Goal-Oriented Business Process Engineering Revisited:

a Unifying Perspective

Dina Neiger

, Leoind Churilov

School of Business Systems, Faculty of Information Technology, Monash University,

Victoria 3800, Australia

Abstract. The goal-oriented approach to business modeling was identified as

one of the “three most important issues in driving business processes towards

their goals” by the Business Process Management Journal [1]. Although goal-

oriented process engineering is gaining momentum, with frameworks, methods

and tools being developed in increasing numbers, it continues to be segmented

across various research disciplines, with duplication of effort and lack of a

coordinated approach to this important research problem. While it is both

unlikely and undesirable to have a single method that addresses all business

needs, understanding the relationship between the existing approaches will help

to identify overlaps and articulate gaps reducing duplication of effort and

providing direction for future research. The aim of this paper is firstly, to build

on existing assessment frameworks to provide a coherent review of goal-

oriented process engineering that crosses disciplinary boundaries; and secondly,

to provide an alternative perspective on goal-oriented process engineering by

integrating decision and process management based methodologies.

1 Introduction and Background

“Paving of the cow path” is how Yu and Mylopoulos ([2], p. 16) describe traditional

modeling techniques that address the “what” of the business process without the

“why”. Goal-oriented approaches aim to avoid this dilemma by complying with the

premise that “human activity is inherently purposeful” ([3], p. 19). In a more

pragmatic view of the world, goal-oriented approaches are the result of the need to

ensure effectiveness as well as efficiency of organizations [4], [5].

This paper is motivated by an apparent need to consolidate in a coherent

framework, the growing number of goal-oriented approaches to process engineering

originating from the decision, system and process points of view representing

Decision Sciences, Requirements Engineering and Business Process Management

disciplines (respectively).

Neiger D. and Churilov L. (2004).

Goal-Oriented Business Process Engineering Revisited: a Unifying Perspective.

In Proceedings of the 1st International Workshop on Computer Supported Activity Coordination, pages 149-163

DOI: 10.5220/0002670401490163

 SciTePress

Process Perspective

Process Models

Goal Models

Decision

Sciences

Requirements

Engineering

Business Process

Management

Goal-Oriented

Process Models

Fig. 1. Multi-disciplinary view of goal-oriented process modeling.

The goal-oriented view of business process engineering dictates that business goals

are the driving force for structuring and evaluating business processes. Furthermore,

to ensure congruency between organizational values and actions, both goal and

process models should be capable of representing various organizational perspectives

(refer to Figure 1). For a goal model, this includes ability to separate between

different types of goals and to describe relationships between them (e.g. [6]). For a

process model, this incorporates the ability to model the sequential nature of business

activities as well as the resources, inputs and outputs linked to the process (e.g. [7],

[8]). Goal-oriented process engineering approaches are often the result of integration

or extension of existing goal modeling and process modeling approaches. A brief

introduction to the goal modeling and process modeling perspectives within the

Decision Science, Requirements Engineering and Business Process Management

disciplines is included to provide the context for the rest of this paper.

The concept of goals and objectives is well established within the Decision

Sciences context. However, the need to link specific decision objectives to the overall

organizational objectives and values has been recognized only relatively recently [9],

[10]. This has resulted in a “value-focused thinking” framework for elicitation and

structuring goals. Similarly, the development of goal-models has been a recent

phenomenon with the business process management community following an

increasing awareness of the importance of aligning process models with

organizational goals (e.g. [3], [11]). Interestingly, the field of Requirements

Engineering (RE) is considerably more advanced in the area of goal modeling and its

links to business processes [6]. Goal models within RE cater for multiple goal types

and relationships among goals and links to other elements of business processes.

These developments in goal-oriented requirements engineering have inspired a

number of frameworks (some accompanied by modeling tools) for goal-oriented

business process engineering (e.g. [2], [12], [13], [14]). Generally, these frameworks

address the “development of business process software” ([15], p. 4) goal of business

process modeling. The other approaches to business process engineering better cater

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for additional aims of business process models defined by Aguilar-Saven ([15], p. 4)

as “learning about the process” and “making decisions on the process”. For example,

within the business process management the focus of business process modeling is on

“documentation, analysis and design of the structure of business processes, their

relationship with the resources needed to implement them and the environment in

which they will be used” ([16], p.2) whereas within the context of Decision Sciences,

the term process (and correspondingly process model) would usually refer to the

decision making processes within the organization.

