An Evaluation of the REA Framework as an Enterprise Domain Ontology: Does the REA
Framework Support Balanced Scorecard information Requirements
Kim Church
Ph.D. Student
Sam M. Walton College of Business
University of Arkansas
Rod Smith
Assistant Professor
Sam M. Walton College of Business
University of Arkansas
October 31, 2004
Version 1.1
ABSTRACT
Geerts and McCarthy (2001b, 2002) proposed the extended resource-event-agent
(REA) framework as an enterprise domain ontology. The REA conceptual accounting
framework was designed to describe the information architecture related to an
organization’s economic activity (e.g., McCarthy 1982, Dunn et al. 2005). Geerts and
McCarthy (2001b, 2002) extend the original REA to include value-chain level
configurations, task-level configurations, and encompass a broader array of business
economic phenomena. They note, however, that future research is needed to refine the
“REA components into a more complete enterprise ontology” (Geerts and McCarthy
2002, p. 2). In this paper, we examine the REA framework to identify those components
that are missing or must be better defined to provide a more complete enterprise
ontology.
Traditional accounting systems as well as current enterprise resource planning
systems are often ill-equipped to capture and report nonfinancial information. The
effective and efficient managerial use of nonfinancial information requires that it be
integrated into existing enterprise information systems. We therefore specifically address
whether the REA ontology encompasses nonfinancial information requirements, and we
employ the balanced scorecard as the vehicle by which we examine the structure, source,
and use of nonfinancial measures in an enterprise domain.
2
1. INTRODUCTION
Geerts and McCarthy (2001b, 2002) proposed the extended resource-event-agent
(REA) framework as an enterprise domain ontology. Although ontology has its origins in
philosophy, researchers are applying ontological theories to improve the design of
information systems. In an enterprise systems context, an ontology expresses formal
definitions of the set of terms, entities, objects, classes and the relationships between
them in an enterprise information system (e.g., Zuniga 2001, Pouchard et al. 2000). The
REA conceptual accounting framework was designed to describe the information
architecture related to an organization’s economic activity (e.g., McCarthy 1982, Dunn et
al. 2005). Geerts and McCarthy (2001b, 2002) extend the original REA to include valuechain level configurations, task-level configurations, and encompass a broader array of
business economic phenomena. They note, however, that future research is needed to
refine the “REA components into a more complete enterprise ontology” (Geerts and
McCarthy 2002, p. 2). In this paper, we examine the REA framework to identify those
components that are missing or must be better defined to provide a more complete
enterprise ontology.
Recently, there has been increased emphasis on nonfinancial measures both for
external reporting and internal management (e.g., Eccles et al. 2001, Kaplan and Norton
1992, 1996a, 1996b, Lev 2001). Traditional accounting systems as well as current
enterprise resource planning systems are often ill-equipped to capture and report
nonfinancial information. The effective and efficient managerial use of nonfinancial
information requires that it be integrated into existing enterprise information systems. We
3
therefore specifically address whether the REA ontology encompasses nonfinancial
information requirements.
Although a number of management tools employ nonfinancial measures, we
selected the balanced scorecard as the vehicle by which we examine the structure, source,
and use of nonfinancial measures in an enterprise domain. We argue that the balanced
scorecard employs a broad range of nonfinancial measures that encompasses those used
in other prominent management tools, such as total quality management, six sigma
management, value-based management, and customer relationship management. The
balanced scorecard is a popular management tool for developing and implementing
strategy. Bain & Company’s annual management tools survey recently found that 72
percent of respondents use the balanced scorecard (Bain & Company 2004).1 To establish
a balanced scorecard, organizations develop strategy maps to identify causal
relationships, establish appropriate objectives, and determine corresponding performance
measures (Kaplan and Norton 1992, 1996a, 1996b, 2000a, 2000b, 2004).
Using the balanced scorecard requires collecting both financial and nonfinancial
performance data from throughout the organization to compare against objectives over
time. The potential for dynamic changes in the performance measures and the need to
integrate information across business processes complicates the design of balanced
scorecard systems. Consequently, most organizations find that their current enterprise
systems do not capture all the required data.2 If balanced scorecard information
1
Rigby (2001) reports 49 percent of respondents used the balanced scorecard in 1999.
In a 2001 survey by IDC and the Balanced Scorecard Collaborative, survey respondents from a broad
range of industries stated that complex data sourcing remains the single biggest challenge to automating
