The
REA Accounting Model: Intellectual Heritage and Prospects for Progress
For more than 50 years, researchers
and practitioners have noted the inability of accounting systems to facilitate
non-financial decisions (Goetz 1939; Firmin 1966; Fisher 1994). In fact, this
problem has contributed to what is considered to be a state of crisis in
accounting systems (Andros, Cherrington and Denna 1992; Cushing 1989; Dunn and
McCarthy 1991; Elliott 1992). The recurring theme in these studies is the need
for accountants to change their role in organizations. Rather than providing
only the services of producing financial statements and policing the control
policies of firms, the accountant must become a business partner striving to
meet all of the firm's information needs. Database accounting in general (for
example, Colantoni, Manes and Whinston 1971; Geerts and McCarthy 1992) and the
Resources-Events-Agents (REA) accounting model more specifically (McCarthy 1982)
have been proposed as means of recording and storing accounting information in
such a way that transaction details are available for non-accounting decisions.
Maintenance and use of such accounting systems moves the corporate accountant
toward the role of a full business partner and manager of the economic
activities of an enterprise.
McCarthy (1981) reviewed the general
nature of work done on multidimensional and disaggregate accounting systems.
Since that time, however, inconsistencies in the perceived origin and nature of
this work have arisen, such as the mistaken belief that REA modeling involves
simply the use of database technology to implement the ideas of Sorter (1969)
on events accounting and of Ijiri (1975) on certain aspects of
accounting measurement fundamentals, particularly causal double entry.
As use of the REA accounting model in AIS research increases (Schneider 1995;
Leech 1995), it is important to dispel such misconceptions. Conflicting and
overlapping use of the terms Events Accounting, Database Accounting, and REA
Accounting also indicates a need for clarification and differentiation of these
models. Making this distinction is one objective of this article. A second
objective is to identify, based on the clarification of the models, potential
research areas.
Section I discusses the intellectual
heritage of the accounting approaches. Section II identifies equivalent or
overlapping aspects of the approaches. Section III delineates the aspects of
each approach that have been subject to validation and the results of such
efforts. Section IV discusses research directions. Section V offers
conclusions.
I.
INTELLECTUAL HERITAGE
Events
Accounting
Sorter (1969) coined the term
"events accounting" as a solution to problems with the conventional
approach to accounting (which he labeled the "value theory" ). He
clarified his definition of the events approach with two operational rules
(Sorter 1969, 16):
A balance sheet should be so constructed as to
maximize the reconstructability of the events being aggregated.
Each event should be described in a manner facilitating the forecasting
of that same event in a future time period given exogenous changes.
While
Sorter advocated less aggregation than was present in financial statements
then, examination of his rules and his later textbook (Sorter et al. 1990)
reveals that he was not advocating storage and maintenance of transaction level
detail. He defined his events approach to accounting as stressing "the
determination of accounting events from financial statements" (Sorter et
al. 1990, 107). He suggested it is necessary to know the changes in the balance
sheet accounts in order to deduce events. His was a reporting method rather
than a proposal to reorient transaction processing systems.
After Sorter proposed his theory,
Johnson (1970) defined several of its concepts more rigidly. He hinted at the
need for multidimensional tracking of events by declaring that user forecasting
capabilities "would be enhanced if the [event] reports were to include
observations other than the monetary characteristic" (p. 649).
Seven years prior to Sorter's
article, Schrader (1962) touched on
many of the same issues addressed by Sorter and Johnson. Schrader's article is
not recognized by many as events accounting, perhaps because he did not
emphasize the term events. However, the content focuses on the recording
and storing of the details of events. He claimed to be applying Goetz's (1939)
notion of a Basic Historic Record to the accounting domain, emphasizing the
need for accountants to focus on the objects given and received and to record
the who, what, when, and where for each relevant event. Relevant events were
defined as exchanges. In a textbook (1981) co-authored with Malcolm and
Willingham, Schrader emphasized the difference between observed data recorded
in an exchange and other analysis or manipulation of the data. In a section
entitled "Basic Historic Record" he suggested that it may be
desirable to furnish different accounting statements to various users,
depending on their desires. However, the ensuing discussion indicates that what
Schrader meant by a basic historical record was not a data bank of primitive
raw data (as Goetz intended), but simply a separation of accrual accounting
entries from entries representing transactions.
Database
Accounting
The idea of using databases or
similar innovations in accounting and financial reporting is even older than
events accounting concepts. Goetz (1939, 1949) criticized accounting because of
its inability to support management functions. He argued that accountants are
not qualified to select, classify, or measure business phenomena unless they
fully understand the nature of the issues to be decided. At the same time,
users cannot evaluate information unless they fully understand the methods used
to produce the information. In addition, Goetz argued that multiple values
should be recorded since "different answers serve different purposes or
fit different situations" (1939, 152).