Even from a brief overview provided above, it is evident that the field of goal-

oriented process engineering is highly fragmented along disciplinary lines.

Unification of goal-oriented process engineering will help avoid duplication of effort

while identifying gaps in the existing approaches. The objectives of this paper are:

− to facilitate unification of goal-oriented process engineering field by providing a

cross-disciplinary framework for assessment of existing approaches; and

− to propose an integrated framework that aims to close gaps in existing goal-

oriented process engineering approaches.

This paper is structured as follows. Goal models and process models are reviewed

from each of the three perspectives illustrated in Figure 1 in Sections 2 and 3. Section

2 includes assessment of goal models using the Nishit framework [14] that outlines a

set of desirable qualities in the goal model, and is the only framework identified in the

literature that allows discipline independent comparison of goal models. Accordingly,

Section 3 includes a cross-disciplinary comparison of process models using the

Giaglis-Curtis framework [7] that describes process perspectives that are required for

a comprehensive process model. This is followed by a discussion of implications of

the individual assessments for evaluation of goal-oriented process modeling

approaches and a review of a goal-oriented process model that integrates both

Decision Sciences and Business Process management perspectives with the aim of

addressing shortcomings identified within the individual methods (Section 4). The

paper is concluded with a brief summary and an outline of future research directions.

2 Goal Modeling

Generic models that are linked to existing process modeling techniques with the aim

of developing a goal-oriented process model, and specific models that form part of an

individual process modeling technique are included in the discussion. Goal models

are discussed within the context of the three disciplines corresponding to the decision,

systems and process perspectives (as illustrated in Figure 1).

2.1 Decision Sciences

Within the decision analysis field two goal models are of particular interest in the

context of process engineering: “the value focused thinking” framework that forms

the basis of the generic goal model in classical decision analysis; and an implicit goal

model included within the system dynamics approach to decision making.

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Value-Focused Thinking Framework. In classical decision analysis, goals are

usually referred to as objectives and are structured using the “value-focused thinking”

framework developed by Keeney and Raiffa [9]. Within this framework, objectives

are defined as “a statement of something that one wants to strive toward” ([10, p.34])

and are structured in two levels: the fundamental objectives hierarchy reflecting the

fundamental values of the business and the means-ends objectives network reflecting

the means of achieving fundamental objectives.

A set of questions for identification of fundamental and means objectives and the

movement within the hierarchy and the network is included within the framework in

order to facilitate elicitation and structuring of objectives. This model was originally

developed in order to link the narrow objectives of individual decision problems to a

wider organizational context. However, due to the generic nature of the model, it has

also been used to link process goals to a wider organizational context (as illustrated in

the model introduced by Neiger and Churilov [17] discussed in a latter part of the

paper), and to separate causal and abstract relationships between objectives in

requirements engineering [18]. One of the shortcomings of this model is the limited

representation of logical relationships among objectives (e.g. it is assumed that all

lower level objectives need to be satisfied in order for the parent objective to be

satisfied), although this can be somewhat overcome by strong links between the

framework and Multiple Criteria Decision Analysis models [19] to allow other

influencing and logical relationships to be represented.

Systems Dynamics. System dynamics (and its application to business, referred to as

business dynamics [20]) is used both for decision making [21] and structuring

business processes [7] using causal loops and stock and flow diagrams. Causal loops

represent the “interdependencies and feedback processes” ([20], p.191) within the

business, while stock and flow diagrams represent “the state of the system and

generating the information upon which decisions and actions are based” ([20], p.192).

This approach to business modeling allows for representation of time delays and non-

linearities inherent in the dynamic nature of a business whilst the rigorous

mathematical foundation for system dynamics makes possible a seamless link from a

business model to a simulation model to allow quantitative evaluation of ‘what if”

scenarios.