balanced scorecards, followed by the unavailability of the needed source data (Williams 2004).
2
4
requirements are to be integrated into enterprise systems, then those requirements should
articulate with the REA framework.
Our work contributes to the literature on conceptual accounting systems design.
The REA framework has an established heritage, but it may be limited in its ability to
describe the broader range of economic activity related to strategic management and
strategy implementation. The REA framework was designed to provide a conceptual
model of accounting systems in a database world. O’Leary (2002) found many
similarities between the REA framework and SAP’s enterprise resource planning system.
He noted, however, that SAP includes extensions that are not yet part of the REA model.
SAP includes balanced scorecard capability within its strategic enterprise management
module, thus this research may help define extensions to the REA framework to
accommodate that difference. Additionally, every organization implementing a balanced
scorecard must develop its nonfinancial measures, determine the source of those
measures, and link its objectives to business processes, but we found little research that
provides a theoretical approach to that process.3
We proceed as follows. In the next section, we review ontological theory as
applied to information systems. Then in section three, we summarize the concepts of the
REA ontology. In section four, we review the information requirements of the balanced
scorecard. In section five, we evaluate whether the REA ontology supports balanced
scorecard information requirements. We then conclude and describe opportunities for
future research.
3
There are, of course, a number of consulting firms with prescribed methodologies.
5
2. ONTOLOGY BACKGROUND
2.1. Purpose and goals of an ontology
In a search for a theoretical foundation for information systems design and
modeling, a number of researchers have borrowed concepts from metaphysics. For
example, Wand and Weber (1990, 1993) proposed a formal model of an information
system within the context of a theory of ontology. Ontology is a branch of metaphysics
that deals with systems structure (McComb 2004). As it relates to information systems,
an ontology articulates the constructs needed to describe particular types of phenomena
that occur in some domain (Wand and Weber 2004).
Ontologies describe things that exist in a problem domain. This includes
properties, concepts and rules, and how they relate to one another, which supports a
standard reference model for information integration as well as knowledge-sharing
(Linthicum 2004). The key to efficiently managing data lies in establishing a common
understanding, and an ontology defines the common words and concepts used to describe
and represent an area of knowledge (Obrst 2003, Schreiber 2003). Ontological theories
impose order on domain phenomena and help us describe the structure of the domain and
relations between objects therein (Weber 2003, Zuniga 2001).
Kim et al. (1999) note that business questions drive the requirements for ontology
design. The goal of ontology-based enterprise modeling is the implementation of an
environment that supports the modeling and design of enterprises. Well defined
ontologies should facilitate the accurate communication critical to the successful
implementation of enterprise systems, because they provide a common understanding of
6
data and processes that exist within a problem domain (Linthicum 2004, Uschold et al.
1997).
3. THE RESOURCE-EVENT-AGENT ONTOLOGY
3.1. REA background
McCarthy (1979, 1982) built on Chen’s (1976) entity-relationship concepts to
create a generalized accounting framework applicable to integrated enterprise systems.
The REA framework abandons debits, credits, and traditional account structures as
artifacts associated with the mechanics of journals and ledgers in standalone bookkeeping
systems. Instead, the REA framework characterizes accounting phenomena in terms of
economic events and the associated enterprise resources and agents. Resources are
defined as things of economic value that are provided or consumed by an enterprise’s
activities and operations. Thus, resources can be considered generally equivalent to
accounting definition of assets. Agents are the persons, organizations, or organizational
units that control or effect economic events. Economic events are the activities that
increase or decrease enterprise resources. The procedural features of the REA framework
allow materializing conclusions, such as periodic financial reports, by deriving
information, decomposing and combining events, and matching expenses to revenues at
the macro level (McCarthy 1982).
3.2. REA Ontology
Geerts and McCarthy (2001b, 2002) propose an extended REA framework as an
enterprise domain ontology. The REA ontology extends the original REA framework to
include “a full accountability infrastructure for a firm (Geerts and McCarthy 2002, p. 5).”
7
In essence, the REA ontology provides a top-down decomposition of the enterprise value
chain. At the top level, the value chain level REA model identifies the business
processes—transaction cycles defined by the dualities of associated economic events—
and the resource flows between processes. The value chain REA model is then
decomposed into the business process level REA model, which closely resembles the
