Impact of Business Rule Management on Enterprise Architecture

Marlies van Steenbergen

, Koen Smit

and Martijn Zoet

Research chair Digital Smart Services, HU University of Applied Sciences Utrecht, The Netherlands

Research chair Knowledge-Intensive Business Processes, Zuyd University of Applied Sciences, The Netherlands

Keywords: Business Rule Management, Enterprise Architecture, Digital Business Strategy.

Abstract: Business Rule Management (BRM) is a means to make decision-making within organizations explicit and

manageable. BRM functions within the context of an Enterprise Architecture (EA). The aim of EA is to enable

the organization to achieve its strategic goals. Ideally, BRM and EA should be well aligned. This paper

explores through study of case study documentation the BRM design choices that relate to EA and hence

might influence the organizations ability to achieve a digital business strategy. We translate this exploration

into five propositions relating BRM design choices to EA characteristics.

1 INTRODUCTION

Enterprises operate in dynamic contexts. Society is

increasingly being digitized, disrupting businesses at

an increasing rate. For enterprises, digital

transformation is a prerequisite to being successful

(Bharadwaj et al., 2013; Bowersox et al., 2005).

Enterprises must make full use of emerging digital

possibilities to understand their customers as well as

to serve their customers (Catlin et al., 2014). By

making use of all available data, both internal and

external, enterprises can develop new services and

improve existing services (Ross et al., 2016; van der

Aalst, 2014; Chen et al., 2012; Erevelles et al., 2015).

These services are increasingly tuned to the specific

needs of the individual customer (Bonchek and

France, 2015; Ross et al., 2016). Customers expect

that all relevant information is incorporated, leading

to services that are always spot-on. Only enterprises

that develop a digital business strategy that possesses

the ability to make full use of the available data and

quickly respond to digital disruptions, can maintain

their competitive advantage (Bharadwaj et al., 2013).

Execution of business processes and interacting

with customers implies making decisions. Decisions

are based on underlying business logic. Business

logic originates from both external regulations and

internal policies. An example from a government

setting of such underlying business logic, is the

business rule that applicants earning more than

150.000 euro on a yearly basis cannot receive any

housing benefits. In dynamic contexts, to make full

use of available data in service offerings, business

logic must be flexible, transparent and consistent.

This requires some form of Business Rule

Management (BRM). BRM separates business logic

from execution, thus making business rules explicit

and, consequently, open to adaptation and inspection.

BRM can be defined as the systematic and controlled

approach, featuring a combination of methods,

techniques and tools, to support the elicitation,

design, specification, verification, validation,

deployment, execution, governance and evaluation of

business rules (Zoet, 2014).

Business rules represent one component in the

organizational landscape. To ensure flexibility,

transparency and consistency at enterprise level,

many organizations employ Enterprise Architecture

(EA). The aim of EA is to structure the enterprise in

a way that fits its strategic objectives. EA can be

defined as “the fundamental organization of a system

embodied in its components, their relationships to

each other and the environment, and the principles

guiding its design and evolution” (ISO/IEC

42010:2007), where in the case of EA the system is

an enterprise. From this definition we may conclude

that BRM solutions, i.e. the implementations of

business logic, are a part of EA and that the design

choices underlying these solutions may impact EA

and hence, the digital business strategy of the

enterprise.

In this paper, we explore the alignment between

BRM and EA. We investigate how BRM design

choices relate to EA, especially with EA’s

564

van Steenbergen, M., Smit, K. and Zoet, M.

Impact of Business Rule Management on Enterprise Architecture.

DOI: 10.5220/0007672105640571

In Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS 2019), pages 564-571

ISBN: 978-989-758-372-8

contribution to realizing a digital business strategy in

mind. The research question we want to answer is:

“What is the relation between BRM design choices

and EA’s ability to support a digital business

strategy?” The contribution of this paper is a set of

propositions defining the impact of BRM design

choices on EA. A good fit of BRM and EA is

important for organizations to successfully deal with

digital disruptions.

In section 2 the research method used is presented.

Next, we explore the positioning of BRM in EA, in

section 3. In section 4, we discuss the characteristics

EA must possess to enable digital success, followed

by an investigation of the relation between BRM

design choices and these EA characteristics in section

5. Section 6 discusses the results. Section 7, finally,

presents conclusions and limitations.