The strong emphasis on causality within the system dynamics framework provides

solid foundations for decision analysis by highlighting causal and feedback

mechanisms within the organization and its wider environment. The disadvantage of

this approach, from a process modeling point of view, is that it inhibits representation

of the sequential nature of business processes. System dynamics is therefore discussed

within the Decision Sciences sections of this paper.

There are no separate goal models within System Dynamics since goals are

represented within causal loops and stock and flow diagrams as “concrete targets”

that guide corrective action if the actual performance of the system falls short of a

satisfactory outcome. These targets are also commonly referred to as Key

Performance Indicators (KPIs) and are derived by quantifying organizational

objectives with aim of reducing complexity associated with solving optimization

problems and in accordance with the principle of bounded rationality ([20], ch. 15).

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2.2 Requirements Engineering

Supporting organizational change resulting from transition between “as is” and “to

be” process models is considered to be “the overriding purpose of requirements

development for business processes” ([13], p.2). Within this context, goals are defined

as “statements which declare what has to be achieved or avoided by a business

process” ([3], p.20) and provide motivation for process description. Hurri [18],

Kavakli [12] and Lamsweerde [6] provide comprehensive reviews of goal-oriented

requirements engineering including a review of generic goal models referred to as

goal-refinement (or goal-reduction) graphs and links between generic models and

elements of process models incorporated in individual RE methods such as i*, GDC,

KAOS, etc [12]. For the purposes of completeness, a brief summary guided by the

Lamsweerde’s review [6] of generic approaches to goal modeling within the

requirements engineering is provided.

Lamsweerde ([6], p. 3) lists the following dimensions for goal classification

according to goal type: functionality, verification, temporal, system state and goal

level. Within the functionality dimension the goals are divided into functional goals

that refer to “services that the system is expected to deliver” including an ability of a

system to satisfy requests and provide required information; and non functional goals

that refer to “expected system qualities such as security, safety, performance,

usability, flexibility, customizability, interoperability and so forth”. Verification

dimension is concerned with whether goal “satisfaction can be established through

verification techniques”, if it can then the goal is referred to as a hard goal, otherwise

the goal is categorized as a soft goal. Temporal behavior of the goal is classified into

three classes: achieve (or cease) goals “require some target property to be eventually

satisfied in some future state (resp. denied); maintain (or avoid) goals “require some

target property to be permanently satisfied in every future state (resp. denied)”; and

optimize goals favor behaviors “which better ensure some soft target property”.

Similarly system state and goal level dimensions classify goals according to desired

system states and goal levels.

According to Lamsweerde ([6], p. 3) name, specification, priority, utility and

feasibility are the four goal attributes that can also be used to characterize goals

within the requirements engineering context.

Requirements engineering goal models cater for a variety of goal modeling

structures using different types of links to “relate goals (a) with each other and (b)

with other elements of requirements models” ([6], p. 3). AND/OR refinement graphs

are widely used to describe relationships between goals. Refinement is referred to a

set of sub-goals that either positively or negatively support a parent goal. AND-

refinement describes situations where “satisfying all subgoals in the refinement is

sufficient for satisfying the parent goal”, whereas OR-refinement means that

“satisfying one of the refinements is sufficient for satisfying the parent goal”. Within

this context a conflict link is introduced for situations when “satisfaction of one of

them may prevent the other from being satisfied”. While the above definitions refer to

goal satisfaction, Lamsweerde [6] provides alternative definitions in terms of goal

satisficing guided by the principle of bounded rationality.

Links between goal models and other elements of requirements models such as

operations, scenarios, objects, agents and organizational policies, enable goal-oriented

approaches to process engineering within the requirements engineering context.

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One of the limitations of the requirements engineering goal model is the lack of

separation between abstract relationships (depicted within the fundamental objectives

hierarchy in the value-focused framework) and causal relationships (depicted within

the means ends network in the value-focused framework) that allow for function and

non-functional goals to be related to each other without confusion ([18], p. 34).

2.3 Business Process Management

Business Process Management models often include components of decision science

and requirements engineering paradigms, while on the whole, being more concerned

with the sequence of activities within the process and having broader organizational

context than either decision sciences or requirements engineering techniques. While

there is a widespread agreement within the Business Process Management field on the

importance of linking processes to goals ([3], [11], [16], [22], [23]) there is no

universally accepted generic model for goals or their relationship to a process model.