As a solution, Goetz proposed the
creation of a Basic Historic Record or Basic Pecuniary Record that would be an objective record of occurrences
(transactions) indicating what was obtained and surrendered by the company
including the date of the transaction. Adjustments necessary for legal or for
financial accounting purposes may be made as supplements to the record, but
they would not permanently alter the record itself. The main requirement of the
proposed system was flexibility. Goetz's goal was to preserve the original data
in its most primitive form so it could be organized in the most appropriate
form for each decision maker.
While Goetz was advocating the
maintenance of a Basic Historic or Pecuniary Record in the American literature,
Schmalenbach was making similar arguments in Germany (Schweitzer 1992). Back-Hock (1995) discussed Schmalenbach's
ideas and noted that he coined the term Grundrechnung for the collection
of data necessary in such an accounting system. According to Back-Hock, the
Grundrechnung supplies data undistortedly so as to satisfy a great variety of
potential information requirements. It may not contain results from arbitrary
distribution operations and valuations; instead quantities and their monetary
aspect must be stored explicitly. Its design must be flexible enough to
accommodate new attributes when requirements change or grow. Back-Hock (1995)
identified the basic types of data units in the Grundrechnung as:
1.
Objects of decisions, e.g., events and states,
2. Factors that influence these objects (e.g.,
decision parameters or aspects to be
taken as given, or functional relationships between these
objects), and
3.
Domain values of objects and influence factors.
By
storing such data units separately, the Grundrechnung would probably be able to
satisfy a variety of potential information requirements.
Colantoni,
Manes and Whinston (1971) were the first accounting researchers to explicitly
connect database technology with the problem of building more powerful
disaggregate and multidimensional accounting systems, although others like
Firmin (1966) and Eaves (1966) had broached the issue in more general
terms. Colantoni et al. described a
technique for coding each event with both monetary and non-monetary
characteristics and extended this scheme by using a tree-type (hierarchical)
data structure to parallel the normal chart of accounts for coded event types.
They also introduced a data management language. In our opinion, however, they
misinterpreted Sorter (1969) when they proposed his ideas as a call for
computerized disaggregate databases. As we explain later, our interpretation of
Sorter’s 1969 article (especially when it is read in the light of his later events
ideas) is quite different. We see no strong proposal for newer kinds of
transaction processing systems, only a suggestion for different types of
financial statement disclosure.
Two more articles proposing
hierarchical database accounting models (Lieberman and Whinston 1975; Haseman
and Whinston 1976) incorporated many of
the early 1970s’ advances in database technology. Lieberman and Whinston
proposed a logical framework for an events-accounting information system and
described a possible implementation of such a system. Haseman and Whinston
described the processes involved in self-organizing databases involving the
transformation (based upon a stream of user inquiries) of unstructured data
files into logical data banks.
Everest and Weber (1977) applied
some concepts of Codd's relational database model (1970, 1972a, 1972b) to
accounting. To derive relational models for both managerial and financial
accounting, Everest and Weber took conventional accounting frameworks and
normalized them using Codd's decomposition process (1972a). They then
illustrated the use of relational algebra operations to derive information from
the normalized database. They observed that application of the relational
database model to accounting frameworks was a procedure fraught with major
problems. For example, they noted that
the duality of double-entry (i.e., the double-entry accounting equation) seems
at odds with efficient computer processing. Also, the normalization process
embedded naming and classification artifacts in the database schema when it was
applied to a conventional chart of accounts framework. They noted that much
accounting theory concerns efficient classification schemes or naming
conventions, whereas database management theory is more concerned with the objects
to be classified. Everest and Weber called for further research to make
accounting systems fit advanced data structures better.
McCarthy (1979, 1980a) developed a
database accounting system to accomplish the better fit of accounting systems
to advanced data structures. He did so by applying Chen’s (1976)
entity-relationship (E-R) design process to the accounting domain. This
resulted in a database schema with a high level of semantic expressiveness and
without embedded procedural aspects of conventional accounting.
REA
Accounting
McCarthy (1982) extended his E-R
approach, exploring the issue of database design in a larger organizational
context. He emphasized that a change in perspective is needed if accounting is
to become a constituent part of an enterprise database system rather than
remaining an independent and non-integrated information system. He explained
that the view modeling and view integration phases of database design require
that accounting phenomena be characterized in terms compatible with
non-accounting decision use. He proposed the REA accounting model as such a
characterization.