2 RESEARCH METHOD

To the best knowledge of the authors, the alignment

between BRM and EA has not been studied

extensively before. Following Edmondson &

McManus (2007) we are dealing with nascent theory

research, i.e. without extensive prior research results

to build on. In accordance with this state our aim is to

search for propositions relating BRM to EA as a first

step towards a theory. This suggests a primarily

qualitative research method. Studying the alignment

between BRM and EA implies connecting two

different fields of study. Both fields of study are

relatively young. However, research on EA has

grown over the past decades, whereas research on

BRM is still rather immature, especially regarding

non-technical, managerial type of research (Arnott

and Pervan, 2014). Eisenhardt (1989) argues that case

study research is an adequate approach to building

theory by way of formulating propositions. Taking

into account the different positions of BRM and EA

theory, we chose to derive knowledge about BRM

design choices from case study research and the

relevant EA characteristics from the extant research

literature, having it validated by experts.

The research presented in this paper is based on

the results of several case studies that are executed by

the co-authors in the past few years and address

various topics related to BRM (Smit, 2018). From the

documentation of these case studies, the first author

harvested the relevant insights about BRM design

choices that emerged in these studies. In parallel,

insights were derived from extant literature how EA

can contribute to the achievement of a digital business

strategy. Next, we compared the insights from the

case studies with those from the literature. This led to

a set of four propositions describing the relation

between BRM design choices and EA characteristics.

These propositions represent a first step towards a

theory of BRM impact on EA. The next step, which

is outside the scope of this paper, will be to test the

propositions using a survey or additional case studies.

The case studies from which the relevant BRM

design choices are harvested, all originate from a

mainly Dutch governmental context. Dutch

governmental organizations are, at an increasing rate,

being given the task to digitize products and services

for civilians and organizations. To this end, many

governmental organizations started to design and

implement BRM. All case studies evaluated in this

study applied a similar research method, collecting

data through one or multiple series of focus groups

and Delphi studies. Additionally, the organizations

included in the case studies were visited and/or

interviewed and secondary data on the

implementation of BRM were collected. Based on the

different types of data collected, grounded theory was

applied for data analysis, see (Smit, 2018) for details.

Additional results are taken from the study of eight

years of historical data from a British governmental

organization (Smit and Zoet, 2016).

3 POSITIONING BRM AS PART

OF EA

EA concerns the fundamental organization of a

system embodied in its components, their

relationships to each other and the environment, and

the principles guiding its design and evolution

(ISO/IEC 42010:2007). In this case, the system is an

entire enterprise, defined as any organization of

people, processes and means that share a common

goal (The Open Group, 2009). Thus, an enterprise can

be a company or institution, but it can also be a

network of cooperating parties. As the enterprise is

the scope of EA, the components to be considered are

diverse, varying from the products and services

offered by the enterprise to the processes that deliver

these products and services, the data and applications

being used and the IT hardware.

Several architecture frameworks exist, which

structure the components of EA and the relations

between them (for instance Zachman, 1987;

Lankhorst et al., 2005; van ‘t Wout et al., 2010). Most

frameworks distinguish two dimensions: one

dimension referring to the object of consideration,

e.g. business processes, data, applications or

Impact of Business Rule Management on Enterprise Architecture

565

infrastructure, and the other dimension referring to

different levels of perspective, e.g. contextual,

conceptual or physical. A well-known example and

one of the earliest frameworks is proposed by

Zachman (1987). As with early information

technology architectures, the original framework

suggested by Zachman does not contain business

rules as a primitive construct. The concept of business

rules is not limited to one cell, it connects various

cells. The Department of Defense Architecture

Framework (DODAF) recognizes two different types

of business rules (Department of Defense, 2010).

Firstly, business rules are applied to constrain process

flows and secondly business rules are applied to

structure decisions. This distinction between viewing

business rules narrowly in terms of their application

in processes or broadly as structuring decisions in

general, is found elsewhere as well. In the Reference

Model for Open Distributed Processing (RM-ODP),

business rules are applied to constrain process flows,

while business rules in the Agile Service

Development framework are applied to structure

decisions (Lankhorst et al., 2012). We conclude that

the concept of business rules is a broad construct that

recent frameworks incorporate differently.

Concerning the object of consideration, business rules

can be related both to the process view and the data

view. Concerning the level of perspective, the

definition of business rules belongs to the conceptual

level, while the implementation belongs to the

physical level.