original REA framework. The process level REA model is then decomposed into a task
level REA model that specifies the logical sequence of activities necessary to carry out
the economic events defined at the higher levels.
The REA Ontology also includes extensions to the original REA framework at the
process level. In particular, Geerts and McCarthy (2001b, 2002) introduce additional
events that are not normally included in traditional accounting records but do play
important roles in commercial enterprise systems.4 For example, they include
commitment events, which are agreements to engage in economic events in the future.
Commitment events are linked to other commitment events in a “reciprocal” relationship
corresponding to the duality relationships between economic events. Additionally, they
introduce an abstraction relationship termed “typification” which links resource, event, or
agent entities to a knowledge-level grouping, a type image. Type images and the
relationships among type images create a policy infrastructure that “conceptualizes what
‘could be’ or ‘should be’ within the context of a defined portfolio of firm resources and
capabilities” (Geerts and McCarthy 2002, p. 6).
Geerts and McCarthy distinguish among three variations of type images:
standards, policies, and budgets. A standard represents an engineered specification, such
“Enterprise systems” is intended to include commercial software packages called enterprise resource
planning (ERP) systems from SAP, Peoplesoft, Oracle, and others, as well as similar integrated software
systems.
4
8
as a recipe or bill of material. A policy implements organizational requirements and
constraints. A budget represents a target or goal for a specific time period. Type level
entities create organizational information or knowledge overlay to the operational level
REA model. The REA type level therefore must provide the structure for to support the
implementation and tracking of organizational strategic initiatives. Figure 1 describes the
different REA levels and type images.5
4. THE BALANCED SCORECARD INFORMATION REQUIREMENTS
4.1. Balanced scorecard overview
Kaplan and Norton (1992, 1996a, 1996b) designed the balanced scorecard as an
alternative to the exclusive use of financial measures to manage company performance.
The balanced scorecard is now a well-known and broadly-used management tool.
Reportedly, over 60% of Fortune 1000 companies used balanced scorecard systems by
2001 (Bourne 2002).
The balanced scorecard retains financial measures, such as profitability, growth,
and shareholder value, but it also includes the nonfinancial measures that drive the
financial results. Managers use the balanced scorecard to look at their business from four
perspectives: the customer perspective, internal business perspective, learning and growth
perspective, and the financial perspective.6 A balanced scorecard system consists of
objectives and corresponding performance measures for each of the four perspectives.
5
Figure 1 is based on figure 2 in Geerts and McCarthy (2002).
See Kaplan and Norton’s three books, three articles in Harvard Business Review, and two articles in
Accounting Horizons, among other publications for a complete description of the balanced scorecard
framework.
6
9
The objectives and measures are linked together so that short-term actions support longterm strategic objectives (Kaplan and Norton 1996a).
The balanced scorecard also complements other widely-used management
systems, such as shareholder value management systems, and activity-based costing
(Kaplan and Norton 2001a). Balanced scorecard measures for the financial perspective
decompose residual income into elemental measures of cost reduction, asset productivity,
and revenue growth. Activity-based costing can improve the quality of operational
measures for the balanced scorecard internal process perspective.
4.2. Balanced scorecard information requirements
Kaplan and Norton (1996a, 1996b) describe four processes for managing strategy
with the balanced scorecard. The first process is translating the vision, expressing the
organization’s strategy as an integrated set of objectives and measures that describe the
long-term drivers of success. The second process is communicating and linking,
communicating those objectives throughout the organizational hierarchy and establishing
departmental and individual objectives that link to the overall strategic objectives. The
third process is business planning, integrating the business and financial plans. The fourth
process is feedback and learning, monitoring the performance measures and the
relationships to strategic goals.
These processes entail the decomposition of strategic objectives and building a
system of both financial and nonfinancial measures at all levels of the organization. The
challenge is to make explicit links between operations and finance to integrate the
nonfinancial measures with traditional financial measures. Kaplan and Norton (1992, p.
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75) note that, “If the information system is unresponsive, however, it can be the Achilles’
heel of performance measurement.”
4.3. Balanced scorecard systems
A key assumption in the literature about the balanced scorecard is that the
performance measurement data required for using the performance management
