thus within the AL asks for a

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different approach: managing within the system. Instead of trying to control the systemas a whole, the focus has to be directed towards providing the best possible conditionsfor the subsystems to self-organize. Control has to be confined to ensuring the right levelbetween equilibrium and chaos for the respective (sub-) system to stay viable.

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6 Managing in the AL: Condition Providingand Subsystem Guiding

In order to manage in the AL, the best possible conditions have to be assessed and real-ized, which is outlined in Section 6.1. Ensuing, control mechanisms such as EA principlesare applicable to guide the orientor satisfaction of the self-organized subsystems. Togetherwith practical examples of EA principles providing guidance for subsystems this is delin-eated in Section 6.2.

6.1 Providing the Best Possible Conditions for the Subsystems toSelf-Organize

Before the actual management in the system by applying EA principles for its self-organizingsubsystems can be done, the approaches of CAS and SoS are used in this section to developthree steps to provide suitable framework conditions for the subsystems. Firstly, the neces-sities of the subsystems have to be assessed individually, weighted, and aggregated. Thisis outlined in Section 6.1.1 with an example of different plants of a company. Secondly,orientor manifestations of different strategic decisions have to be examined which is donewith an example of three different forms of IT organization in Section 6.1.2. Thirdly, clus-ters involving several subsystems may be introduced to satisfy differentiated necessitiesof different groups of subsystems. This is described in Section 6.1.3.

6.1.1 Aggregating Subsystem Orientor Manifestations

For assessment of the as-is state of the AL, understanding the overall and individual en-vironments and their evolution is less important and by weighing and aggregating, theoverall orientor manifestation can be determined in an easier fashion, as seen in the pre-vious chapter dealing with capabilities. Providing the best possible conditions for a to-belandscape, however, is a key challenge which still has to be done by one person in charge ofthe AL manually and – up to a certain degree – with gut instincts. Adding up all necessitiesof the lowest level elements is not possible because of the aforementioned dynamics andnon-linearity. Only individually weighted orientor manifestations from the lowest level,e.g. via domains, aggregated hierarchically upwards can serve as a basis to transform ITgoals into strategic decisions and EA principles. The more profound the understandingof a person in charge is of the behavior of his subsystems and elements, the faster and

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6 Managing in the AL: Condition Providing and Subsystem Guiding

Figure 6.1: Aggregation of Subsystems’ Orientors Involving Environmental Influences

better the strategic decisions can be. Every step upwards subsuming the orientor mani-festations needs individual weighing and assessment whether, e.g. outliers are acceptableand if other subsystems can make up for a deficiency in one orientor (consider the R&Dexample above). An exemplary aggregation of such orientors is depicted in Figure 6.1,where different plants of the overall company embody the subsystems. To finalize the ori-entor manifestation of the overall system, not only the subsystems are relevant but also theenvironment of the overall system. Understanding this interplay, weighing and aggregat-ing the subsystems’ orientors, and involving the own environmental influences is a crucialtask for management.

After having assessed the individual behavior along the orientors of the subsystems inthe AL, and knowing the scope of the own environment, strategic decisions may followto achieve IT goals, first from the top management and subsequently from the people incharge of the subsystems. There are numerous strategic decisions to be made, such as ITgovernance, make-or-buy, ITSM, and portfolio, but here, only one example concerning ITorganization is used to increase the understanding of how to decide for the whole systemand satisfying the majority of demands from the subsystems.

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6.1 Providing the Best Possible Conditions for the Subsystems to Self-Organize

6.1.2 Assessing Orientor Manifestation for Different Forms of ITOrganization

One question regarding IT organization is whether the IT is centralized, decentralized orof federal nature (Brown, 1999) and an important scope on homogeneity of used applica-tions in the AL. The right distribution of responsibilities and decision making power con-cerning the IT in the federal approach is among the eight imperatives for IT organizationintroduced by Rockart, Earl, and Ross (1996). Therefore, the orientor manifestation of thedifferent organizational forms has to be assessed, which is done subsequently in Table 6.1for the centralized, Table 6.2 for the decentralized, and Table 6.3 for the federal approach.While in the centralized approach, the IT department is in charge of all crucial decisionswith a focus on efficiency, in a decentralized form, every Business Unit (BU) runs its ownIT and is rather autonomous. In the federal approach, a Governance Board (GB), which isunder the control of the Chief Executive Officer (CEO), controls up to which degree busi-ness units are able to make independent decisions. This approach seeks to leverage thebest of both worlds. Within each form, every orientor is examined by review of relevant ITgovernance literature. Statements predicating low support for the respective orientor areoutlined in the first column whereas propositions for strong support are subsumed in thesecond column. Blank cells illustrate that no declarations were found in literature whichback this particular combination. Finally, the last column represents the decision, whetherthe orientor’s manifestation is low, medium, or high for the respective form of IT organi-zation. Even if there is no support of one orientor found in literature and it is eventuallydeclared low, it does not mean, that it is non-existent as every system still has to ensurethe minimum satisfaction of every orientor (cf. Section 4.3).

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Low support High support Result

Exis

tenc

e “Specialization under centralizationincurs risks due to bounded ra-tionality and information overload.”(Peterson, 2004, p. 46)

Control is a primary concern of a cen-tralized approach. (Warkentin & John-ston, 2007, p. 53)

Effe

ctiv

enes

s Companies with centralized or federalIT governance forms are likely to befirms that compete in single or relatedbusinesses that seek IS cost efficiencies(through economies of scale and stan-dardized infrastructures) as well ascross-unit synergies. (Brown & Mag-ill, 1998, p. 185)“Low business costs through stan-dardized business processes.” (Weill& Ross, 2004, p. 8)High synergy, standardization andspecialization. (Peterson, 2004, p. 46)Leveraging established technologyand vendors is a chief advantage ofcentralized governance. Reducedduplication of effort, resources,and expertise leads to efficiencygains. (Warkentin & Johnston, 2007,pp. 53ff.)Centralized IT governance increasesthe effectiveness of the implementa-tion stage. (Tarafdar & Gordon, 2004)

Free

dom “Centralization leads to greater spe-

cialization, consistency, and standard-ized controls.” (Peterson, 2004, p. 45)Centralized approaches oper-ate in highly related markets.(Sambamurthy & Zmud, 1999, p. 279)

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Reg

ard “Centralized systems are isolated

from customers and real businessconcerns.” (Warkentin & Johnston,2007, p. 54)A centralized governance form cre-ates a dysfunctional effect: “a struc-tural barrier to strong alignment withbusiness management.” (Brown, 1999,p. 6)

Ada

ptiv

ity Low flexibility. (Peterson, 2004, p. 46)

Slow response time. (Warkentin &Johnston, 2007, p. 53)Added bureaucracy and inflexibility.(Warkentin & Johnston, 2007, p. 54)

Secu

rity Formal assessment of technological

requirements and professional evalua-tion of choices results in lower techni-cal risks. (Warkentin & Johnston, 2007,p. 53)Highly reliable due to structured sys-tems design and maintenance proce-dures. (Warkentin & Johnston, 2007,p. 54)High environmental stability.(Peterson, 2004, p. 48)

Table 6.1: Assessing Orientor Manifestation in a Centralized IT Organization

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Exis

tenc

e Communication and monitoring isdifficult, as a corporate level coor-dination role may not even exist insuch firms, and horizontal coordina-tion initiatives may even be voluntary.(Brown, 1999, p. 6)

High business competency and in-formation intensity. (Peterson, 2004,p. 48)

Effe

ctiv

enes

s Firms with such IS contexts have sig-nificantly lower opportunities for IT-related cross-unit synergies. (Brown &Magill, 1998, pp. 182ff.)“Establishing few, if any, enterprise-wide technology and business processstandards.” (Weill & Ross, 2004, p. 9)Low standardization, specializationand synergy. (Peterson, 2004, p. 46)

Free

dom “Increased information is necessary to

facilitate market awareness and re-sponsiveness to increasingly sophis-ticated customers. Those needs areoften best served by a decentralizedstructure.” (Brown & Magill, 1998,p. 185)“Top performers on growth minimizeconstraints on creativity and businessunit autonomy.” (Weill & Ross, 2004,p. 9)“For operations in markets of low re-latedness” (Sambamurthy & Zmud,1999, p. 279)Flexibility and empowerment is achief advantage of decentralized gov-ernance. (Warkentin & Johnston, 2007,p. 53)

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Reg

ard “Firms with a decentralized IS

decision-making context are leastlikely to invest in horizontal mecha-nisms for corporate/division collabo-ration.” (Brown, 1999, p. 6)

High customer responsiveness.(Peterson, 2004, p. 46) Does notmake up for all other systems and iseventually classified as low.