In this paper, in accordance with the underlying

case studies, we adhere to the broader definition of

business rules as structuring decisions. According to

the ArchiMate 3.0 specification (The Open Group,

2016), a business decision can be defined as: “A

conclusion that a business arrives at through business

logic and which the business is interested in

managing”, where business logic can be defined as:

“a collection of business rules, business decision

tables, or executable analytic models to make

individual business decisions” (Object Management

Group, 2016).

With regard to the structural elements of a BRM

solution, three domains can be distinguished. The

source domain contains the sources for decision-

making, such as regulations and policies. The

implementation-independent domain contains the

definition of business rules and fact types in

conceptual terms. The implementation-dependent

domain contains the realization of the business rules

and fact types in actual systems, such as work

instructions or automated systems. The way the

interconnection between these domains is designed

has an impact on how easily business rules can be

adapted to changing circumstances. Decisions are

structured into contexts. A context is a coherent and

contained collection of knowledge required to

determine (part of) a decision. For instance, the

decision about granting child benefit rights consists

of checking child conditions on the one hand and

applicant conditions on the other hand. Within a

context a distinction is made between business rules

and fact type models. A business rule expresses what

is or is not allowed within the enterprise. A fact type

model expresses the factual business knowledge of

the enterprise, connecting core business concepts in a

way that reflects the real world. The way business

rules and fact types are structured may impact the

ease with which decisions made can be explained and

justified as well as the ease with which the underlying

knowledge can be adapted.

As part of EA, BRM solutions are part of the EA

models and subject to the EA principles. Based upon

the definition of EA, the match between EA and its

components can be considered from two

perspectives: 1) the connection of the BRM solution

to the rest of the enterprise and to its environment and

2) the extent to which the principles guiding the

design and evolution of the BRM solution are

consistent with the principles guiding the enterprise,

i.e. the EA principles. Our assumption is that the

extent to which the design choices that govern the

implementation of a BRM solution and the EA

principles are consistent with each other determines

the overall quality of business rule application within

the enterprise, in terms of contributing to the digital

business strategy. To determine the alignment

between BRM and EA, we must investigate the

design of the BRM solution and relate this to the

characteristics of EA that contribute to the digital

business strategy.

4 EA AND DIGITAL BUSINESS

STRATEGY

Realizing a digital business strategy implies certain

characteristics. We identified five themes that

emerged from the literature on digitalization as being

important to a successful digital business strategy:

adaptiveness, participation in an ecosystem,

transparency, openness and allowing for multiple

dynamics. We validated these themes in a focus group

of 4 enterprise architecture professionals with many

years of experience. They recognized the five

characteristics as being very relevant. However, they

ICEIS 2019 - 21st International Conference on Enterprise Information Systems

566

proposed that openness is part of transparency. In

answer to the question whether they missed an

important characteristic, they added the characteristic

of customer centricity. This led to the following five

characteristics being identified as important to

enterprises in achieving their digital business strategy

and reflected in EA.

 Adaptiveness: todays enterprises must be flexible

in their offerings to survive (Keen & Williams,

2013). Keen and Williams argue that EA must cater

for ever-continuing shifts in value. Value that is

determined by the customer, not by the enterprise,

and therefore less predictable. EA must be

designed for optimal capture of digital

opportunities. The increasing adoption of agile

approaches by organizations is indicative of the

need for adaptiveness. EA must enable

unanticipated changes in the organization’s

environment. Fine (2000) argues that flexibility is

about smartly alternating the adaption of product

development, process development and supply

chain development (3-dimensional concurrent

engineering). This requires modularity between the

dimensions. EA must ensure that the required

flexibility is built-in in the organization.

 Ecosystem Participation: the flexibility required of

enterprises can only be realized in cooperation with

other parties (Pagani, 2013). Most organizations

cannot deliver in time all capabilities needed to

keep up the required fast pace of innovation. This

implies that the capability of participating

successfully in an ecosystem is a key capability of

the organization. Enterprise boundaries are losing

their fixedness and are becoming fluent (Keen &

Williams, 2013). Also, enterprises come and go in

increasing rate. Analysis over the past shows that

the lifespan of enterprises is reducing rapidly

(Anthony, Viguerie and Waldeck, 2016). Because

of these developments, EA must enable fast in-,

out- and co-sourcing. As organizational boundaries

are becoming increasingly less fixed and important,

building an architecture on current existing

boundaries is not wise. Instead, the enterprise

architecture should be organization-agnostic,

effectively fulfilling the strategic goals of the

organization independent of actual organizational

boundaries. This approach will allow for flexible

sourcing in fast changing ecosystems. Designs

should cater for the possibility that any business

capability could be executed by any party. This

means that capabilities should be well-defined and

independent of other capabilities. In this way EA

can enable successful participation in the

enterprise’s ecosystem. Cooperation with other

parties, as well as engaging more with customers

requires a certain amount of openness of the

organization. (Pagani, 2013). At a structural level,

this requires interoperability (Guédria et al., 2013).