framework is readily available (Williams 2004). However, the balanced scorecard
imposes complex data requirements; many measures can’t simply be aggregated and
disaggregated. Absent an approach to integrate the balanced scorecard requirements into
existing enterprise systems, “the implementation of a balanced scorecard would resemble
the all-too-common rapid deployment of a standalone application” (Williams 2004).
In 2000, the Balanced Scorecard Collaborative published functional standards for
balanced scorecard systems.7 Those standards specify that balanced scorecard software
should allow the description of perspectives, objectives, measures, targets, and strategic
initiatives, and it should also allow users to establish specific cause-and-effect linkages
among various objectives, associate measures with objectives, associate targets with
measures, and link strategic initiatives to one or more objectives. The standards do not,
however, require that the balanced scorecard software be integrated into an organization’s
financial or enterprise systems.
4.4. An entity-relationship model of a balanced scorecard system
We located material on the Balanced Scorecard Institute web site that describes a
generic data model for a balanced scorecard application. We compared that data model
against descriptions of balanced scorecard applications from several of the certified
7
The functional specifications are available at http://www.bscol.com/bsc_online/technology/standards/.
11
vendors, e.g., SAP, Peoplesoft, Microsoft, and SAS. Our impression is that the data
model accurately represents the structure of the typical balanced scorecard software.
Figure 2 describes the generic balanced scorecard data model. As described in the
functional specifications, a balanced scorecard system includes perspectives, objectives,
measures and targets. It incorporates initiatives, links the initiatives to the owner of the
initiative, and links the owner to the organizational unit. It does not, however, link these
objects to the underlying business processes. Thus, misinterpretations can lead to issues
of data quality (e.g., Wand and Weber 1996).
5. EVALUATING THE REA FRAMEWORK AS AN ENTERPRISE
DOMAIN ONTOLOGY AGAINST BALANCED SCORECARD
INFORMATION REQUIREMENTS
5.1. Criteria for evaluating ontologies
Although there is substantial research on the application of ontological theories to
information systems, there is relatively little research that describes the criteria for
evaluating information systems ontologies. The existing research appears to provide two
approaches to evaluating ontologies. One is the more general approach that evaluates
ontologies based on broad criteria (Gruber 1993, Uschold and Gruninger 1996, Fox et al.
1998). The second approach is to evaluate ontologies against the information
requirements of a specific domain (Gruninger et al. 1997, Kim et al. 1997).
As one example of the broad approach, Gruber (1993) argues that ontologies
should be evaluated against objective design criteria. He presents five broad design
criteria for ontologies whose purpose is knowledge sharing:
12
1.
Clarity: effectively communicates the meaning of defined terms.
2.
Coherence: allows inferences only consistent with the definitions.
3.
Extendibility: able to be extended and specialized without requiring the
revision of existing definitions.
4.
Minimal encoding bias: depends little on a particular symbol-level
encoding.
5.
Minimal ontological commitment: makes as few claims as possible about
the world modeled.
Uschold and Gruninger (1996) also present guidelines for developing ontologies
based on their experience in developing the enterprise ontology. They highlight three
broad criteria for evaluating ontologies.
1.
Clarity: effectively communicate the intended definitions.
2.
Coherence: internal consistency.
3.
Extensibility: offers a conceptual foundation for a range of anticipated
tasks.
Fox et al. (1998) also describe evaluation as problem in engineering of ontologies.
They highlight criteria to evaluate ontologies that are quite similar to the five presented
by Gruber.
1.
Functional completeness: represents the information necessary to support
some task in the domain.
2.
Generality: provides shared versus specific definitions.
3.
Efficiency: supports efficient reasoning without transformations.
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4.
Perspicuity: easily understood by the users so that it can be consistently
applied and interpreted across the enterprise.
5.
Precision/granularity: provides a core set of ontological primitives that are
partitionable and do not overlap in meaning; supports reasoning at various
levels of abstraction and detail?
6.
Minimality: contains the minimum number of objects (terms or
vocabulary) necessary.
There are clearly similarities among these three sets of criteria for evaluating
ontologies. We see major common themes as clarity, coherence, completeness, and the
ability to be extended. We argue that the long history of the REA framework both in the
classroom and in research strongly indicates that the theory is clear and coherent. We
therefore concentrate on whether the REA framework is complete, i.e., does it represent
the information necessary to support the enterprise domain in a balanced scorecard
context, and is the REA framework extendable to include balanced scorecard