“Decentralized systems allow individ-ual units the autonomy to managetheir own IT resources without regardto other units.” (Warkentin & John-ston, 2007, p. 54)

Ada

ptiv

ity Firms with such IS contexts are likely

to be firms with highly autonomousbusiness units. (Brown, 1999, p. 6)High flexibility. (Peterson, 2004, p. 46)Decentralized companies are more fo-cused on innovation and time to mar-ket. (Weill & Ross, 2004, p. 9)

Secu

rity Low environmental stability.

(Peterson, 2004, p. 48)“They require few governance mecha-nisms, often relying only on an invest-ment process that identifies high pri-ority strategic projects and managesrisk.” (Weill & Ross, 2004, p. 9) The lat-ter due to few established standards.

Table 6.2: Assessing Orientor Manifestation in a Decentralized IT Organization

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Exis

tenc

e “The complexity of matrices can over-whelm managers.” (Weill & Ross,2004, p. 13)Coordination challenge. (Peterson,2004, p. 72)

Effe

ctiv

enes

s “This model tries to achieve both ef-ficiency and standardization for theinfrastructure, and effectiveness andflexibility for the development of ap-plications.“ (De Haes & Van Grem-bergen, 2004, p. 2) So it remains atrade-off and eventually is classifiedas medium.

Companies with centralized or federalIT governance forms are likely to befirms that compete in single or relatedbusinesses that seek cost efficiencies(through economies of scale and stan-dardized infrastructures) as well ascross-unit synergies. (Brown & Mag-ill, 1998, p. 185)“Low IT unit costs; reuse of standardmodels or services.” (Weill & Ross,2004, p. 8)

Free

dom Conflicting contingencies induce a

federal IT governance and resultin partly autonomous management.(Sambamurthy & Zmud, 1999, p. 279)(only partly autonomous)

This arrangement allows “for sharedexpertise across the firm”, but regards“the benefits of local customizationmore important than global standard-ization.” (Weill, 2004, p. 9)

Reg

ard “The federal archetype ensures con-

sistency across the operational unitsvia firm-wide strategies while recog-nizing differences among the businessunits.” (Weill, 2004, p. 17)The Federal governance form there-fore fosters strong business to IS col-laboration through the reporting ar-rangements for the systems develop-ment units. (Brown, 1999, p. 6)High customer responsiveness andbusiness ownership. (Peterson, 2004,p. 46)

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Ada

ptiv

ity “This model tries to achieve both ef-

ficiency and standardization for theinfrastructure, and effectiveness andflexibility for the development of ap-plications.“ (De Haes & Van Grem-bergen, 2004, p. 2) So it remains atrade-off and is eventually classifiedas medium.

High flexibility. (Peterson, 2004, p. 46)Se

curi

ty The pursuit of a dual business strategy(low cost, high value) is not withouthigh risk of failure. (Brown & Magill,1998, p. 185)

Accountability for information secu-rity must be shared by all employees,and not only the information securitymanager. (Von Solms & Von Solms,2004, p. 375)

Table 6.3: Assessing Orientor Manifestation in a Federal IT Organization

Summing up, the three basic forms of IT organization exhibit exceedingly different mani-festations of orientors. While a centralized IT department offers a high level of security andeffectiveness, a decentralized is suitable for high necessities on adaptivity and freedom.Both show low regard for other systems as they are specialists either for the functional ortechnical needs but have a medium level of existence – between information overload andhigh competency. Both, the centralized and decentralized approach offer no variation inthe manifestation of the orientors.

However, the federal IT organization provides such variations, or fine tuning, dependingon the individual implementation and distribution of rights but only up to a mediumlevel. Coordination and communication issues, self-interest and complexity of such a form

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let the existence orientor be tied to the minimum level. Solely, the regard for others ishighly marked as both sides are highly specialized and know about important systemssurrounding them.

6.1.3 Clustering and Choosing the Best Suitable Form of IT Organization

Following the assessment of the overall necessity of orientor manifestation and the ones ofpossible strategic decisions as in the aforementioned example of the organizational formof the IT, the one which matches most should be chosen to provide the best possible con-ditions. Under these circumstances, the subsystems are able to self-organize in the bestway with a remaining freedom to act independently up to a certain degree. Resumingthe example of different plants in Subsection 6.1.1, the overall orientor manifestation ismost in line with the federal approach of IT organization. Admittedly, this does apply tothe majority of firms but there are also many examples, where an entirely centralized ordecentralized approach is the perfect fit.

If entirely different or even contradictory necessities arise from the different subsystems,such as countries, business processes, services, products, etc., it can be useful to clustersimilar ones in order to make the management within the system easier. What is more,depending on the decision that follows the assessment of the orientors, it is possible toprovide different best possible conditions for each cluster. This is only partly applicablefor the IT organization (where business units may become more autonomous than others),but when deciding upon make-or-buy or failure safety, these clusters can support the con-solidation of subsystems to an inner core, where security is of overriding importance andto a periphery, where freedom is major.

Through providing the best possible conditions for the system and its subsystems, unin-tended behavior, unwanted feedback loops and the risk of system archetypes damagingthe integrity of the AL can be mitigated.

6.2 Applying EA Principles to Guide in Subsystems

Having provided the best possible conditions for the subsystems to self-organize, whichis of course not a one-off event but an ongoing endeavor, is the first step to manage inALs. But subsystems still cannot be left alone as they – to a certain extent – need guidance,rules and control (Eusgeld, Nan, & Dietz, 2011; Johnson et al., 1964). As an example, Plsek(2001) mentions the establishment of rules of the following three kinds in order to guidein CAS which involve humans:

1. general direction pointing,

2. prohibitions,

3. resource or permission providing.

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For this to happen, applying EA principles to the AL and its subsystem is a suitable ap-proach but just one “element in a structured set of ideas that collectively define and guidethe organization, from values through to actions and results” (The Open Group, 2013). Theexample of EA principles is useful and intended to show the practicality of introduced ori-entors and SoS theory that can result in realizable cases of application. EA principles aredefined as “fundamental values that guide IT decision-making and activities [...] and arethe foundation for IT architecture, standards, and policy development” (NIHEA, 2013).The importance of EA principles in practice has been shown by Fischer, Aier, and Win-ter (2010) and most approaches describe the principles to be applied for the whole EA(Armour et al., 1999; The Open Group, 2013), even though the OMG states, that principlesmay be established within different domains and at different levels and thus in differentsegments. The aforementioned possibility to cluster subsystems which have a largely sim-ilar orientor manifestation suggests to also use different principles for different clustersto guide in the system AL. “Essentially, principles drive behavior” (Object ManagementGroup, 2011).

When introduced in an organization, the requirements on EA principles are numerous andinclude following five criteria which have to be met according to the OMG.

1. Understandability: easy to grasp and unambiguous language and content to mini-mize possible violations by individuals.

2. Robustness: sufficiently definitive and precise to support good quality decisionseven in controversial situations.

3. Completeness: every perceived situation in the organization has to be covered by adefined principle.

4. Consistency: a balance of interpretations is needed to retain flexibility as adherenceto one principle may impede another one. Thus, principles should not be contradic-tory.