Figure 1 about here
Figure 1 illustrates the entities
and relationships of an REA model. In addition to the resource, event, and
agent entities, there are four different types of relationships in the REA
model. Stock-flow (including inflow and outflow) relationships denote events
which increase or decrease economic resources. Duality relationships
associate the dual parts of a single economic exchange, i.e., what is given
up is linked to what is taken in. An example would be a sale that is
linked to a resulting cash receipt. The control relationship is a
ternary connection between an inside agent, an outside agent, and an economic
event. For example, a purchase typically involves a buyer (inside agent) and a
vendor (outside agent). Often, however, this ternary relationship is divided
into two binary ones; this is a common implementation compromise that makes the
model easier to understand and implement. Responsibility relationships
were also defined by McCarthy (1982) for REA, although they are not shown in
Figure 1. A responsibility relationship indicates that higher level units
control and are accountable for the activities of subordinates. Economic units
are a subset of economic agents. The role declarations for each of the four
types of relationships are portrayed in McCarthy (1982, 564).
McCarthy proposed that the REA
framework be used as a starting point for enterprise-wide database design. He
suggested modifications that may be useful, depending on specific corporate
information needs. One such modification is the use of generalization as
advanced by Smith and Smith (1977). Generalization relates different subtypes
or subsets of entities to a generalized type or superset. McCarthy used the
example that the entities raw material, work in process, and finished goods
generalize to the entity inventory. The modeling of generalization hierarchies
allowed much closer correspondence of system primitives with the real-world
phenomena they represented.
McCarthy (1982) also enumerated many
of the procedural enhancements that would be needed in a working REA system to
materialize accounting conclusions. This same topic was treated in more detail
in McCarthy (1984).
Influences
of Mattessich and Ijiri on the REA model
When McCarthy first formulated the
REA model, he did so by abstracting from current practice in the structure of
accounting systems with the data modeling techniques of aggregation and
generalization (Chen 1976; Smith and Smith 1977). The concepts he produced as a
result, however, bore clear resemblance to the works of theorists such as
Mattessich and Ijiri, and McCarthy used elements of their work to describe REA
components. It is important to remember that those concepts are not identical
but only similar. Although the precise definitions of REA constructs are those
in McCarthy (1982), the ideas of Ijiri and Mattessich strongly influenced the
choice of terms.
Mattessich (1964) was one of the
best sources for abstract descriptions of accounting phenomena available in the
1970s. His axiomatization of accounting gave substance to the notions of
economic agents, economic objects, and duality. None of the REA primitives
match the 1964 definitions exactly, but overall they are close in spirit. The
most notable difference was in Mattessich's explanation of duality which diverges
sharply with REA duality because it concentrates on classificational
double-entry, a circumstance noted later by Ijiri (1975).
Ijiri's (1967, 1975) accounting
measurement work had a clear influence on the REA accounting terms used by
McCarthy and later by Geerts and McCarthy (1994). His differentiation between
causal and classificational double-entry laid a foundation for the REA notion
of duality, and his causal networks presaged the concept of connecting REA
processes into an enterprise value chain like that popularized by Porter
(1985). It should be emphasized, however, that although Ijiri's causal
double-entry is similar to REA's duality, the concepts are clearly not
identical, a disparity accentuated by Seddon (1991, 5-11). Ijiri stressed
equality of values for resources incremented and decremented in an exchange
while there is a clear presumption in REA accounting systems that increments
are expected to exceed decrements in value (Geerts and McCarthy 1994) in normal
exchanges. Additionally, Ijiri (unlike a full REA model) did not advocate full
traceability as evidenced by his allowance of procedures such as periodic
matching. Ijiri (1975) introduced the concept of intentionally degenerate
exchanges such as spending money on general and administrative services. REA
does not model such expenditures as unrequited decrements, but as decrements
that will be traceable to future increments.
In later years, Ijiri's work on
triple-entry bookkeeping and momentum accounting took him further and further
from the world of REA modeling as he moved toward a preoccupation with
classificational systems. However, Ijiri (1967, 1975) undeniably influenced the
development of REA accounting concepts in a very substantial way. In many ways,
the differences between Ijiri's fully explicated ideas and REA primitives are
ones of focus and orientation. Ijiri's early work emphasized
accountability-driven measurement and valuation based on historical cost
concepts, while McCarthy was most concerned with semantic representation of
enterprise economic phenomena leading to actual information system
implementation.