requirements, i.e., can it be extended to include appropriate nonfinancial objectives and
measures.
5.2. Does the REA Ontology support balanced scorecard information requirements?
Gruninger et al. (1997) argue that existing ontologies can be evaluated for
completeness by determining which requirements from a comprehensive set of process
requirements are supported. Kim et al. (1999) also note that business questions drive
ontology competency questions and therefore the requirements for ontology design. We
therefore examine the balanced scorecard information requirements as the standard
against which we evaluate the REA ontology.
14
Table 1 provides a listing of the REA components and the definitions of those
components. Based on Chen’s (1976) original entity-relationship theory, the REA
framework has been extended to encompass the array of enterprise operating activities.
Certainly, we expect that the REA framework supports aggregate financial measures that
are typically included in the balanced scorecard’s financial perspective. The REA
framework appears limited, however, in its ability to describe elements of the balanced
scorecard’s customer perspective, learning and growth perspective, as well as those value
chain processes that Porter (1985) describes as support processes.
Figure 3 presents the REA value chain structure within a balanced scorecard
strategy map. The existing description of the REA framework covers operating processes,
but it is not clear that it covers the other important balanced scorecard processes. The
balanced scorecard emphasizes innovation processes, customer management processes,
and regulatory and social processes, in addition to the operational processes (e.g., Kaplan
and Norton 2004, figure 3). These processes contribute to the organization’s customer
value proposition, which affects customer satisfaction, acquisition, retention, and
profitability. Increased customer growth and profitability drives the organization’s
revenue growth, productivity, and profitability. All processes also require resources and
generate costs and therefore affect organizational productivity and profitability.
Table 2 lists example balanced scorecard information requirements for all the
perspectives. For each information requirement, we determined whether that information
is supported, or could be supported, by the REA framework as presented in existing
research by McCarthy (1979, 1982), Geerts and McCarthy (2001b, 2002), and the
15
textbook by Dunn et al. (2005). If the information could be supported, we also identified
the likely source of that information in the REA framework.
As shown in Table 2, we believe that the financial information requirements of
the balanced scorecard are supported by the REA framework. The summary financial
measures can be provided by what McCarthy (1982) terms “conclusion materialization”
from the underlying processes. Additionally, many of the customer perspective
information requirements are supported as properties of sales process external agents, i.e.,
the customer or customer type images. We do not believe that broader measures of
market attributes, e.g., market share, are supported in the current descriptions of the REA
framework. It is possible, however, that the market could be represented as the set of
customers and potential customers of the firm, and with that extension, it would be
possible to support the market-oriented information requirements. Additionally, we
suppose that customer satisfaction measures could be supported as properties of the
customer agent, but there are many elements of the value proposition that affect customer
satisfaction. Customers could be satisfied with some products, some salespersons, and
some sales transactions, but not others. Thus, we believe that customer satisfaction
information is a complex construct that is not clearly supported by the REA framework.
Table 2 also shows that the REA framework is least likely to support the
information requirements of the learning and growth perspective. The REA framework
includes type images that describe knowledge level specifications, such as standards
(Geerts and McCarthy 2003). But, Geerts and McCarthy define a standard as an
engineered specification, e.g., a bill of materials, “for something like a product.” The
elements of human capital, information capital, and organization capital are complex and
16
dynamic intangible constructs, which do not appear to be supported fully by the REA
type image component.
In summary, many of the balanced scorecard information requirements, especially
the financial information requirements, are—or could be—supported by the existing REA
ontology. Many of the softer, nonfinancial, information requirements are not supported
within the existing definitions of the REA ontology.
5.3. Does the REA Ontology support the strategic structure of a balanced scorecard?
The balanced scorecard supports a top-down approach to strategy implementation.
Organizations develop balanced scorecards by translating a high level set of strategic
objectives into an integrated set of objectives that cascade down through the
organizational hierarchy (Kaplan and Norton 2004). The balanced scorecard objectives
are linked in a causal chain within and across perspectives.
The REA framework currently only offers type images as the vehicle for