The spectrum of goal representation starts with modeling methods that have no

concept of goals and progresses through to fully goal-driven process modeling

frameworks incorporating all intermediate steps.

To avoid the impossible task of reviewing every process modeling technique from

a goal modeling perspective, the process modeling methods have been classified into

four categories: traditional, coordination, socio-technical and generic. This

classification is based largely on the categories introduced by Katzenstein and Lerch

[24] of business process models that were based on the ability of the process

modeling techniques to represent social context including goals. The generic category

was added to Katzenstein and Lerch classification to accommodate the concept of

generic methodologies that have business process modeling capabilities [15]. The

goal-modeling aspects of each class are reviewed in this section.

Traditional System Methodologies. Traditional systems analysis methodologies

refer to methodologies that were used to develop information systems, for example

flowcharts, dataflow diagrams and IDEF suite of process models. Despite recent

proposals to make some traditional methodologies goal-friendly (e.g. Downs & Lunn

in [11]), these methodologies generally do not have goals as part of their model

elements and therefore are of no further interest in the context of goal modeling.

Coordination Models. Models included in this class are derived from computer

science, operations management and the quality movement including Petri-net based

models generally used for workflow modeling, other workflow modeling languages,

object-oriented business process models including UML based models, and others

such as Rummler-Brache model and Role Activity Diagrams. These models usually

have a concept of goal, with some methods such as UML having capacity to explicitly

link activities and corresponding goals. Most methods within this category especially

those used to model the workflow rely on an underlying process model for linkage to

goals.

Kueng [3] proposed a goal-based business process model to be used as a basis for

an object-oriented business model (without loss of generality the model can be

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adopted to other coordination models). The four steps involved in this method as

described by Kueng ([3], p.22) are:

1. Goal modeling that defines goals by asking the question “why has something to be

done?”

2. Activity modeling that defines activities and output by asking the question “what

has to be done?”

3. Role modeling that defines logical dependencies between activities by asking the

question “when has it to be done?”

4. Object modeling to define the roles and assign them to activities by asking the

question “by whom has it to be done?”

Within this framework the goals are represented by a Goal/Means-hierarchy that is

used to decompose process-related goals until they can be transformed into activities

that can be carried out within the process. However the Goal/Means-hierarchy doesn’t

reflect the contradictory, independent and complementary nature of relationships that

exists between goals. One of the strengths of this methodology is that it encourages an

evaluation of the business process model using goals.

Socio-technical Qualitative Systems. Socio-technical qualitative systems (such as

Goal-Exception-Dependency framework (GED), and Multiview) are based on the

principle that “both technology and people matter” ([24], p.388). One of the

advantages of these systems is their ability to capture and organize goals such as for

example, the GED framework [24]. Within this framework, a goal/exception diagram

is used to represent “process-level and individual goals, the relationship among those

goals, the exceptions that have emerged in the process, and the goal conflicts that are

reflected in those exceptions” ([24], p.401). According to the GED framework

authors, this model provides “the same reasoning and communication advantages as

cognitive maps and as the more specific goal-based causal reasoning” of requirements

engineering methods such as i* ([24], p.401).

Generic Methodologies. Generic methodologies are more encompassing than

business process models alone as they include other capabilities. For example, the

ARIS methodology is based on the concept of an extended-event-driven process chain

(e-EPC) model of a business process ([16], [23], [25], [26]) but includes objectives

diagram and a balance-score card tools within its tools set. Similarly, the GRAI GIM

methodology makes explicit the why dimension of the process and articulates how

objectives can be reached through its decision view [27]. The uniqueness and

advantage of these methodologies is that various tools are seamlessly linked to each

other providing ‘a simple yet clear view of the business’ ([28], p. 149) through

multiple views of a business process. The ARIS methodology is of particular interest

as it is considered to be one of the most “advanced tools available in the market

place” ([29], p.12) and it has a large market share (through its integration within the

SAP suite [28], [30] in the corporate and government sectors in the developed

countries [31].