5. Stability: principles need the ability to allow changes but only after ratification andotherwise show endurance.

It is not the goal to establish a full set of EA principles here as, in order to demonstrateapplicability, only exemplary rules suffice. Thus, the criteria of completeness has to be leftout here. As the following examples of principles are all taken from Object ManagementGroup (2011), they should be designed in accordance with introduced quality criteria. Butthe main reason for bringing in these five criteria is the requirement on consistency. Fullquote of the criteria says, that

strict adherence to one principle may require a loose interpretation of anotherprinciple. The set of principles must be expressed in a way that allows a balanceof interpretations. Principles should not be contradictory to the point whereadhering to one principle would violate the spirit of another. Every word in aprinciple statement should be carefully chosen to allow consistent yet flexibleinterpretation. (Object Management Group, 2011)

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Besides the requirement on completeness, it is probably this requirement on consistency,which is the most difficult task when introducing EA principles. In order to understand thedependencies and influences, a profound and rather holistic approach is necessary to notlose sight of possible side-effects or loops and to avoid the introduction of contradictorymeasures.

Therefore, an approach such as applying orientor theory as introduced and further devel-oped in this thesis is helpful to map EA principles to respective orientors and therewithidentify incompatible management activities. What is more, if contradictory principlesare inevitable for different subsystems according to their orientor manifestation, the afore-mentioned possibility of clustering subsystems with similar needs can be applied. Also,principles can be introduced at different levels, but before discussing these measures, thefollowing examples are outlined to understand the relationship between principles andmanifestations of respective orientors. Presented principles either focus on technology,applications, data, or business, but what they all have in common is effects on the AL.The titles as well as the respective statement in this choice of seven principles are directlytaken from Object Management Group (2011) as introduced in TOGAF 9.1. Based on therationale and the implications of these principles, a short summary is outlined to explainthe effects on different orientors and whether they are positive or negative. The coloredicons are used for visual aid: a blue icon implies positive effect of this principle, red standsfor negative effect, and gray means that there is no major effect on the orientor.

Business Continuity (Principle 1)Enterprise operations are maintained in spite of system interruptions.

In case of external events, such as hardware failure, data corruption or natural disas-ters, the enterprise with its businesses has to be able to continue operating. Reliabilityalso means producing the same output even when circumstances have changed which isimportant, as organizations are more dependent on IS as ever before. Measures to pro-vide business continuity include assessing criticality of each application, testing for vul-nerability, undertaking periodic reviews, installing redundancy, and developing recoveryplans.

Similarly to the outline of the capabilities of the AL (cf. Section 5.3), business continuity asan EA principle has a positive influence on security to cope with environmental variabil-ity. It also fosters the existence orientor as determining criticality, monitoring, and testingare important measures to assess, what the normal state is comprised of and to providecountermeasures in case of slight deviations. But as such measures often do, they hinderfreedom of the system. Effectiveness is generally also impeded as redundancy and othermeasures hinder the ability to secure scarce better, but when an incident happens, where

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business continuity is needed, effectiveness is maintained. Thus, effectiveness remainsgray in for this principle.

Common Use Applications (Principle 2)Development of applications used across the enterprise is preferred over the de-velopment of similar or duplicative applications which are only provided to aparticular organization.

While redundancy is a desired measure to ensure business continuity when thinking ofdata, it is commonly not when it comes to applications as it may lead to conflicting dataand increased costs due to duplicative capability. Subsystems should not develop appli-cations on their own if the whole system, or enterprise, may need or depend on the samefunctionality. Thus, costs can be decreased not only due to less conflicting data incidentsand standardized processes but also due to less (initial) development expenditures as wellas reduced costs of change.

Standardization is an important driver of effectiveness as less scarce resources are neededduring the whole application life cycle. The downside of these enterprise-wide capabili-ties is a decreased freedom within the system which results in less variety in products orprocesses offered to the customers. While a decentralized application management is usu-ally a good measure to realize and adapt to environmental change, standardization andthe common use of applications among several subsystems rather impedes the adaptivityorientor.

Compliance with Law (Principle 3)Enterprise information management processes comply with all relevant laws,policies, and regulations.

Abidance to national or international laws and regulations is essential to protect the orga-nization’s data from external as well as internal threats and to prevent loss of reputation incase of noncompliance. Such laws have to be transformed to rules within the organizationthat are often more rigorous than presented EA principles. Educating employees, provid-ing access to the established rules as well as monitoring and anticipating the introductionof new laws and regulations is an important management task.

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Concerning the orientors, compliance with law is first and foremost driving security and atthe same time reduces freedom as restrictions always narrow down choices. Business pro-cess improvements or effectiveness must not necessarily be lower than without complyingto laws but often, budgets that are spent on amendments due to regulations are lacking forthe increase in effectiveness. On the other hand, this principle fosters the regard to othersas it requires constant monitoring of which law amendments and new regulations mayeffect the system and which may trigger changes in the AL. While compliance with lawis necessary for the system to exist, it does not necessarily increase the awareness of thenormal state. Therefore, the existence orientor remains gray.

IT Responsibility (Principle 4)The IT organization is responsible for owning and implementing IT processes andinfrastructure that enable solutions to meet user-defined requirements for func-tionality, service levels, cost, and delivery timing.

A reliable and independent PPM has to be established to prioritize projects. The orga-nization should benefit from every project while the costs for each solution needs to bereasonable. Expectations, capabilities, and costs have to be aligned and expedient data,application, and technology models have to ensure the high quality of the outcome.

Choosing, scheduling, and monitoring projects ensures effectiveness and efficiency whichhas impact on the respective orientor dealing with securing scarce resources. At the sametime, the existence in the normal state is increased, as monitoring and control foster adeeper understanding and a well-elaborated PM offers action alternatives in case of inci-dents. However, focusing on internal effectiveness and efficiency may hinder the regardfor other systems.

Data is Shared (Principle 5)Users have access to the data necessary to perform their duties; therefore, data isshared across enterprise functions and organizations.

In order to increase the reliability and quality of decisions in the organization, access torelevant and up-to-date data has to be granted to the employees. This principle also hasstandardization as an important mean, just as the principle of common use of applicationsand contradicts to some parts the need for redundancy as in the first principle. A mutualset of procedures and guidelines has to be set up to govern data: from creating, integrating,maintaining, accessing, to deleting.

Having access to a commonly used knowledge base and being able to use reliable data in adistributed environment quickly, fosters the effectiveness of the organization. Knowledge

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of how co-workers do a process or what the last order of a customer was and process-ing available data, furthermore increases the regard towards such other systems. Whenconfronted with this sharing principle, it is important to closely watch security issues andthe existence in the normal state, as proliferation especially of confidential could harm thesystem severely.

Technology Independence (Principle 6)Applications are independent of specific technology choices and therefore can op-erate on a variety of technology platforms.

High prices, narrowed choice of extensions, and a disability to fulfill users’ or customers’requirements (because technology has become the driver of change) are the consequencesof dependence on few or just one vendor. Thus, technology independence has to be pro-vided by decoupling applications from specific software solutions and establishing inter-faces by respective subsystems. Introducing standards to ensure this independence mightbe required.

Allowing technologically independent solutions increases the freedom of the system as itcan cope with an increased variety of users and customers by offering a greater variety.Not being dependent on a few vendors also increases the adaptivity as appropriate re-sponses to environmental change can be found and implemented easier and faster. Butwhile relying on one, probably conclusive, concept of a single vendor may positively in-fluence security aspects, technology independence can have the downside of increasedsecurity issues as more technologies and a multitude of interfaces leads to the necessityof more security measures dealing with every single technology. In addition, technologyindependence lowers the satisfaction of the existence orientor as many technologies maylead to uncertainties of what the normal state is when individuality of information pro-cessing increases.

Responsive Change Management (Principle 7)Changes to the enterprise information environment are implemented in a timelymanner.

The importance of adapting to the environmental conditions which constantly change isunquestionable and has been discussed before in this thesis. Realizing the need to change

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and implement solutions in a timely manner requires processes that support these alter-ation while at the same time daily routines should not be interrupted. Transitions shalloccur swiftly and have minimal effects on the usual business.