modeling organizational policy (Geerts and McCarthy 2001b). Yet, Geerts and McCarthy
(2001b, p. 21) recognize that further extensions to the REA architecture are necessary to
“transform such an infrastructure into an enterprise system useful for a much wider range
of managerial planning and control.”
Uschold et al. (1997) describe other efforts to create an enterprise ontology. The
major sections of their enterprise ontology include:
1.
Meta-ontology: entity, relationship, role;
2.
Activity, plan, capability, and resources: processes and planning terms;
3.
Organization: relating to how organizations are structured;
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4.
Strategy: terms related to high level planning, e.g., purpose, mission,
decision, critical success factor; and
5.
Marketing: terms related to marketing and selling goods and services, e.g.,
sale, customer, price, brand, promotion.
Uschold et al. (1997) go on to develop specific definitions for such terms as
activity, plan, capability, skill, strategy, purpose, influence factor, critical success factor,
and decision. The REA framework provides type images and the links among type
images as the only knowledge level construct to support this broad array of concepts.
Incorporating these elements into the REA framework would improve clarity and likely
address many of the balanced scorecard strategic elements.
6. CONCLUSION AND FURTHER RESEARCH RECOMMENDATIONS
Geerts and McCarthy (2001b, 2002) proposed the extended resource-event-agent
(REA) framework as an enterprise domain ontology. In this paper, we examine the REA
framework to identify those components that are missing or must be better defined to
provide a complete enterprise ontology. Specifically, we examined whether the REA
framework supports the information requirements of the balanced scorecard. We selected
the balanced scorecard as one prominent example of the trend toward broader use of
nonfinancial measures for both internal management and external reporting.
Organizations face significant challenges when implementing integrated
enterprise systems or pursuing comprehensive strategic initiatives like the balanced
scorecard. The success of such initiatives depends on how well the requirements are
defined and communicated throughout the organization. As we noted earlier, well defined
18
ontologies should facilitate the accurate communication critical to the successful
implementation of enterprise systems, because they provide a common understanding of
data and processes that exist within a problem domain (Linthicum 2004, Uschold et al.
1997).
The REA framework represents a widely accepted conceptual accounting
framework. Our analysis finds that the REA framework supports many of the balanced
scorecard information requirements but not all of those requirements. The REA
framework remains closely tied to its accounting roots. One important area for further
study is how to extend the REA architecture to include the strategic management
requirements, including the use of nonfinancial measures, that are embodied in the
balanced scorecard and similar management systems. Our research identifies areas where
the REA framework could be extended to support broader issues of managerial planning
and control.
19
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23
FIGURE 1
REA Levels and Type Images
(adapted from Geerts and McCarthy 2002, fig. 2)
Policy Infrastructure
Type Images
Accountability Infrastructure
Actual Business Events
REA Value
Chain Specification
REA
Process
REA
Process Ty pe
is_part_of
REA Process
Specification
is_part_of
Agent
Resource
Event
E Type
Commitment
C Type
decomposes_into
decomposes_into
REA Task
Specification
Task-1
A Type
R Type
governs
Task-2
Task-3
Task-4
Task Type-1
Task Type-2
Task Type-3
Task Type-4
24
FIGURE 2
Generic Balanced Scorecard Data Modela
Owner
OO
OH
Perspectives
Objectives
PO
Causal
Links
OI
OM
Initiative
IM
Measures
MT
Targets
Owner = person or organizational unit accountable for balanced scorecard objectives.
Perspectives = balanced scorecard perspectives: financial, customer, internal process, and
learning and growth.
Objectives = performance objectives set by the owner for each perspective; objectives are
related to other objectives according to causal relationships.
Initiative = specific strategic initiative(s) designed to improve performance.
Measures = financial and nonfinancial measures related to specific objectives.
Targets = target values for specific measures for specific periods.
a
Adapted from Balanced Scorecard Institute example relational data model for the
Balanced Scorecard
25
FIGURE 3
REA Value Chain Embedded In Balanced Scorecard Strategy Map
Financial
Perspective
Customer
Perspective
Productivity
Strategies
Long Term Shareholder
Value
Product Attributes
Growth
Strategies
Relationship
Customer Value
Proposition
Image
Service Attributes
Customer Management
Process
Operation Management Process
Cash
Raw Material
& Overhead
Acquisition &
Payment
Process
Ma
nu
f
Go actu
od red
s
Cash
Cash
Labo
r
Internal
Perspective
Revenue
Process
Payroll
Process
Financing
Process
Conversion
Process
ry
nto
e
Inv
Learning &
Growth
Perspective
Human Capital
Innovation
Process
Information Capital
26
Regulatory &
Social
Processes
Organizational Capital
TABLE 1
REA Terms and Definitionsa
Term