Within ARIS, goals are implied in the definition of a function as “a technical task

or action performed on an object to support one or more company goals” ([32], p.4-1).

This approach assumes that company goals and objectives are known to the modeler

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in advance and are supported by functions [23]. There is no accompanying framework

for goal-oriented business process modeling.

Other Goal Models. Discussed goal models within the business process management

context, are either part of or tightly integrated with existing process modeling

methodologies and tools. As the importance of goal-orientation in business process

modeling becomes more apparent to researchers and practitioners within this field,

other conceptual approaches to goal modeling and goal-oriented process modeling

will arise independent of already existing tools and techniques. The Workshop on

Goal-Oriented Business Process Modeling [11] and a follow-up special issue of the

Business Process Management Journal on Goal-Oriented Business Process Modeling

(to be published in 2004, [1]) has identified a number of such approaches.

Within the material currently available (see web site) the general pattern is towards

identification and structuring goal using methods similar to Requirements

Engineering methods described above (e.g. AND/OR reduction graphs, identification

of various relationships among goals, linkage between goals and actors responsible

for them). The exception is a “state-flow view of business processes” advocated by

Khomyakov and Bider [33] that defines the process as a trajectory between system

states with the business goal being described as a final state that business is aimed to

achieve. This model has more in common with the Dynamic Programming [34] view

of the process than with traditional process modeling approaches. In this context, a

goal is expressed in terms of “reaching the surface in the state space of process

variables” ([35], p.3).

2.4 Goal-Modeling Comparison

In “A study on Goal-Oriented Business Process Modeling”, Nishit [14] identified the

following elements as being important for a goal-oriented business process model:

goal concept (present in all goal models by definition), goal relationship including

logical, causal and influencing relationships, and an evaluation mechanism to enable

an assessment of the level of achievement of different goals. While this model

requires further development and refinement (for example implementation issues need

to be included in the model) it provides a good starting point for comparison of goal

modeling capabilities across disciplines and methodologies. Table 1 summarizes the

properties of the discussed goal models according to the 4 criteria. Traditional system

methodologies are excluded from the table, as they do not have the concept of goal.

Requirements engineering goal-models are combined as they have mostly the same

characteristics as far as assessment criteria are concerned. In the Business Process

Modeling category only one methodology was evaluated as a representative of its

group to avoid comparison of ‘like’ methods.

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Table 1. Comparison of goal models.

Goal relationship Discipline Goal modeling

methodology

Goal model

Logical Causal Influencing

Evaluation

mechanism

Value-Focused

Thinking

Objectives

hierarchy,

Means-ends

network

Some Yes Yes Some

Decision

Analysis

System

Dynamics

Stock &

Flow, Causal

Loop

diagrams

No Yes Yes Yes

Requirements

Engineering

Various Goal

refinement

graphs

Yes Yes Yes i* [14]

Coordination

models

(Kueng)

Goal-means

hierarchy Yes Some Some Yes

Socio-technical

qualitative

systems (GED)

Goal-

exception

diagram

Yes Yes Yes No

Generic

methodologies

(ARIS)

Objectives

Diagram,

Balance-

Score Card

No Some No Some

Business

Process

Modeling

State-flow

model

System

equations

Some Yes No Yes

3 Business Process Modeling

As the purpose of this paper is to compare goal-oriented process modeling techniques

rather than to provide an overall comparison or review of process models the scope of

this section has been limited to the process modeling techniques associated with or

incorporating goal models described in the previous section.

In discussing process modeling capabilities of various goal-oriented methodologies,

references are made to the four perspectives of business process models used as a

basis for the Giaglis-Curtis taxonomy of business models ([7], p.212):

1. The functional perspective represents what process elements (activities) are being

performed.

2. The behavioral perspective represents when activities are performed (for example,

sequencing) as well as aspects of how they are performed through feedback loops,

iteration, decision-making conditions, entry and exit criteria, and so on.

3. The organizational perspective represents where and by whom activities are

performed, the physical communication mechanism used to transfer entities, and

the physical media and locations used to store entities.

4. The informational perspective represents the information entities (data) produced

or manipulated by a process and their interrelationships.