 Transparency: society increasingly demands

transparency from organizations. New European

regulation demands transparency from enterprises

in the use of customer’s data, putting high fines on

non-compliance (GDPR, 2017). Transparency

concerns both the way organizations handle the

data they acquire when interacting with customers

and delivering services and how they arrive at the

decisions they make, for instance regarding

applications for loans, admission to education or

housing. EA must ensure the traceability required

to be transparent.

 Multidynamic: many enterprises experience hybrid

situations, with the need to simultaneously manage

robust core transaction systems and experiment

with new technologies. As a consequence, different

rhythms of development and operations may occur

within the same organization, requiring different

EA principles (Ross et al., 2016; Da Rold et al.,

2014; Messaglio & Hotle, 2012). Ross et al. (2016)

argue that enterprises must distinguish between

their operational backbone and their digital

services backbone. The operational backbone

provides the capabilities for operational excellence.

It constitutes the set of business and technology

capabilities that ensure the efficiency, scalability,

reliability, quality, and predictability of core

operations. The digital services backbone

facilitates rapid innovation and responsiveness to

new market opportunities. It is the set of business

and technology capabilities that enable rapid

development and implementation of digital

innovations. Ross et al. (2016) argue that while the

technological differences between the two

backbones are likely to diminish with time, the

need for their differing organizational

characteristics will likely remain.

 Customer-centric. Customer centricity is about

truly putting the customer first, thinking outside-in,

instead of inside-out. The rise of data-driven

services offers consumers a lot of choice.

Customers will no longer be loyal to enterprises

that do not cater to their needs. In the past, as far as

IT was concerned, enterprise architecture tended to

focus on internal efficiency, whereas nowadays

customer experience seems to be a main driver.

Enterprises are more and more putting the customer

in the centre (Keen & Williams, 2013). Large

organizations are introducing the new role of

customer journey manager or customer journey

Impact of Business Rule Management on Enterprise Architecture

567

expert. Processes become increasingly centered

around the customer, instead of around a product or

service. Offerings and interactions are increasingly

tuned to the specific needs of the individual

customer (Bonchek & France, 2015; Ross et al.,

2016). Increasingly, customers are actively

involved in service creation (co-creation): with the

customer instead of for the customer.

The way BRM is implemented in the organization

ideally should be aligned with the way EA deals with

the above trends.

5 BRM DESIGN CHOICES

IMPACTING EA

CHARACTERISTICS

As a first step in determining how BRM design

choices might impact EA, the first author analysed

eight case study publications by the co-authors, from

the years 2014 (two publications), 2015 (one

publication) and 2016 (five publications). The

publications were annotated for concepts that pertain

to EA, i.e. concepts related to structure in terms of

components and relations between components. From

these case study analyses, several underlying design

choices for BRM emerged. These can be categorized

into design choices regarding the basic structural

model of a BRM solution and design choices

pertaining to traceability between BRM solution

components. In addition, one of the publications

reported on a set of 22 BRM design principles

resulting from the case studies. As such design

principles are meant to guide actual BRM design, it is

relevant to investigate how each of them relates to

EA.

 Structural Model. The structural model of BRM

solutions concerns the way a BRM solution is

structured into components and the way it interacts

with other components. For instance, a BRM

solution that isolates and explicates business rules

in a manner that is understandable by persons,

instead of hard-coding rules in software, can

greatly contribute to transparency as decisions

made by the organization are potentially easier to

explain. Also, this provides more intuitive access to

business rules, which has a positive impact on

adaptiveness. The extent to which the structural

model clearly separates the business rule

inferencing method, business rule repository and

business rule authoring service greatly influences

adaptiveness. A clear distinction between the

implementation-independent business rule

formulation and the implementation-dependent

formalization, increases the options for exploiting

business rules. Thus, business rules can be

valorised as knowledge, as a service, as a software

system or embedded within a product. This

separation between ‘know and flow’ (Zoet, 2014)

enables new application of business rules, such as

in smart contracts within blockchain

implementations.