Without doubt, this principle has a major impact on the adaptivity orientor of the system.In addition, responsive change management also increases the regard for others, as pro-cesses have to be introduced, which deal with observing the near environment to react tochange in the required timely manner. As additional resources are necessary to establishand run such processes, this principle decreases the effectiveness of the organization. Nor-mally, change processes initially lead to uncertainty and unexpected behavior within oroutside the system could result in a decrease of the security orientor.

To gain an overview of (possibly) introduced principles and their effects on orientors, and– the other way round – which orientor can be increased or decreased by which principle,an illustration with mappings can be useful as shown in Figure 6.2. Arrows from left toright depict positive influence of the principle on the orientor, while a possible decrease ofan orientor is indicated with an arrow drawn conversely.

What is introduced in Section 6.1.3 by clustering, the same holds true for the guidance inthe subsystem. Similarly, when subsystems have a quite similar orientor manifestation,they can be clustered and different principles can be applied to clusters with sometimeseven opposed orientor satisfaction and needs. This requires the ability to introduce guid-ance providing measures – such as introduced EA principles – on different levels. Previ-ously mentioned example of an inner core with processes with high security requirementscould herewith be decoupled from processes that require freedom to satisfy environmentalvariety. Instead of applying all principles to all subsystems and then allowing deviation orsoftening of rules through a time consuming and costly process, this approach may leadto more effective and easier solutions.

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Figure 6.2: Mapping EA Principles’ Influence on Orientors

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7 Summary, Critical Reflections, and FutureWork

Ever increasing demands due to globalization, technological advancements and a con-stantly evolving environment pose intricate challenges to today’s organizations. To ap-propriately respond to these defiances, organizations have to monitor their environment,avouch for restructuring initiatives, mount change processes in a timely manner, ascertainoperations during transition phases, and observe outcomes to elevate wealth of experiencefor future endeavors. While this holds true for every part of the organization, this thesisfocuses on ALs within the scope of EAs with its business IT alignment, due to the growingimportance of IS in organizations. Even though there exist many approaches and frame-works dealing with (re-) structuring ALs, all lay the focus on the rather static architecturewith lacking regard to behavioral aspects. Applying system theory and treating ALs in-cluding humans as a system, yields in the necessity to increase the understanding of thedynamics that may influence overall outcomes gravely and lead to overly unintended andunexpected results.

In contrast to an architecture, behavior cannot be designed but modeling dynamics of andin ALs has to be dared to understand and ideally predict behavior. Existing approaches tomodel dynamics such as CLDs, SFDs, and behavioral UML diagrams with an example ofan activity diagram are examined and outlined in this thesis focusing on the applicabilityto describe dynamics in ALs. Consequently, a green field approach of what is necessaryto holistically describe dynamics in this system is outlined. This leads to the assumption,that, before dealing with the detailed inside dynamics including applications and people,the influences of the environment on such a system have to be examined. An allegedly ap-propriate measure to do so is the approach of Bossel, whose orientor theory is applied andfurther developed to be used to describe external influences and their interplay affectingALs. Subsequently, introduced theories of a SoS and CAS ask for breaking down the ALinto its subsystems for two reasons. Firstly, in order to explain the behavior of the individ-ual parts of the system which are all susceptible to the same categories of environmentalinfluence and secondly, to deduce the inability to manage a system from the outside.

To allocate a counterdraft how management within the AL can be successful a twofold ap-proach is presented which applies the introduced orientor model. At first, the best possibleconditions for the subsystems to self-organize have to be assessed and provided which isenriched by an example of choosing the right form of IT organization. Thereupon, guid-ance has to be provided with attention to dependencies and the interplay of orientors.This is explained through an example using EA principles and the possibility of clusteringsimilar subsystem.

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7 Summary, Critical Reflections, and Future Work

Taking up the research questions of Section 1.3, following section outlines the findingsregarding each question and concludes with a critical reflection. Finally, delineation of anoutlook with future work is given in Section 7.2.

7.1 Recapitulation of Raised Research Questions and CriticalReflection

Three research questions accompanied the search for a suitable approach to model ALs asdynamic systems. While answers to these questions can be found alongside this thesis ina more profound way, the following answers only depict a short summary omitting manypreviously outlined details.

RQ 1: Does understanding the application landscape’s dynamics increase its manageability?

As already announced, no evaluation with practitioners is conducted within this thesis topossibly lay the groundwork for further research. Thus, neither a quantitative answer canbe given nor a definite yes or no. Several means and models have been examined on theircapabilities to model dynamics in an AL (cf. Question 3) before the urgent necessity to un-derstand such a system’s environment arose. Understanding the application landscapes’dynamics means understanding the behavior of the subsystem as well as the behavior ofthe system in its environment and the transition from environmental influences through tothe subsystems. Regarding the inside dynamics of a system like the AL, indicated modelsand diagrams such as CLDs can back manageability as qualitative effects of measures canbe explained in hindsight or foreseen in an easier fashion. Especially in combination withSFDs, such as the Navy example, these detailed approaches are a powerful tool to supportmanagement. But especially the importance of the environmental influencing factors hasbeen described in this thesis and through the further development of the orientor theorywith classification of environmental influences, a generic model is introduced, which isapplicable to explain behavioral aspects and to prevent from losing sight of the externalaspects affecting the system with certainty. Apart from explaining behavior and as de-scribed in Chapter 6, management decisions can be supported with the help of orientors,regarding, for example, the choice of the IT organization or the allocation of EA princi-ples.

RQ 2: Where and to which degree is the application landscape’s behavior influenceable?

In general, behavior is always influenceable from inside and outside the system but theresult is often uncertain. Deliberately influencing behavior of the AL to achieve a certaingoal is a difficult endeavor, especially when undertaken from one instance which is incharge of steering the system as a whole. There are far too many other influencing factorsas well as eigendynamics driving the behavior of and inside the system AL. This leadsto a highly dynamic setting, where the outcome of applied means are hardly predictableas dynamics belie expectations which are reduced to simple if/then rules. Recapitulating

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7.1 Recapitulation of Raised Research Questions and Critical Reflection

the CAS approach which is applicable to ALs, non-linearity, self-organization and non-equilibrium ask for different approaches. By treating the AL as a SoS and providing thebest possible conditions for the subsystems to self-organize as well as guiding through theapplication of measures like rules or principles, behavior cannot be controlled preciselybut undesired behavior can be impeded. Installing capabilities or introducing principlesto direct the behavior of a subsystem towards one orientor like security is possible, but ithas to be taken heed of side-effects and downsides of every measure. But the further asystem gets broken down into its subsystems, the deeper the understanding of possibleresults will be.

RQ 3: Which means are applicable to model dynamics of and within application landscapes?

Different means are introduced in this thesis which model dynamics or claim to describebehavior, from very detailed ones to those with a broader focus. Depending on the purposeof the model, every approach has its right to exist but especially when outside influencesand people – as part of the application landscape’s dynamics – and their behavior have tobe modeled, many diagrams fail to depict the entirety of dynamics aspects. While behaviorresulting from foregone activity as well as few exceptions interrupting causal chains canbe modeled in UML activity diagrams, it is not applicable to describe further aspects ofenvironmental influences or to explain quantities and anything that exceeds simple activ-ities. As an example, exhaustion of employees due to order acceptance or change requestsdue to increased variety of competitors is not presentable. At the bottom of the scale re-garding abstractness, delineated CLDs and SFDs are suitable means to model dynamicsqualitatively and quantitatively and every influencing factor of the system’s environmentcan be modeled as well. These models can incorporate important aspects of behavior suchas feedback loops and even predict results such as in system archetypes. Even for thewhole AL, these diagrams are applicable but not quite suitable as their scope is usuallyvery special and narrow as in the example explaining project delays due to of change re-quests in the Navy example which took years and thousands of equations. Taking intoconsideration their enormous amount of time and their susceptibility to errors by omittingimportant effects, a cost-benefit analysis might often argue against the installation of suchmodels. The most abstract approach to explain system behavior is embodied by outlinedand applied orientor theory. Here, it is applicable as well as suitable to depict the whole ALin one model. Especially in the further development with a focus on ALs, peoples’ behav-ior as well as feedback loops and other dynamics phenomena are not omitted but reducedto the effects they have on the respective subsystem. The downside of this approach is,that it is stuck for an answer on why things happen, which is an advantage of CLDs. All inall and up to the present day, there are no means to model dynamics of ALs in its entiretyfor a reasonable amount of effort. But when knowing the scope of such a model, either themore detailed or more general approach can be chosen according to the needs. But one hasto be aware that for CLDs, which might become immense, simulation is necessary whileapplying orientor theory requires assessments which are often subjective.