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3.1 Decision Sciences

Generally, the main weakness of decision analysis methodologies is the weak

representation of activities aimed at fulfilling business objectives (i.e. the functional

perspective). For example, within the value-focused framework the link to activities

responsible for fulfilling objectives is not made. Similarly, the System Dynamics

approach provides limited support for representation of functional and informational

perspectives [7] due to its focus on the process as “defined by flows and

accumulations and controlled in terms of information feedback and process

parameters” ([20], p.12). This approach results in a behavioral representation of the

business that “can be used to show how a change in any stage of the process can

propagate to all subsequent stages” ([20], p.12).

Despite the obvious limitations of decision analysis methods in modeling business

processes, their ability to enrich existing process models by facilitating greater

decision support capability and a more strategic approach to process engineering

makes them very useful tools in goal-oriented process engineering.

3.2 Requirements Engineering

Kavakli ([12], p. 238) identifies three core RE activities: requirements elicitation,

requirements specification and validation. The latter two activities are concerned with

specification of system components and validating system specifications

(respectively) and as such do not directly involve process engineering. On the other

hand, process engineering is an important component of the requirements elicitation

activity that is concerned with understanding of the current organizational situation

and the need for change ([12], p. 239). Within this context, the i* strategic rationale

modeling is identified by Kavakli as one of the two goal-oriented approaches within

the scope of requirement elicitation. As was shown Table 1, i* has the most

comprehensive goal model within the RE field and from a process modeling

perspective it can be described as an agent-oriented approach that defines processes

“according to the organizational agent that performs certain tasks” [13]. This and

similar approaches effectively represent behavioral and organizational perspectives

but less effective in representing functional and informational perspectives.

The S

framework proposed by Loucopoulos ([13], p.1) aims to overcome this

limitation by adopting a ‘multifaceted approach that addresses issues that arise from

the nature of the business processes and of the RE process itself”. Within the S

framework business process models incorporate goals (strategy or “why” dimension),

activities (service or “what” dimension) and collaboration between organizational

actors (support or “how” dimension). System Dynamics is proposed by Loucopoulos

([13], p.3) as an integrating modeling paradigm. While not specifically goal-oriented,

this approach integrates goal models adopted by requirements engineering to a

broader process engineering context. The framework in its current presentation [13]

doesn’t include implementation guidelines or an illustration of its application in

practice; hence it is difficult to compare it with other approaches in the field.

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3.3 Business Process Management

Traditional system methodologies and coordination models have a strong focus on the

functional perspective of business process modeling. Giaglis [7] provides a

comprehensive assessment of these techniques that is used in the Comparison section

of this paper. Other goal-oriented business process management models discussed in

this paper are excluded from the Giaglis review and therefore justify a brief

discussion.

Katzenstein and Lerch [24] provide a review of socio-technical systems from

process redesign point of view. It is clear that, similar to requirements engineering

models, these systems provide better than average representation of roles, goals and

dependencies (behavioral and organizational perspectives) but lack other process

perspectives. For example, the sequential flow of activities critical for a process

model is not represented within the GED methodology.

The strength of generic methodologies, on the other hand is their ability to

represent all process perspectives in a coherent but simple to understand manner. For

example, within ARIS, the functional perspective is represented in the process view

and with a functional tree model, behavioral perspective is represented through an

event-driven process chain that illustrates sequencing and the details of individual

activities through the decomposition capabilities of the tools. The organizational chart

provides the “who” component of an organizational perspective, while the

Data/Output view and information flows ensure that information flows demonstrate

informational process inputs and outputs and their interrelationships.

The state-flow view of the business process is able to demonstrate the sequential

nature of the process but has a limited applicability with respect to other perspectives.

3.4 Business Process Modeling Comparison

Table 2 summarizes discussion in this section. Categories presented in Table 2 are

limited to the depth dimension of the Giaglis-Curtis framework for ease of

presentation. The choice of the depth dimension over breadth was motivated by the

fact that the depth dimension aims to analyze qualities of process modeling

techniques across disciplinary boundaries while the breadth dimension is more

aligned to disciplinary boundaries ([7], p. 213). The absence of breadth dimension

means that some of the modeling techniques requirements are not discussed (e.g.

process automation, decision support, etc.). Future research is planned to provide a

more complete assessment of goal-oriented process modeling methods from the

process modeling point of view in the context of the Giaglis-Curtis and other

evaluation frameworks (e.g. [7], [15], [24], [29], [36], [37]).