Section
Summary
This section has presented the
intellectual heritage of REA systems. The citation history of REA accounting
includes events accounting works such as Sorter (1969) and database accounting
works such as Colantoni et al. (1971). Works of theorists such as Mattessich
and Ijiri, which do not fit into either of these categories, helped give
McCarthy a theoretical foundation for his REA primitives. However, the
differences between the REA model and its intellectual predecessors are
significant. The next section proposes a means of differentiating among REA and
other accounting models. These criteria are then applied to the papers
discussed in this section to provide clarification as to the extent to which
equivalence and overlap can be identified between events accounting, database
accounting, and REA accounting.
II.
DIFFERENTIATING ACCOUNTING MODELS
Criteria
for Differentiation
Three core features of the REA
accounting framework--its database orientation, its semantic orientation, and
its structuring orientation--can be used to compare and contrast events,
database, and REA accounting models. Each of these orientations is explained in
this section. Subsequently, each of the papers discussed in section I is
analyzed as to what extent these features are included.
Database
Orientation
A
database orientation as defined here requires three conditions:
1.
Data must be stored at their most primitive levels (at least for some
period),
2.
Data must be stored such that all authorized decision makers have access
to it, and
3.
Data must be stored such that it may be retrieved in various formats as
needed
for different purposes.
These
conditions do not require the use of database technology--object
oriented, artificial intelligence, or other technologies that allow storage and
maintenance of primitive detail accommodate this orientation. This also allows
for systems built using database technology that do not have a database
orientation. An example is a system built with microcomputer database
management software that uses tables to represent journals and ledgers, but
does not keep information about multiple line items for sales or purchases (or
that keeps such information only until the accounting period is closed).
Semantic
Orientation
Integrated semantics is a
fundamental idea of modern database management, reflected in Abrial’s (1974, 3)
definition, "a database is a model of an evolving physical reality."
Re-stated in terms of design methodology, this means that all potential users
of a database pool their notions of important information concepts and use that
integrated set of ideas to build one conceptual data model that serves
everybody. The objects in this conceptual model are required to correspond
closely to real world phenomena, hence the accentuated use of the term semantic
to describe this activity. In an accounting domain, integrated semantics means
that accounting models should depict the economic exchanges or processes that
produce the firm's accounting data (such as the revenue process shown in figure
2). Components of the models should reflect real world phenomena, a situation
that precludes the use of basic double-entry artifacts (e.g., debits, credits,
accounts) as declarative primitives. Semantically-modeled accounting systems
allow representations of economic exchange phenomena to be integrated well with
descriptions of non-accounting phenomena (as displayed by some of the dotted
lines in figure 2). Both of these types of data can be accessed and used
extensively by non-accounting decision makers, something not facilitated by
traditional accounting systems.
Figure 2 about here
Structuring
Orientation
A structuring orientation mandates
the repeated use of an occurrence template as a foundation or accountability
infrastructure for the integrated business information system. There are two
core structuring ideas within the REA accounting model.
First is the use of a
template that records and stores data associated with sets of economic events,
as illustrated in both halves of figure 1. For each economic event, data are
recorded and stored pertaining to resources and agents connected to the event.
For example, Sales is a set of events about which businesses record and store
data. Along with capturing data about each sale event (e.g., invoice number,
date, amount, etc.), REA structuring requires that data be captured about the
associated resources (e.g., inventory, delivery truck, labor) and agents (e.g.,
salesperson, customer) involved. The resources, events and agents
are referred to as entities or things of concern to organizational decision
makers. The REA model also requires that data about relationships between or
among the entities be maintained. Therefore, the data must be stored in such a
way that the links (1) between an event and its resources involving inflows and
outflows (stock-flow relationships) and (2) among an event and its agents
involving participation (control relationships) are preserved.
The second structuring
idea is that there are two basic types of economic events--resource outflows
(give) and resource inflows (take)--and that these types are normally coupled
through duality relationships. For a transaction cycle, this means that two
mirror-image REA templates are connected in a give-take pairing that models an
exchange. This is shown above and below the dotted line in figure 1.
A simplified example of the two
structuring ideas being used together is portrayed in figure 2 where sale event
templates are related to cash receipt event templates and where some types of
resources are given in consideration for others (i.e., there must normally be
at least one cash receipt associated with a sale). At any time, there may be
exchange imbalances (e.g., a sale for which cash has not yet been received)
that result in claims such as accounts receivable (McCarthy 1982, 568).