Three main business rule architectures (BRA) can

be distinguished: rule family-oriented architecture,

fact-oriented architecture and decision-oriented

architecture (Smit & Zoet, 2016b).

 Traceability. In Smit, Zoet & Berkhout (2016a) a

traceability framework is presented. The

framework is directed at traceability of legal

requirements in a governmental environment. The

framework distinguishes three domains in which

elements are managed and traces implemented. The

source domain comprises the laws and regulations

as defined by the legal authorities. The

implementation-independent artifact domain

comprises artifacts that are free from technology-

specific aspects. The implementation-dependent

artifact domain contains technology- or vendor-

specific elements. Traces between these domains

can occur on various levels. Traces between the

implementation-independent artifact domain and

the implementation-dependent artifact domain can

occur between business rules and software systems,

services, components, classes or lines of code. The

design decisions made concerning traceability

determine the detail in which organizations can

explain their IT-supported decision-making. This

has a direct impact on the transparency of EA.

 Guiding Design Principles. In a combined focus

group and Delphi study, 22 principles governing

sound BR design were identified (Zoet & Smit,

2016). In table 1, these 22 principles are related to

the digital business strategy trends EA should

support, discussed in the previous section. Each

cross indicates an impact of a guiding design

principle on a digital business strategy trend.

The allocation of relationships in Table 1 was

established using the expertise of the four authors of

this paper. Two authors can be considered experts in

the field of EA and have extensive experience (20+

years) in the practical application of EA. Besides, one

of the EA experts is a PhD student and the other holds

an EA-related PhD and holds a professorship partly

related to EA. The other two authors can be

considered experts in the field of BRM and have

extensive experience (7+ years) in both the academic

as well as the practical application of BRM. Both

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568

researched their PhD projects in relation to BRM and

one expert holds a professorship related to BRM. All

four experts individually coded the relationships

between EA characteristics and the identified BRM

principles, thus exploring the impact of BRM on EA.

Each expert was provided upfront with the definitions

of the EA characteristics as well as the descriptions of

the BRM guiding principles. This ensured that every

expert had a similar, if not the same, frame of

reference when allocating the relationships. After the

coding was completed, two experts, one from the EA

and one from the BRM side, discussed the results of

the individual coding. In this process, the experts

evaluated the amount of votes a relationship had

(binary, there either was a relationship or not), and the

arguments for a given relationship.

Table 1: Allocation of impact of BRM principles on EA

characteristics.

Adaptiveness

Ecosystem

Transparency

Multi-dynamic

Customer-centric

1 Automated decisions

X X

2

IT formulates not BR

3

No big bang approach X

4 Authorization

X X

5

Ownership of decision

X X

6

Traceable decisions X X X

7 Two-time dimensions data

8

Source referral X X X

9

P.E.N.S criteria determined

10 Reuse before buy X

11

Best-of-suite approach

12

Gaming X

13 Sharing knowledge

X X

14

Context structure X X

15

Create once use multiple X X

16 Communication standards X X

17

Flexible decisions X

18

Government standards

19 Separation know and flow X

20

Management perspective X

21

Transparency

22 Compliancy by design

From Table 1 we can conclude that the guiding

principles governing business rule design as

identified in the case studies primarily affect the

adaptiveness and the transparency of EA, as well as

the contribution of EA to participation in the

ecosystem. The other trends are only primarily

impacted by one or two design principles.

6 DISCUSSION

Analysis of the way in which BRM solutions are

designed shows several EA-relevant design choices.

From the structural model perspective, we saw that

the way implementation-independent business rule

formulation and implementation-dependent

formalization are distinguished influences both

adaptiveness and transparency. Adaptiveness, not

only in the ease with which business rules can be

adapted or implementation can be updated, but also in

the ease with which enterprises can create new value

from their business rule knowledge and

implementation, possibly creating new positions in

the ecosystem they participate in. In addition, the

choices made in the way business rules and fact types

are structured (rule family-oriented, fact-oriented or

decision-oriented architecture) directly influence

adaptiveness. Transparency is, besides the separation

of implementation-independent and implementation-

dependent domains, also greatly influenced by the

granularity of the traceability in the BRM

components, from source to implementation-

independent to implementation-dependent. Finally, it

appears that adaptiveness and transparency are also

impacted by half of the design principles that govern

BRM implementation. In addition, participation in an

ecosystem is also supported by almost half of the

design principles.