Evidence concerning the findings on manageability is missing due to non-execution ofevaluation. But before thoroughly assessing the importance of behavioral aspects by prac-titioners, description of existing models, deduction of what behavior is comprised of in

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ALs and system theory was necessary. But especially the further development of the ori-entor approach provides a first step towards an increased understanding of environmen-tal influences on the system’s behavior and a starting point to determine necessary systemcapabilities, to provide the best possible conditions, and to realize guidance in a highly dy-namic system, the AL. Starting point indicates, that there are demands to further developthe modeling of dynamics and to answer subsequently outlined questions which emergedduring this thesis.

7.2 Further Development and the Outlook on Future Work

Refine orientor theory One field for further research is to examine and refine the hereinintroduced and further developed orientor theory to the specifics of ALs. Before anyevaluation can be conducted, open questions have to be answered first. In order toprovide a starting point, the orientor theory of Bossel (1994) is used proceeded on theassumption that his work – which is continued by indicators mapping on orientors,concrete simulation processes, and evaluation which indicates thoroughness – andthe description of six orientors dealing with six environmental influences is useful.However, more profound examination of the theory might be necessary and the wayof applying it to systems in today’s world might be different, as the environmentchanged in comparison to almost twenty years ago. As outlined at different passagesin this thesis, adaptation to environmental change is vital and consequently, suchmodels also should be reviewed and altered if necessary to remain viable. Therefore,elimination of the possibility, that there exist less or more orientors or a change of theinterplay should never be done.

For the assessment of the single orientors in a system and its subsystems, the de-termination of the manifestation as well as aggregation to the suprasystem can befurther developed. Here, four levels of orientor manifestation are introduced at a or-dinal scale, from an insufficient to a high satisfaction. By assessing indicators whichfoster orientors as accurately and complete as possible and determination of the ca-pabilities and underlying applicable quantitative commensurability such as maturitymeasures or certificates, a more precise scale could be applied to assess a more ob-jective orientor manifestation and enable reliable calculations.

Use orientor theory Finding concrete other cases of application for orientor theory thanassessment of the respective manifestation, determining capabilities, choosing themost suitable form of IT organization or assessing EA principles is another importanttask that can be conducted following this thesis. But not only finding new cases ofapplication is an interesting aspect, but also to further develop an existing one suchas applying EA principles. By using real examples from practice, the dependencies,interplays, and exclusions of different principles can be determined. Equally, such anexample can be followed up by changing rules, such as introducing them to differ-ent subsystems on different levels for the purpose of eliminating mutually exclusiverules and decreasing exception regulations.

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7.2 Further Development and the Outlook on Future Work

Through evaluation of presented theory with practitioners, the understandability ofthe environmental influences and orientors has to be determined and descriptionsmight have to be altered correspondingly. Another major task that ensues this thesisis following up the idea of orientors and indicators in the system AL and its subsys-tem and develop exemplary models and conduct path analysis and actual simula-tion, which is most likely not the first follow-up work of this thesis.

Examine divisibility There exist many works on the SoS approach, also in combinationwith EAs but naturally not with a focal point on behavior in the context of orien-tor theory. Examples on the divisibility of the system AL into subsystems are used,such as different plants or business units of an organization. Disclosing an univer-sally applicable procedure for every type and size of organization is not feasible, butgetting across the trade-offs to decision makers and providing recommendations onhow dividing the system into subsystems can be done under certain circumstancesis achievable. Ensuing the question of how to break down such a system like theAL, examining the most qualified depth of a segmentation is equally important. Thefurther a system is unraveled, the more reliable the information is, for example con-cerning orientor manifestation, but at the same time the more effort has to be spent.Assessing the right level of system fragmentation into subsystems is not only im-portant for determination of requirements but for applying rules and controls alike.Also, aforementioned possibility to separate subsystems with similar orientor satis-faction and apply different rules – such as EA principles – according to the differentnecessities should be studied in future works in respect to its applicability.

Figuring out the right degree of abstraction by combining general management the-ory with system theory to apply orientor theory at the right level between controland self-organization can be revealing in view of coping with systems’ behavior.

Combine existing models Developing an all-embracing, complete, and accurate modelto explain behavior in its entirety even in a narrowed scope like an AL is impossible,especially when humans are part of the system. Depending on the purpose of such amodel, introduced approaches offer different increments of describable details. Fora high degree of abstraction with a focus on environmental influences, orientor the-ory is the means of choice, for a low degree of abstraction focusing on reasons forinside dynamics, CLDs and SFDs are favored. Despite that there cannot exist theall-embracing model, different nuances by combination of both extremes can be ex-amined and tested for respective cases of application. Either, behavior caused byorientors adjusting to external influences could be added to a CLD to introduce en-vironmental influences to a detailed case of application or an important and specialcase of dynamics could be introduced to the more general orientor model. Whetherthese possibilities are applicable or not has to be found out, bearing in mind that thehigher the share of CLD elements, the more time-consuming and costly it will get.

Broaden scope Applied theories including system theory, SoS, CAS, orientor theory, andmodeling all originate from basic works describing general theories even thoughthey have their roots in particular disciplines such as biology. Thus, none of thesetheories are designed for the specifics of ALs but through the inherent property ofequity of systems, adaptation to the system AL was possible. In future works, the

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scope can be increased by describing a more extensive system, namely the suprasys-tem of the AL, the EA. Even though the focus of this thesis is the AL, a few aspectsin this thesis already indicated the applicability for this broader scope, such as ex-amination of EA principles and their effect on orientor manifestation. Using intro-duced theories for the whole EA requires more resources but may result in resilientassertions and provide a more holistic view on the dynamics of and within the EAincluding its environmental influences.

Understand human behavior Whenever people are part of a system, such as in the hereinexamined ALs, modeling their behavior to determine dynamics is a venturous en-terprise. Man has to be treated as an individual as power relations, survival, self-interest, group-interest, value systems, culture, etc. are all part of decision makingand behavior is not always guided by logic (Kandjani et al., 2013, p. 3866). Thus,the ultimate precept of Bertalanffy (1984) serves as a final statement on individualityand variety of humans in these systems facilitating long-term viability.

We may, however, conceive of a scientific understanding of human societyand its laws in a somewhat different and more modest way. Such knowl-edge can teach us not only what human behavior and society have in com-mon with other organizations, but also what is their uniqueness. Here themain tenet will be: Man is not only a political animal; he is, before andabove all, an individual. The real values of humanity are not those whichit shares with biological entities, the function of an organism or a commu-nity of animals, but those which stem from the individual mind. Humansociety is not a community of ants or termites, governed by inherited in-stinct and controlled by the laws of the superordinate whole; it is basedupon the achievements of the individual and is doomed if the individualis made a cog in the social machine. This, I believe, is the ultimate pre-cept a theory of organization can give: not a manual for dictators of anydenomination more efficiently to subjugate human beings by the scientificapplication of Iron Laws, but a warning that the Leviathan of organizationmust not swallow the individual without sealing its own inevitable doom.(Bertalanffy, 1984, pp. 52f.)

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References

Altieri, M. A. (1999). The ecological role of biodiversity in agroecosystems. Agriculture,Ecosystems & Environment, 74(1), 19–31.

Armour, F. J., & Kaisler, S. H. (2001). Enterprise architecture: Agile transition and imple-mentation. IT professional, 3(6), 30–37.

Armour, F. J., Kaisler, S. H., & Liu, S. Y. (1999). A big-picture look at enterprise architec-tures. IT professional, 1(1), 35–42.