The structuring orientation of REA
accounting enables the maintenance of a centralized data bank, structured such
that the resultant accounting system can serve as a framework for the
integrated business information system. Full REA modeling as described by
Geerts and McCarthy (1994) considers the firm as a set of exchanges or
activities where some resource is given up (the decrement) in return for a
resource taken (the increment) in each process (Geerts 1993). At the highest
level of abstraction, the entire enterprise is considered as one process with
an input of cash (initial financing) and an output of cash (debt or equity
repayment plus profit). The abstract organization of such processes downward
into successively finer levels of data detail and upward into an enterprise
value chain is a theme explored extensively by Geerts and McCarthy (1994).
Application
of the Differentiation Criteria
Events
Accounting
Although the events accounting
papers discussed in section I advocate less aggregation than had previously
been present in accounting systems, they do not have a database, semantic, or
structuring orientation. Providing users with financial statements that are
prepared in sufficient detail that they can deduce underlying events (by
emphasizing cash flows and removing accruals) is a very different concept than
providing users with a database of information from which they can extract
event data in various levels of focus and aggregation.
Figure
3 about here
Figure 3 illustrates specifically
the main difference between events accounting per Schrader (1962), Sorter
(1969) and Johnson (1970) and REA accounting per McCarthy (1982). As
illustrated in figures 2 and 3, semantic data models easily accommodate the
notion of generalizing from entity sets and typifying class attributes of those
concepts. Figure 3 generalizes the entity set types of sale, cash-receipt,
cash-disbursement, and purchase to the set of all economic events.
Doing so causes an expansion in the aggregation plane (McCarthy 1982) as seen
on the middle left of figure 3. In the notion of event type, there is the clear
intent of Sorter in his events accounting. He was not proposing an
accounting model that would maintain transaction level detail (as designated in
the individual events of figure 3), but only the disaggregation of certain
lines on financial statements. The table[1]
representing the relationship between event type and time period
in figure 3 (the Period-Event Categories table) comes close to the
meaning of Sorter's events accounting. He called for less accrual and fewer
combination judgments, not for a different kind of accounting data model.
Database Accounting
The database accounting work
described in section I varies as to the extent it includes database, semantic,
and structuring orientations. An analysis of each follows.
The Goetz (1939, 1949) idea of a
Basic Historic Record was startlingly similar to the notion of a modern
computer database, especially a semantically built database that models
reality. Goetz's ideas are consistent with the use of a template to capture
data in primitive form, and he also hinted at the notion of duality, although
he did not discuss it specifically. Thus, database and semantic orientations
are clearly evident in Goetz's work, and a structuring orientation is partly
represented. Schmalenbach's (1948) Grundrechnung appears to be
equivalent to Goetz's Basic Pecuniary Record and is likewise consistent
with the database and semantic orientations.
The work of Colantoni et al. (1971)
is important, because it was the first to recognize that the events concept (at
the instance level) could only be realized by a thorough integration of
accounting concepts with concepts of database management[2].
Their work is also very important, because they were among the first to propose
a computerized accounting system that was not based primarily on double entry
accounting. Their lack of immediate classification of events into debit and
credit terms and the ability of their proposed system to create multiple views
of data are consistent with a database orientation, and at least partly
consistent with a semantic orientation (although they still cling to the
account artifact in their declarations). There is no clear structuring
orientation in their system.
Lieberman and Whinston (1975) and
Haseman and Whinston (1976) focus on events at the instance level, thus
demonstrating some level of database orientation. However, the example
implementations they discuss use list processing that (as Everest and Weber
(1977) point out) negates the database orientation by eliminating data
independence. There is also no strong evidence of a semantic orientation; they
use debits, credits, and accounts. There does not appear to be a structuring
orientation.
Everest and Weber's (1977) work
demonstrates a database orientation, but their attempt to support
classificational double-entry artifacts make their model inconsistent with a
semantic orientation. As mentioned in section I, they recognized that the
problems they encountered probably resulted from this lack of semantic
orientation, and they suggested that future database systems not model
accounting artifacts. No structuring orientation is evident in their work.
McCarthy (1979, 1980a) included
database and semantic orientations, advocating shared use of elementary data
without accounting artifacts embedded into a system. The full structuring of
the REA model is not specified in this work, but many of its elements (such as
stock-flow and duality relationships) are discussed and demonstrated.[3]
REA
Accounting Systems
McCarthy (1982) extended his earlier
work by keeping its database orientation, by expanding its semantic orientation[4]
to including generalization hierarchies, and by adding a full structuring
orientation as described earlier in this section. The result of adopting all
three of these orientations is a semantic theory of how an information system
that tracks economic phenomena should be structured in a shared use environment
without regard for ever changing technology platforms.