From the above we can derive five propositions.

The first proposition expresses the nature of the

impact of BRM design choices on EA:

 Proposition 1. BRM primarily impacts

adaptiveness and transparency, and to a slightly

lesser extent, participation in an ecosystem.

The other four propositions refine this relation,

further detailing the relation between specific BRM

design choices and EA:

 Proposition 2. The choice of business rule

architecture directly impacts the adaptiveness of

EA.

 Proposition 3. The level of detail designed into the

traceability of BRM directly impacts the

transparency of EA.

Impact of Business Rule Management on Enterprise Architecture

569

 Proposition 4. The distinction between

implementation-dependent and implementation-

independent business rules directly impacts both

the adaptiveness and the transparency of EA.

 Proposition 5. The design principles defined by

BRM experts primarily impact adaptiveness,

transparency and participation in an ecosystem.

The design of BRM solutions has direct impact on the

ability of EA to contribute to a digital business

strategy. We have seen how various characteristics of

BRM choices influence various characteristics of EA.

However, we should bear in mind, that in context, the

relation between BRM design choices and EA

characteristics may be influenced by other factors and

that the interplay between many factors determines

the nature of EA. Thus, BRM can increase

transparency by making decision-making

understandable and traceable. If, however, business

rules are used by a machine learning component,

traceability might to a certain extent be lost again.

And whereas the rule family-oriented architecture

scores best on modifiability, thus contributing to the

adaptiveness of EA, it remains to be seen whether it

also scores best on transparency.

Reasoning the other way around, to make the most

of BRM, EA must also possess an adequate level of

quality. For instance, a high maturity level of data

management within the organization enables better

integration of BRM solutions in the entire EA,

offering better opportunities for automation and

service innovation. Also, an EA based on the concept

of services will be better positioned to make the most

of a well-designed BRM solution. If this is the case, a

well-designed BRM solution allows various business

models, not only using business rules for offering

products and services, but offering the business rule

knowledge itself, either to be implemented by another

party or as a service in its own right. As a final

example, the level of traceability implemented in the

BRM solution must be sustained in EA. It is not

sufficient to be able to explain what business rules are

defined and how they relate to regulation if the

organization cannot explain the validity of the input

to the rules or the legality of the use of the outcomes.

7 CONCLUSIONS

In a digitized society, organizations face new

challenges and new opportunities. BRM can play an

important role in dealing with the challenges as well

as in seizing the opportunities. Challenges such as

increased demand for transparency and the need for

flexible sourcing as well as opportunities such as new

service offerings and more personalized services are

addressed by careful BRM design. To fully exploit

the possibilities of business rules, they must be

smoothly fitted in the overall EA.

Based on case study research we developed a

number of propositions concerning the relation

between BRM design and EA. It appears that BRM

primarily impacts adaptiveness and transparency, and

to a slightly lesser extent participation in an

ecosystem. To validate our propositions, future

research is needed into operationalizing EA

adaptiveness and EA transparency. In addition, we

propose future research into the trade-off between

various BRM characteristics.

REFERENCES

van der Aalst, W.M.P. 2014. “Data Scientist: The Engineer

of the Future,” in Enterprise Interoperability VI,

Proceedings of the I-ESA Conferences 7, K. Mertins et

al. (eds.), Switzerland: Springer International

Publishing, pp. 13-26.

Anthony, S.D., Viguerie, S.P., and Waldeck, A. 2016.

“Corporate Longevity: Turbulence Ahead for Large

Organizations,” Innosight, Executive Briefing, spring

2016.

Arnott, D., and Pervan, G. 2014. “A critical analysis of

decision support systems research revisited: the rise of

design science,” Journal of Information Technology,

29, pp. 269-293.

Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., and

Venkatraman, N. 2013. “Digital Business Strategy:

Toward a Next Generation of Insights,” MIS Quarterly,

(37:2), pp. 471-482.

Bonchek, M., and France, M. 2015. “The Best Digital

Strategists Don't Think in Terms of Either/Or,”

Harvard Business Review, pp. 1-5.

Bowersox, D., Closs, D., and Drayer, R. 2005. “The Digital

Transformation: Technology and Beyond,” Supply

Chain Management Review, January 2005, pp. 1-9.

Catlin, T., Patiath, P., and Segev, I. 2014. “Insurance

Companies' Untapped Digital Opportunity,” Harvard

Business Review, pp. 1-5.