Ashby, W. R. (1956). An introduction to cybernetics. Chapman & Hall Ltd. And UniversityPaperbacks.

Assmann, M., & Engels, G. (2008). Transition to service-oriented enterprise architecture.In Software architecture (p. 346–349). Springer.

Basel Committee. (2010). Basel III: a global regulatory framework for more resilient banksand banking systems. Basel Committee on Banking Supervision, Basel.

Baumeister, R. (2003). The psychology of irrationality: Why people make foolish, self-defeating choices. The psychology of economics decisions, 1.

Beer, S. (1984, January). The viable system model: Its provenance, development, method-ology and pathology. The Journal of the Operational Research Society, 35(1), 7. doi:10.2307/2581927

Bernus, P., Nemes, L., & Schmidt, G. (2003). Handbook on enterprise architecture. Springer.

Bertalanffy, L. v. (1984). General system theory. New York: Braziller.

Bieberstein, N., Bose, S., Walker, L., & Lynch, A. (2005). Impact of service-oriented architec-ture on enterprise systems, organizational structures, and individuals. IBM systemsjournal, 44(4), 691–708.

Boardman, J., & Sauser, B. (2006). System of systems-the meaning of of. In IEEE/SMCinternational conference on system of systems engineering (SoSE 2006) (p. 6–pp).

Bossel, H. (1994). Modeling and simulation. AK Peters Wellesley, MA.

Boulding, K. E. (1956). General systems theory—the skeleton of science. Managementscience, 2(3), 197–208.

99

References

Breu, R. (2010). Ten principles for living models-a manifesto of change-driven softwareengineering. In International conference on complex, intelligent and software intensivesystems (CISIS 2010) (p. 1–8).

Brown, C. V. (1999). Horizontal mechanisms under differing IS organization contexts. MISQuarterly, 23(3), 421–454.

Brown, C. V., & Magill, S. L. (1998, March). Reconceptualizing the context-design issue forthe information systems function. Organization Science, 9(2), 176–194.

Broy, M. (2012). Software engineering, lecture script. Munchen.

Brucker, P., Drexl, A., Mohring, R., Neumann, K., & Pesch, E. (1999). Resource-constrainedproject scheduling: Notation, classification, models, and methods. European Journalof Operational Research, 112(1), 3–41.

Bubak, O. (2006). Composing a course book for system and enterprise architecture educa-tion. In IEEE/SMC international conference on system of systems engineering (SoSE 2006)(p. 6–pp).

Buckl, S. (2011). Developing organization-specific enterprise architecture management functionsusing a method base. Unpublished doctoral dissertation.

Buckl, S. (2012). Strategisches IT management & enterprise architecture management.

Buckl, S., Dierl, T., Matthes, F., & Schweda, C. M. (2010). Building blocks for enterprisearchitecture management solutions. In Practice-driven research on enterprise transfor-mation (p. 17–46). Springer.

Buckl, S., Ernst, A. M., Matthes, F., & Schweda, C. M. (2009a). An information modelfor managed application landscape evolution. Journal of Enterprise Architecture (JEA),5(1), 12–26.

Buckl, S., Ernst, A. M., Matthes, F., & Schweda, C. M. (2009b). Visual roadmaps formanaged enterprise architecture evolution. In 10th ACIS international conference onsoftware engineering, artificial intelligences, networking and Parallel/Distributed comput-ing (SNPD 2009) (p. 352–357).

Buckl, S., Matthes, F., & Schweda, C. M. (2009). A viable system perspective on enter-prise architecture management. In IEEE international conference on systems, man andcybernetics (SMC 2009) (p. 1483–1488).

Buckl, S., Matthes, F., & Schweda, C. M. (2010). Future research topics in enterprise ar-chitecture management–a knowledge management perspective. In Service-orientedcomputing. ICSOC/ServiceWave 2009 workshops (p. 1–11).

Buckley. (2009). Systems research for behavioral science. Transaction Publishers.

Burt, J. (2011). BYOD trend pressures corporate networks. eweek, 28(14), 30–31.

100

References

Castells, M. (2011). The power of identity: the information age: economy, society, and culture(Vol. 2). Wiley-Blackwell.

CEIOPS. (2010, March). Level 3 guidance on solvency II: pre-application process forinternal models. Retrieved 2013-12-09, from https://eiopa.europa.eu/fileadmin/tx dam/files/consultations/consultationpapers/CP80/CEIOPS-DOC-76-10-Guidance-pre-application-internal-models.pdf

Child, J. (1972). Organizational structure, environment and performance: the role of strate-gic choice. Sociology, 6(1), 1–22.

Choi, T. Y., Dooley, K. J., & Rungtusanatham, M. (2001). Supply networks and complexadaptive systems: control versus emergence. Journal of operations management, 19(3),351–366.

Christensen, C. M. (1997). The innovator’s dilemma: when new technologies cause great firms tofail. Harvard Business Press.

Christensen, C. M., & Overdorf, M. (2000). Meeting the challenge of disruptive change.harvard business review, 78(2), 66–77.

Commoner, B. (1996). The closing circle: nature, man, and technology. In Thinking aboutthe environment: Readings on politics, property, and the physical world (pp. 161–pp). NewYork: M.E. Sharpe.

Costanza, R., Wainger, L., Folke, C., & Maler, K.-G. (1993). Modeling complex ecologicaleconomic systems. BioScience, 43(8), 545–555.

Darwin, C. (1859). On the origins of species by means of natural selection.

De Haes, S., & Van Grembergen, W. (2004). IT governance and its mechanisms. InformationSystems Control Journal, 1, 27–33.

Deming, W. E. (1986). Out of the crisis. cambridge, MA: massachusetts institute of tech-nology. Center for Advanced Engineering Study, 6.

Dictionary.com. (2013, May). System (10th ed.). Retrieved from http://dictionary.reference.com/browse/system

Dijkstra, E. W. (1971). Hierarchical ordering of sequential processes. Acta informatica, 1(2),115–138.

Donoghue, A. (2009, November). Lightning takes down amazon cloud | ZDNet. Retrieved2013-08-16, from http://www.zdnet.com/lightning-takes-down-amazon-cloud-3039662584/

Dooley, K. J. (1997). A complex adaptive systems model of organization change. Nonlineardynamics, psychology, and life sciences, 1(1), 69–97.

101

https://eiopa.europa.eu/fileadmin/tx_dam/files/consultations/consultationpapers/CP80/CEIOPS-DOC-76-10-Guidance-pre-application-internal-models.pdf



http://dictionary.reference.com/browse/system

http://dictionary.reference.com/browse/system

http://www.zdnet.com/lightning-takes-down-amazon-cloud-3039662584/

http://www.zdnet.com/lightning-takes-down-amazon-cloud-3039662584/

References

Dorner, D. (1995). Logik des Misslingens. Springer.

Dorner, D., & Schaub, H. (2002). Die Simulation von Gefuhlen. Psychologisches Institut derUniversitat Bamberg, 8.

Dullinger, S., Essl, F., Rabitsch, W., Erb, K.-H., Gingrich, S., Haberl, H., . . . Hulme, P. E.(2013, April). Europe’s other debt crisis caused by the long legacy of future ex-tinctions. Proceedings of the National Academy of Sciences, 110(18). doi: 10.1073/pnas.1216303110

Ernst, A. M., & Schneider, A. W. (2010). Roadmaps for enterprise architecture evolution. In2nd european workshop on patterns for enterprise architecture management (PEAM 2010).Paderborn, Germany.

European Commission. (2013, July). Future rules (solvency II and omnibusII). Retrieved 2013-12-09, from http://ec.europa.eu/internal market/insurance/solvency/future/index en.htm

Eusgeld, I., Nan, C., & Dietz, S. (2011). “System-of-systems” approach for interdependentcritical infrastructures. Reliability Engineering & System Safety, 96(6), 679–686.

Farwick, M., Agreiter, B., Breu, R., Haring, M., Voges, K., & Hanschke, I. (2010). To-wards living landscape models: Automated integration of infrastructure cloud inenterprise architecture management. In IEEE 3rd international conference on cloud com-puting (CLOUD 2010) (p. 35–42).