Figure 4 about here
Section Summary
Figure 4 summarizes how all the
different works discussed in this section fit together. The outer circle
represents those accounting models that focus on event types as primitives and advocate less aggregation
than the traditional double-entry bookkeeping model provides, yet have no database,
semantic, or structuring orientation. We label this category Events
Accounting because the events articles all fit this criterion. The next
circle toward the center represents those accounting models that have a
database orientation but do not exhibit a semantic or structuring orientation.
We have labeled this category Database Accounting because most of the
articles in that heading fit these criteria with exceptions of Goetz (1939,
1949), Schmalenbach (1948), and McCarthy (1979, 1980a). These exceptions constitute a new category,
the third circle toward the center of the diagram. This circle represents
accounting models that have a database and a semantic orientation, but do not
specify a structuring orientation. We label this category Semantically-Modeled
Accounting. The center circle represents accounting models that encompass
all three orientations. We label this REA Accounting because REA is the
only accounting model that contains all three orientations. Table 1 portrays
the works in this categorization, and it also summarizes[5]
some of each work’s major ideas.
Table 1 about here
Obviously, this delineation cannot
exhaustively and precisely type all research efforts aimed at building better
accounting systems, but it can be used to give some structure to a field where
ambiguity of terms is widely present. As with most categorizations, there are
gray areas. For example, Colantoni et al. (1971) have at least a partial
semantic orientation. Thus they probably belong on the border between Database
Accounting and Semantically-Modeled Accounting. Also, the benefits of the three
different orientations are only hypothesized; they have not been directly
subjected to empirical tests. The next section therefore examines to what
extent the accounting models presented in this categorization have been subject
to validation, and what the results of those validations have been.
III. EXISTING
VALIDATIONS OF ACCOUNTING MODELS
A representation model’s value can
be assessed in various ways. One is by seeing if other researchers have found the model’s concepts useful in their
own conceptual research and if they have used variations of the basic themes
and ideas in their own model-building efforts. Assessment also occurs with the
development of a proof of concept--the building of a working implementation of
the model--which is often done in computer science (Newell and Simon 1976;
McCarthy et al. 1992). Most convincingly in accounting research, a model may be
validated through empirical examination. This section reviews the extent to
which events, database, semantically-modeled, and REA accounting models have
been refined, implemented, or validated in both research and in practice.
Events
Accounting
In summarizing Sorter, Johnson, and
Schrader's work, it is important to emphasize that these authors concentrated
primarily on the external reporting aspects of events accounting. In other
words, they did not attempt to develop specifications for disaggregate and
multi-dimensional transaction processing systems but chose instead to expound on the disclosure methods (and the
effects of such methods) that could be realized with an events approach. These
works were very important in that they sparked several more articles that used
their themes and proposed implementation of the events theory with varying
forms of computer science techniques. However, as discussed in section II, the
proposed implementations actually added a database orientation that was clearly
not present in the original events accounting theory.
Revsine (1970) did not conduct an
empirical test of Sorter’s ideas, but he identified the need to test the
practicability of events accounting from a user standpoint. He agreed with the
potential benefits of events systems. However, he cautioned that the user
processing constraint of finite channel capacity would cause events systems to
result in user information overload, an assertion that had clear empirical
implications. Benbasat and Dexter (1979) tested the events hypothesis at an
individual user level (loosely stated, users are better off with disaggregate
data) by comparing decision performances in an operational control context. The
paper-based implementation they tested was one of an event-type nature,
consistent with the concepts advanced by Sorter; it had no database
orientation. They found no significantly better (profit) performance
attributable to disaggregated information and additionally found that the
disaggregate data user took more time to make decisions. The task users
performed was highly structured, so designers would be likely to know users’
information needs and would aggregate accordingly. Benbasat and Dexter
recommended that events systems be tested using different (unstructured) tasks
as well.
Database
Accounting
While all four articles in this
section described proposed implementations of
the events accounting model (as augmented with a database orientation)
none of the four described an actual working implementation.[6]
This is probably because of the problems identified by Everest and Weber (1977)
as inherent in trying to implement accounting artifacts in database format.
Parrello et al. (1985) attacked this implementation of accounts problem with a
more abstract approach. However, their models became overwhelmingly complex and
less generalizable very quickly, and there was no further implementation work
done with them. Additionally, there was no empirical testing involving these
database accounting systems.
Semantically-Modeled
Accounting
Because the works of Goetz and
Schmalenbach appeared before technology was available on which to implement
their proposed accounting systems, there are no direct working implementations
of their ideas. However, the seeds planted by Schmalenbach clearly had
influence in a later computer-oriented age. Some Grundrechnung implementations
are described by Back-Hock (1995).