Chen, H., Chiang, R.H.L., and Storey, V.C. 2012.

“Business Intelligence and Analytics: from Big Data to

Big Impact,” MIS Quarterly, (36:4), pp. 1165-1188.

Da Rold, C., Olding, E., and Short, J. 2014. “Bimodal IT

and Adaptive Sourcing Are Critical to Digital Business

Success,” Gartner report, 3 september 2014.

Department of Defense, 2010. The DoDAF Architecture

Framework Version 2.0, Retrieved from

http://dodcio.defense.gov/Library/DoD-Architecture-

Framework/

Edmondson, A.C., and McManus, S.E. 2007.

“Methodological Fit in Management Field Research,”

ICEIS 2019 - 21st International Conference on Enterprise Information Systems

570

The Academy of Management Review, (32:4), pp. 1155-

1179.

Eisenhardt, K.M. 1989. “Building theories from case study

research,” Academy of Management Review, 14, pp.

532-550.

Erevelles, S., Fukawa, N., and Swayne, L. 2015. “Big Data

Consumer Analytics and the Transformation of

Marketing,” Journal of Business Research, (69:2), pp.

897-904.

Fine, Ch.H. 2000. “Clockspeed-based Strategies for Supply

Chain Design,” Production and Operations

Management, (9:3), pp. 213-221.

GDPR. 2017. Retrieved from https://www.eugdpr.org/ .

Guédria, W., Naudet, Y., and Chen, D. 2013. “Maturity

model for enterprise interoperability,” Enterprise

Information Systems, http://dx.doi.org/

10.1080/17517575.2013.805246.

Keen, P., and Williams, R. 2013. “Value Architectures for

Digital Business: Beyond the Business Model,” MIS

Quarterly, (37:2), pp. 642-647.

Lankhorst et al. 2005. Enterprise Architecture at Work,

Heidelberg: Springer.

Lankhorst et al., 2012. Agile Service Development:

Combining Adaptive Methods and Flexible Solutions,

Springer.

Messaglio, M., and Hotle, M. 2012. “Pace-Layered

Application Strategy and IT Organizational Design:

How to Structure the Application Team for Success,”

Gartner report, 5 october 2012.

Object Management Group, 2016. Decision Model And

Notation (DMN), Version 1.1. Retrieved from

http://www.omg.org/spec/DMN/1.1

Pagani, M. 2013. “Digital Business Strategy and Value

Creation: Framing the Dynamic Cycle of Control

Points,” MIS Quarterly, (37:2), pp. 617-632.

Ross, J. W., Sebastian, I. M., Beath, C., Mocker, M.,

Fondstad, N., O., and Moloney, K. G. 2016. “Designing

and Executing Digital Strategies,” In: Proceedings of

the International Conference on Information Systems

(ICIS), 1-16.

Smit, K., Zoet, M., & Berkhout, M. 2016a. “A Framework

for Traceability of Legal Requirements in the Dutch

Governmental Context,” In Proceedings of the 29th

Bled eConference pp. 151–162. Association for

Information Systems (AISeL).

Smit, K., Zoet, M. 2016b. “Utilizing change effort

prediction to analyse modifiability of business rules

architectures,” Proceedings of the 20th Pacific Asia

Conference on Information Systems (PACIS), Chiayi,

Taiwan.

Smit, K. 2018. “Organization and Governance of Business

Rules Management Capabilities,” Hogeschool Utrecht.

The Open Group. 2009. TOGAF, Version 9, The Open

Group Architecture Framework (TOGAF), Van Haren

Publishing.

The Open Group, 2016. ArchiMate 3.0 Specification.

Retrieved from http://pubs.opengroup.org/architecture/

archimate3-doc/

van't Wout, J., Waage, M., Hartman, H., Stahlecker, M.,

and Hofman, A. 2010. The Integrated Architecture

Framework Explained: Why, What, How. Berlin:

Springer.

Zachman, J.A. 1987. “A framework for information

systems architecture,” IBM Systems Journal, (26:3), pp.

276-292.

Zoet, M. 2014. “Methods and Concepts for Business Rules

Management” (1st edition). Utrecht: Hogeschool

Utrecht.

Zoet, M., & Smit, K. 2016. “Business Rules Management

Principles in the Dutch Governmental Context,” In

PACIS 2016 Proceedings pp. Paper 260.

Impact of Business Rule Management on Enterprise Architecture

571