Fischer, Aier, & Winter. (2007). A federated approach to enterprise architecture modelmaintenance. Enterprise Modelling and Information Systems Architectures, 2(2), 14–22.

Fischer, Aier, & Winter. (2010). What is an enterprise architecture principle? In Computerand information science 2010 (p. 193–205). Springer.

Flood, R. L. (1993). Dealing with complexity: an introduction to the theory and application ofsystems science. Springer.

Forrester, J. W. (1969). Principles of systems: text and workbook chapters 1 through 10. Wright-Allen Press.

Forrester, J. W. (1971). Counterintuitive behavior of social systems. Theory and Decision,2(2), 109–140.

Forrester, J. W. (1994). System dynamics, systems thinking, and soft OR. System DynamicsReview, 10(2-3), 245–256.

Forrester, J. W., & Senge, P. M. (1979). Tests for building confidence in system dynamics models.System Dynamics Group, Sloan School of Management, Massachusetts Institute ofTechnology.

Gabler Verlag. (2013, May). System (10th ed.). Gabler. Retrieved from http://

102

http://ec.europa.eu/internal_market/insurance/solvency/future/index_en.htm

http://ec.europa.eu/internal_market/insurance/solvency/future/index_en.htm

http://wirtschaftslexikon.gabler.de/Archiv/3210/system-v10.html


References

wirtschaftslexikon.gabler.de/Archiv/3210/system-v10.html

Gell-Mann, M. (1995). What is complexity? remarks on simplicity and complexity by thenobel prize-winning author of the quark and the jaguar. Complexity, 1(1), 16–19.

Goldstein, J. (1994). The unshackled organization: facing the challenge of unpredictability throughspontaneous reorganization. Productivity Press.

Golinelli, G. M., Pastore, A., Gatti, M., Massaroni, E., & Vagnani, G. (2011). The firm as aviable system: managing inter-organisational relationships. Sinergie rivista di studi ericerche(58).

Graham, A. K. (1977). Principles on the relationship between structure and behavior of dynamicsystems. Unpublished doctoral dissertation, Massachusetts Institute of Technology.

Gray, W., Duhl, F. J., & Rizzo, N. D. (1969). General systems theory and psychiatry. Little,Brown.

Guizzardi, G. (2005). Ontological foundations for structural conceptual models. CTIT, Centrefor Telematics and Information Technology.

Haerle, P., Lueders, E., Pepanides, T., Pfetsch, S., Poppensieker, T., & Stegemann, U. (2010).Basel III and european banking: Its impact, how banks might respond, and the challenges ofimplementation. McKinsey&Company.

Hanschke, I., Giesinger, G., & Goetze, D. (2013). Business-analyse - einfach und effektiv:Geschaftsanforderungen verstehen und in IT-Losungen umsetzen. Munchen: Hanser.

Hardin, G. (1963). A second sermon on the mount. Perspectives in Biology and Medicine, 6,366.

Hardin, G. (2009). The tragedy of the commons. Journal of Natural Resources Policy Research,1(3), 243–253.

Harel, D., & Rumpe, B. (2000). Modeling languages: syntax, semantics and all that stuff.

Helbig, J. (2012). Strategisches IT-Management und Enterprise Architecture Management.

Hess, A., Humm, B., Voss, M., & Engels, G. (2007). Structuring software cities a multidi-mensional approach. In 11th IEEE international enterprise distributed object computingconference (EDOC 2007) (p. 122–122).

Hevner, A. R. (2007). The three cycle view of design science research. Scandinavian Journalof Information Systems, 19(2), 87.

Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in informationsystems research. MIS quarterly, 28(1), 75–105.

Hoffer, L. D., Bobashev, G., & Morris, R. J. (2009). Researching a local heroin market as acomplex adaptive system. American journal of community psychology, 44(3-4), 273–286.

103



References

Holland, J. H. (1992). Complex adaptive systems. Daedalus, 121, 1.

Holland, J. H. (1995). Hidden order: how adaptation builds complexity. Basic Books.

Holland, J. H. (2006). Studying complex adaptive systems. Journal of Systems Science andComplexity, 19(1), 1–8.

Holland, J. H., & Miller, J. H. (1991). Artificial adaptive agents in economic theory. TheAmerican Economic Review, 81(2), 365–370.

Holling, C. S. (1992, December). Cross-scale morphology, geometry, and dynamics ofecosystems. Ecological Monographs, 62(4), 447–502. doi: 10.2307/2937313

Hornby, A. S. (2000). Oxford advanced learner’s dictionary - 6th edition (6. A. ed.). OxfordUniv. Press (OELT).

IEEE, & ISO/IEC. (2007, July). Standard for systems and software engineering - recom-mended practice for architectural description of software-intensive systems. ISO/IEC42010 IEEE Std 1471-2000, c1–24. doi: 10.1109/ieeestd.2000.91944

Iyer, B., & Gottlieb, R. (2004). The four-domain architecture: An approach to supportenterprise architecture design. IBM SYSTEMS JOURNAL, 43(3), 588.

Janssen, M., & Kuk, G. (2006). A complex adaptive system perspective of enterprise archi-tecture in electronic government. IEEE Computer Society. doi: 10.1109/hicss.2006.6

Johnson, R. A., Kast, F. E., & Rosenzweig, J. E. (1964). Systems theory and management.Management Science, 10(2), 367–384.

Kaisler, S. H., Armour, F., & Valivullah, M. (2005). Enterprise architecting: Critical prob-lems. In Proceedings of the 38th annual hawaii international conference on system sciences(HICSS 2005) (p. 224b–224b).

Kandjani, H., Bernus, P., & Nielsen, S. (2013). Enterprise architecture cybernetics andthe edge of chaos: Sustaining enterprises as complex systems in complex businessenvironments. In 46th hawaii international conference on system sciences (HICSS 2013)(p. 3858–3867).

Karagiannis, D., & Kuhn, H. (2002). Metamodelling platforms. In Proceedings of the third in-ternational conference EC-Web (2002) (p. 182–182). Aix-en-Provence, France: Springer-Verlag.

Kauffman, S. (1996). At home in the universe: the search for the laws of self-organization andcomplexity. Oxford University Press.

Kent, S. (2002). Model driven engineering. In Integrated formal methods (p. 286–298).

Kersten, W. (2002). Vielfaltsmanagement: Integrative Losungsansatze zur Optimierung undBeherrschung der Produkte und Teilevielfalt. TCW Transfer-Centrum.

104

References

Knopper, S. (2009). Appetite for self-destruction: the spectacular crash of the record industry inthe digital age. Simon and Schuster.

Kramer, J., & Magee, J. (1990). The evolving philosophers problem: Dynamic changemanagement. Software Engineering, IEEE Transactions on, 16(11), 1293–1306.

Krcmar, H. (2010). Informationsmanagement. Berlin; Heidelberg: Springer.

Krogstie, J. (2002). A semiotic approach to quality in requirements specifications. Proceed-ings of the IFIP TC8/WG8, 1, 231–249.

Langdon, C. S., & Sikora, R. T. (2006). Conceptualizing co-ordination and competition insupply chains as complex adaptive system. Information Systems and e-Business Man-agement, 4(1), 71–81.

Lankhorst, M. (2005). Enterprise architecture at work: modelling, communication and analysis.Springer.

Lansing, J. S. (2003). Complex adaptive systems. Annual review of anthropology, 32, 183–204.

Levin, S. A. (1998). Ecosystems and the biosphere as complex adaptive systems. Ecosys-tems, 1(5), 431–436.

Liddell, H. G., & Scott, R. (2013, May). System. Retrieved from http://www.perseus.tufts.edu/hopper/text?doc=Perseus:text:1999.04.0057:entry%3Dsu/sthma

Liu, J. Y.-C., Chen, V. J., Chan, C.-L., & Lie, T. (2008, August). The impact of software pro-cess standardization on software flexibility and project management performance:Control theory perspective. Information and Software Technology, 50(9–10), 889–896.doi: 10.1016/j.infsof.2008.01.002

Lucke, C., Krell, S., & Lechner, U. (2010, August). Critical issues in enterprise architecting– a literature review. In Proceedings of the americas conference on information systems(AMCIS 2010).