Ontology
Conceptualization

REA Ontology

Value chain

Business Process
Enterprise Script
















Process
Conclusion
Materialization
Claims
Stock
Flow
Entity
Economic Resource (R)
(asset)
Economic Event (E)
Exchange
Task
Recipe
Transformations
Transfers
Economic Agent (A)

Economic Units
Internal Agent

External Agent

Commitment Images

Instigation Event

Type Images

Standards

Definition
explicit specification of conceptualization
objects, concepts, and other entities that are assumed to exist in
some area of interest and relationship among them
an enterprise domain ontology built around an extended resourceevent-agent (REA) conceptual accounting model
a set of business processes through which resources flow, with the
assumption that value is added to the resources within each business
process
a set of business activities that corresponds to a transaction cycle
series of processes with each being further exploded into an
exchange specification from which itself is derived a script of lowlevel tasks needed to accomplish the exchange
exchange and tasks needed to execute the exchange
producing information "snapshots" from records of continuing
activities
future assets
balance sheet account
income statement account
person, object, or happening modeled in a database
things that are scarce, have utility, and under control of the
enterprise
class of phenomena which reflect change in economic resources
a trade of resources between two parties
specific components of an exchange
ordered sequence of tasks
create value through changes in form or substance
create value in market transaction with outside parties
persons and agencies participating in economic events or
responsibilities
subset of inside economic agents
persons, agencies, or organizational units performing activities in a
process that are inside the organization and responsible for a
particular event
persons, agencies, or organizational units interacting with an
organizational process from outside the process and participating in
a particular event
agreement to execute an economic event in a well-defined future
that will result in either an increase of resources or a decrease of
resources
preliminary contacts between internal agents and external agents,
such as marketing activity, information requests.
represent intangible structure of economic phenomena, such as
grouping or characterization at an abstracted, knowledge, level
an engineered specification, such as a blueprint or a recipe
27















Prototypes
Policies
Budget
Characterizations
Relationship
Association Relationship
Generalization
Relationship
Stock-Flow Relationship
Duality Relationship
Participation
Relationship
Control Relationships
Responsibility
Relationship
Linkage Relationship
Cooperation
Relationship
Composite Relationship