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Table 2. Comparison of goal models.

Modeling Perspective Discipline Goal modeling

methodology

Process model

What When

How

Where

Who

Data

Value-Focused

Thinking

not applicable

No No No No

Decision

Analysis

System Dynamics Stock & Flow,

Causal Loop

diagrams

Some Yes Yes Some

Requirements

Engineering

Various i*

Some Yes Yes Some

Coordination models

(Kueng)

UML

Yes Some Some Yes

Socio-technical

qualitative systems

GED

Some Yes Yes Some

Generic

methodologies

ARIS

Yes Yes Yes Yes

Business

Process

Modeling

State-Flow model Some Yes Some Some

4 Goal-oriented Business Process Engineering – an Integrated

Approach

While it is possible to identify strengths and weaknesses of goal-oriented business

process modeling by simply looking at Table 1 and 2 side by side, the multi-

dimensional nature of the problem (that would be compounded if other criteria or

dimensions were introduced) makes it difficult to determine the best model in a

particular situation. A Multi-Criteria Decision Analysis [19] model using these

(and/or other criteria) would facilitate a dynamic assessment of goal-oriented process

modeling incorporating priorities and constraints of individual modelers and

situations and will be the subject of future research. Within the scope of this paper it

is sufficient to say, that requirements engineering provides the best compromise in the

field of goal-oriented process modeling within the scope of existing modeling

technique. However, the complementary nature of ARIS and value-focused thinking

methodologies suggests itself to an integrated approach that would provide the best of

both worlds.

By integrating value-focused thinking and ARIS approaches, we provide a decision

perspective on goal-oriented process modeling (for more detail refer to [5] and [17]).

Within this perspective, decision analysis tools, and in particular, the “value-focused

thinking” framework is used to identify and structure objectives of business processes

that are represented using ARIS methodology. The resulting conceptual model and

implementation framework facilitate expression of each goal in some aspect of a

process model by:

1. Modifying the “value-focused thinking” framework to include logical relationships

that are not currently available within the model.

2. Structuring functional and process objectives within the ARIS framework as a

means-ends network using Keeney’s principle for identification and linkage

between the objectives.

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3. Using the means-ends network to guide the decomposition of the business process

while taking advantage of the hierarchical and nested models functionality

available within ARIS.

The integration of these two methodologies within a single model, builds on the

strength of these methodologies while addressing the shortcomings identified within

them. Among advantages of using the proposed model are: a rational approach

towards process decomposition that facilitates achievement of business objectives by

business processes; an ability to integrate a vast library of decision models into

process modeling to address both efficiency and effectiveness objectives of business;

and access to the process modeling capabilities of widely adopted software

applications.

5 Summary

As is clear from the modeling literature, “the suitability of a modeling approach will

depend on the goals and objectives for the resulting model. A given language

construct or type will be better suited to achieving some modeling objectives than

others” ([38], p.86). None of the models discussed in this paper aim to be universal or

the only “correct” goal-oriented approach to process engineering. Each model is

suited to its particular environment and objectives. That aside, understanding of the

available goal-oriented process engineering approaches provides opportunities for

collaboration in addressing outstanding research problems and minimizes the

duplication of effort that can result from a lack of coordination.

It is hoped that this paper goes some way towards informing various research

communities of the development in the field of goal-oriented process engineering by

providing a coherent framework for the evaluation of various fields, pointing to the

current research in this area within each field and identifying future research

directions that include but are not limited to:

− a more comprehensive framework for evaluation of goal models in the context of

business process engineering;

− a more in in-depth analysis of goal-oriented process modeling methods using

existing process modeling evaluation frameworks;

− an application of MCDA to evaluation of goal-oriented process modeling with the

aim of developing an easy to use tool for practitioners looking to choose or assess

available techniques;

− further development of an integrated methodology aiming to utilize the best of both

goal and process modeling.

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