Luenberger, D. (1979). Introduction to dynamic systems: theory, models, and applications.

Luftman, J. (2003). Competing in the information age : align in the sand. Oxford UniversityPress, USA.

Maier, M. W. (1998). Architecting principles for systems-of-systems. Systems Engineering,1(4), 267–284.

Matthes, F. (2013). IT Management mit Unternehmensarchitektur. Retrieved 2013-12-09, from http://www.in.tum.de/forschung/forschungs-highlights/it-management-mit-unternehmensarchitektur.html

Matthes, F., Neubert, C., & Steinhoff, A. (2011). Hybrid wikis: Empowering users to

105

http://www.perseus.tufts.edu/hopper/text?doc=Perseus:text:1999.04.0057:entry%3Dsu/sthma



http://www.in.tum.de/forschung/forschungs-highlights/it-management-mit-unternehmensarchitektur.html

http://www.in.tum.de/forschung/forschungs-highlights/it-management-mit-unternehmensarchitektur.html

References

collaboratively structure information. In 7th international joint conference on softwaretechnologies (ICSOFT 2011) (p. 250–259).

Micklin, P. (2007). The aral sea disaster. Annual Review of Earth and Planetary Sciences, 35,47–72.

Mykhashchuk, M., Buckl, S., Dierl, T., & Schweda, C. M. (2011). Charting the landscape ofenterprise architecture management. In Wirtschaftsinformatik (p. 83).

Nidiffer, K. E., & Dolan, D. (2005). Evolving distributed project management. Software,IEEE, 22(5), 63–72.

NIHEA. (2013, August). NIH enterprise architecture - principles. Retrieved2013-08-28, from https://enterprisearchitecture.nih.gov/Pages/NIHEAPrinciples.aspx

Nonaka, I. (1988). Creating organizational order out of chaos: self-renewal in japanesefirms. California Management Review, 30(3), 57–73.

Object Management Group (Ed.). (2011, August). OMG unified modeling language (OMGUML), superstructure specification (version 2.4.1). Retrieved 2013-09-12, from http://www.omg.org/spec/UML/2.4.1/

OLeary, D. E. (1998). Enterprise knowledge management. Computer, 31(3), 54–61.

Oppenheimer, D., Ganapathi, A., & Patterson, D. A. (2003). Why do internet services fail,and what can be done about it? In USENIX symposium on internet technologies andsystems (Vol. 67).

Pascale, R. T. (1999). Surfing the edge of chaos. Sloan management review, 40(3), 83–94.

Pascual, R., Meruane, V., & Rey, P. A. (2008). On the effect of downtime costs and budgetconstraint on preventive and replacement policies. Reliability Engineering & SystemSafety, 93(1), 144–151.

Peterson, R. R. (2004). Integration strategies and tactics for information technology gover-nance. In Strategies for information technology governance (p. 37–80). Idea Group.

Pidwirny, M. (2006). Definitions of system and models (2nd ed.). Retrieved from http://www.physicalgeography.net/fundamentals/4b.html

Plsek, P. (2001). Redesigning health care with insights from the science of complex adap-tive systems. In Crossing the quality chasm: A new health system for the 21st century(p. 309–322).

Porter, M. E. (2008). Competitive advantage: creating and sustaining superior performance.Simon and Schuster.

Raff, M. (1998). Cell suicide for beginners. Nature, 396(6707), 119–119.

106

https://enterprisearchitecture.nih.gov/Pages/NIHEAPrinciples.aspx

https://enterprisearchitecture.nih.gov/Pages/NIHEAPrinciples.aspx

http://www.omg.org/spec/UML/2.4.1/

http://www.omg.org/spec/UML/2.4.1/

http://www.physicalgeography.net/fundamentals/4b.html

http://www.physicalgeography.net/fundamentals/4b.html

References

Rockart, J. F., Earl, M. J., & Ross, J. W. (1996). Eight imperatives for the new IT organization.Sloan management review, 38(1), 43–55.

Ross, J. W., Weill, P., & Robertson, D. C. (2006). Enterprise architecture as strategy: creating afoundation for business execution. Harvard Business Press.

Rouhani, B. D., & Nikpay, F. (2012). Agent-oriented enterprise architecture: new approachfor enterprise architecture. International Journal of Computer Science Issues (IJCSI), 9(6).

Sadler, P., & Barry, B. (1970). Organisational development: case studies in the printing industry.Longmans London.

Sage, A. P., & Cuppan, C. D. (2001). On the systems engineering and management ofsystems of systems and federations of systems. Information, Knowledge, Systems Man-agement, 2(4), 325–345.

Sambamurthy, V., & Zmud, R. W. (1999). Arrangements for information technology gov-ernance: a theory of multiple contingencies. MIS quarterly, 23(2), 261–290.

Schekkerman, J. (2005). The economics of enterprise architecture: how to quantify and managethe economic value of enterprise architecture. Victoria: Trafford.

Senge, P. (1990). The fifth discipline: The art and science of the learning organization. New York:Currency Doubleday.

Stachowiak, H. (1973). Allgemeine Modelltheorie. Springer-Verlag, Wien.

Sterman, J. (1994). Learning in and about complex systems. System Dynamics Review,10(2-3), 291–330.

Sterman, J. (2000). Business dynamics: systems thinking and modeling for a complex world.Irwin-McGraw-Hill.

Sutherland, J., & van den Heuvel, W.-J. (2002). Enterprise application integration andcomplex adaptive systems. Communications of the ACM, 45(10), 59–64.

System Dynamics Society. (2013, September). What is system dynamics. Retrieved 2013-09-10, from http://www.systemdynamics.org/what-is-s/

Tarafdar, M., & Gordon, S. R. (2004). How IT makes organizations more innovative: Theinfluence of IT capabilities on organizational innovation.

The Open Group. (2013, August). TOGAF® 9.1. Retrieved 2013-08-28, from http://pubs.opengroup.org/architecture/togaf9-doc/arch/

Ulrich, D., & Lake, D. (1991). Organizational capability: creating competitive advantage.The Executive, 5(1), 77–92.

Urban, M., Phillips, B., Skelly, D., & Shine, R. (2008, March). A toad more traveled: Theheterogeneous invasion dynamics of cane toads in australia. The American Naturalist,

107

http://www.systemdynamics.org/what-is-s/

http://pubs.opengroup.org/architecture/togaf9-doc/arch/

http://pubs.opengroup.org/architecture/togaf9-doc/arch/

References

171(3), E134–E148. doi: 10.1086/527494

Von Solms, B., & Von Solms, R. (2004). The 10 deadly sins of information security manage-ment. Computers & Security, 23(5), 371–376.

Wang, C. L., & Ahmed, P. K. (2007). Dynamic capabilities: A review and research agenda.International Journal of Management Reviews, 9(1), 31–51.

Warkentin, M., & Johnston, A. C. (2007). IT governance and organizational design forsecurity management. Advances in Management Information Systems, 11, 46–68.

Webster, J., & Watson, R. (2002). Analyzing the past to prepare for the future: Writing aliterature review. MIS Quarterly, 26(2), 13–23.

Weill, P. (2004). Don’t just lead, govern: How top-performing firms govern IT. MISQuarterly Executive, 3(1), 1–17.

Weill, P., & Ross, J. W. (2004). IT governance on one page. CISR WP, 349(2).

Winter, & Fischer. (2007). Essential layers, artifacts, and dependencies of enterprise archi-tecture. Journal of Enterprise Architecture, 3(2), 7–18.

Zachman, J. A. (1997). Enterprise architecture: The issue of the century. Database Program-ming and Design, 10(3), 44–53.

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Documents

FAKULTAT F¨ UR INFORMATIK¨ - TUM · Management (EAM) dwell upon balancing these partly contradictory targets. One central task ofEAMis the management of the Application Landscape