Custody Relationship
Reservation
Relationship
Fulfillment Relationship

Partner Relationship

Reciprocal Relationship


Typification
Abstraction

Ordering


blueprint of the configurations of the actual phenomena
abstractions that restrict the legal configurations of the actual
phenomena
target or goal for a specific time period
informative type-image relationships
association between two or more entities
dependencies between agents
relate different sub sets/types of entities to generalized
type/supersets
connect appropriate elements of the resource and event of enterprise
Links event that increments a resource with corresponding
decrement event for a process
links inside/outside parties to economic events
association between resource, inside agent, and outside agent
higher level units control and accountability for activities of
subordinates
dependencies between resources
existing dependencies between external agents
a resource (whole) as an aggregate of two or more other resources
(parts)
internal agent responsible for specific resource
link between commitment event and resource/resource type that
schedules inflow and outflow of resources
link between commitment event and subsequent economic event
special participation relationship that describes outside agents
participating in commitments
association between commitment events corresponding to the
duality relationship between economic events
descriptions that apply to a group of actual phenomena
mental process to characteristics and properties of a set of objects
and exclude other irrelevant characteristics
dependency between two tasks
a
Sources: Batini et al. (1992), Dunn et al. (2005), and the series of articles by Geerts and
McCarthy, and McCarthy.
28
TABLE 2
Evaluating Whether Balanced Scorecard Information Requirements Are Included
in the REA Framework
Perspectives/Objective
Categoriesa
Sample
Objectives/Measure Type
Financial Perspective (figure 2-3)
Productivity Strategy
Improve cost
Reduce cash expense
structure
Increase asset
utilization
Growth Strategy
Expand revenue
opportunities
Enhance Customer
Value
In REA
Frameworkb
Yes
Invest to eliminate
bottlenecks
Possible REA Component
Conclusion materialization:
Resources, Events, Internal
Agents all processes
Maybe
Conclusion materialization:
Resource acquisition events after
determination of bottleneck
locations
New sources of revenue
Yes
Improve profitability of
existing customers
Yes
Conclusion materialization: Sales
Process
Conclusion materialization: Sales
Process
Customer Perspective (figure 2-4)
Customer satisfaction
Customer
profitability
Market share
Account share
Customer acquisition
Customer retention
Percent satisfied
Percent unprofitable
No
Yes
Percent market share
Percent account share
Conversion rate
No
No
Yes
Customer lifetime value
Yes
Customer value proposition
Product/service
Price, quality, availability,
attributes
selection, functionality
Relationship
Service, partnership
Image
Brand image
Internal Perspective (figure 3-2)
Operations Management (figure 3-2)
Develop supplier
Supplier ratings: quality,
relations
delivery, cost
Produce goods and
Cost per unit of output
services
Distribute to
ABC costs of storage and
customers
delivery to customers
29
Maybe
Conclusion materialization: Sales
Process
Conclusion materialization: Sales
Process
Conclusion materialization: Sales
Process
Resource or Resource Type
properties
No
No
Yes
Agent/Agent Type properties
Yes
Conclusion materialization
Yes
Conclusion materialization:
Distribution process
Manage risk
Percent of capacity from
existing and backlogged
orders
Customer Management Process (figure 4-6)
Customer selection
Target high-value
customers
Customer acquisition Communicate value
proposition
Customer retention
Service excellence
Customer growth
Customer education
a
b
Conclusion materialization and
Agent/Agent Type properties
Instigation Event
Agent/Agent Type properties
Instigation Event and
Agent/Agent Type properties
No
No
Maybe
Number of new products
launched
Learning and Growth Perspective (figure 7-1)
Human capital
Strategic competencies:
availability of skills, talent,
knowledge
Organization capital
Maybe
No
Maybe
Regulatory and social processes (figure 6-2)
Environment
Energy consumption
Safety and Health
Incidence rates
Employment
Diversity
Community
Community investment
Information capital
Conclusion materialization: Sales
Process Resources, Commitment
Events, Reservation
Relationships
Maybe
Innovation Management Process (figure 5-2)
Identify opportunities Number of new projects
Manage the portfolio
Net present value of
of projects
projects in pipeline
Design and develop
Number of patents
Launch
Maybe
Strategic information:
availability of information
systems, knowledge
applications and
infrastructure
Culture, Leadership,
Alignment, Teamwork
Maybe
Resource/Resource Type
properties
Resource/Resource Type
properties
Yes
No
Yes
Maybe
Stockflow relationship
Maybe
Agent/Agent Type property
Agent/Agent Type property
Conclusion materialization
No
No
Parenthetical information are references to figures in Kaplan and Norton (2004) book.
Yes = example available in or can be implied by prior REA research;
Maybe = reasonable extension to REA examples;
No = no example in REA research and no clear avenue for inclusion.
30