McCarthy (1978,1980b) used a
relational database model to implement his E-R system for a small retail
enterprise. Later implementations reflect the advances of REA structuring over simple semantic
representation, and they are thus discussed in the REA accounting sub-section
below. Reuber (1990) proposed a semantic representational scheme for
manufacturing that accounted for REA modeling of activities, but which also
added a layer of non-structured semantics for cost management.
REA
Accounting
Gal and McCarthy (1983, 1986)
defined a compromised retail REA implementation, first with a CODASYL database
management system and then with a Query-By-Example (QBE) database system. Denna
and McCarthy (1987) did the same for a manufacturing enterprise with an SQL
system, as did Armitage (1985) with QBE. Kandelin and Lin (1992) followed these
implementations with object-oriented work in the ACTOR language. Research
prototypes such as REACH (McCarthy and Rockwell 1989) and CREASY (Geerts and
McCarthy 1992) combined the REA model with artificial intelligence and object
oriented programming, implementing their systems using Goldworks and Prolog
respectively. Finally, constructs of the REA model have been used in production
accounting system implementations such as the Price Waterhouse GENEVA Data
Architectures and the IBM-Japan Financial Data Warehouse Project (Cherrington
et al. 1993).
Weber (1986) is an attempt to
empirically assess the validity of the REA model. Weber approached the question
by observing what was being done in practice, noting that real-world accounting
implementations provide a rich source of data against which to test accounting
models proposed by researchers. Weber found that the major elements of the REA
model are incorporated into software at the infological or high semantic level.
Thus, the model is at least partially validated. At the datalogical or low semantic
level, the software packages differed from one another in areas that are not
specifically defined by REA. He suggested that the REA model be refined to
lower levels of abstraction, even if that means making it domain specific. One
recommendation was to build contracts and commitments into the REA model, two
types of transactions that McCarthy (1982) specifically mentioned as possible
extensions. McCarthy (1982) claimed that existing accounting convention allows
less than full specification of schema elements, and he demonstrated that
procedural implementations and modifications could be made to the generalized
framework to model such instances. Different situations may call for different
use of procedural representations or declarative modifications. These
implementation characteristics perhaps accounted for some of the lower level
variance in the software studied by Weber.
Section
Summary
In
this section we reviewed the various categories of accounting models
identified in section II, assessing the extent to which working systems based
on the models have been implemented and the extent to which aspects of the
models have been subject to empirical tests. This analysis revealed that the
REA accounting model has been the most widely implemented, refined, and
empirically tested of the four model categories. Perhaps more striking is the
fact that very few of the studies discussed in this section were empirical
validations.
IV. PROSPECTS
FOR PROGRESS
In this section, we discuss March
and Smith’s (forthcoming) framework for information technology (IT) research to
help identify potentially productive extensions and validations of REA
accounting.
Figure 5 about here
March
and Smith Framework for Information Technology Research
As portrayed in figure 5, March and
Smith (forthcoming) propose a two dimensional framework for planning and
evaluating IT research. The components of each dimension are below.
1. The
horizontal dimension of the framework distinguishes between design science and
natural science. March and Smith note that natural science typically consists
of two stages--theorize and justify--and they also propose that design science
consists of two stages--build and evaluate--which actually parallel the two
stages of natural science. Build is defined as the construction of an
artifact, proving feasibility (i.e., that it can be constructed). Evaluate
is defined as the development of specific metrics for assessing the performance
of an artifact and then measuring the artifact according to that criteria. Theorize
in IT research involves explaining why and how an artifact works (or doesn't
work), while justify performs empirical and/or theoretical research to
test the proposed theories.
2. The
vertical dimension of the IT research framework consists of the broad
categories of outputs produced by design research: constructs, models, methods, and instantiations. The exact
delineation of these categories is somewhat imprecise, but the four in concert
certainly cover most design science endeavors.
March and Smith (forthcoming) say
natural science aims to understand and explain phenomena, whereas design
science aims to develop ways to achieve human goals. They further argue that IT
research should be concerned both with utility, as a design science, and with
theory, as a natural science. In discussing the evaluation of IT research,
March and Smith argue that building the first (never done before within the
discipline) of virtually any kind of construct, model, method, or instantiation
has research contribution provided the artifact has utility for an important
task. Building subsequent constructs, models, methods, and instantiations
addressing the same task must demonstrate significant improvement in order to
provide research contribution. Thus, per March and Smith, evaluation is the key
activity for assessing such research.
The
Build and Evaluate Categories
Most of the work discussed in this
paper fits into the Build category of March and Smith’s framework, and
more research can be done in this category. However, as March and Smith
emphasize, future Build work must meet certain criteria if it is to be
considered useful. March and Smith emphasize repeatedly that any new models,
methods, or constructs proposed must be Evaluated against existing ones
before their research efficacy can be established. We contend that any new
constructs, methods, models, or implementations in events or database
accounting that ignore the semantic and structuring orientations of the REA
model would not be justifiable as advances in the field. For example, a
proposed system that contains a database orientation but which declaratively
models accounting artifacts (debits, credits, accounts) as primitives must
prove its superiority over a pure semantic database that directly models
real-world economic phenomena.
We believe that semantic accounting
models must also have a structuring orientation if they are to serve as a
foundation for enterprise-wide models; however, there could certainly be
alternatives to REA’s methods of structuring. March and Smith’s framework
firmly places the burden of proof on researchers proposing such alternatives to
demonstrate that they Evaluate well against REA’s methods on some
definitive metrics. Thus, for example, it is not acceptable to simply say
object oriented systems are perceived to be an advance over more declarative
semantic formalisms (such as entity-relationship modeling, data abstraction, or
Nijssen’s Information Analyses Methodology (NIAM)), therefore any object-oriented
accounting system is better than the accounting systems already built which
used those prior frameworks. One must define specific metrics for evaluating
the two models and demonstrate where the previous work falls short on those
metrics. In the case of object-orientation, we believe that such efforts would
find many of the advantages to be already present in existing REA work
(McCarthy and Rockwell 1989; Geerts and McCarthy 1992). In other words, every
new software idea is not automatically research when it is applied for the
first time to an accounting domain.
Potentially productive extensions in
REA accounting research could include (1) use of REA to explicate better
methods, constructs, or instantiations or (2) building better instantiations of
accounting systems than the ones reviewed here.
An example of research fitting the
first category is development of the construct of epistemological adequacy
(McCarthy and Hayes 1969) for accounting systems (Geerts and McCarthy 1992).
The definition of this construct stems from the idea that a system consists of
repeated occurrences of the structured REA template. Other examples would
include the use of REA as a foundation for manufacturing systems, as proposed
by Denna et al. (1994) and by Grabski and Marsh (1994). For the second
category, March and Smith point out that instantiations offer proof of
feasibility of constructs, models, and methods; these resulting artifacts then
become the objects of study. Since REA has been instantiated both in prototype
systems and in corporate implementations (Cherrington et al. 1993), research in
category 2 could include instantiation of other proposed accounting models and
evaluation of those instantiations compared to existing prototypes and
implementations.
The
Theorize and Justify Categories
The greatest need for REA accounting
research appears to be in the Theorize and Justify columns, i.e.,
the empirical realm. The only two empirical studies reviewed in section II were
Benbasat and Dexter (1979) and Weber (1986). Benbasat and Dexter studied
individual behavior; Weber examined organizational level phenomena. Potentially
productive validations of the REA model could likewise include studies at
either the individual level or at the organization level.
Individual
User Validation Studies
The study of individual users’
behavior as a means of validating accounting systems and models is an area that
has been largely untouched in accounting systems research and thus provides a
vast array of research possibilities. Existing instantiations of REA accounting
constructs, models, and methods can be tested with user performance as a
validation criteria. Theories can be generated as to why performance with one
instantiation would be expected to surpass that with the other; tests can be
conducted to justify the theories.
Studies in this category could be
laboratory experiments, field tests where users are questioned directly, or
survey research. Measures of user performance included in other IS studies
(e.g. Jih et al. 1989) have included decision quality, decision completion
time, and user satisfaction. Decision
quality has been measured as accuracy (in studies where there are correct
answers), as best result (such as highest profit where decisions affected
profit or lowest cost where decisions affected costs), or as consensus (in
studies where there was no correct answer or best result possible, it was
determined that the extent to which experts agreed with the decision indicated
how good it was). Decision completion time may be measured as amount of time to
make a decision. Alternatively it may be measured more finely, for example,
through process traces indicating how much time a subject spent looking at
particular computer screens within a system. Suggestions for measuring user
satisfaction are presented by Seddon and Kiew (1994).
Dunn (1995) is an example in this
category that encompasses all four columns of the March and Smith framework.
McCarthy (1982, 1987) and Gal and McCarthy (1995) suggested the use of
abstraction hierarchies as developed by Smith and Smith (1977) in conjunction
with the REA model. They proposed a seven level abstraction hierarchy which
could be used to control complexity in accounting systems. Dunn built an
instantiation of this REA abstraction hierarchy as an interface to an REA
database, and she also built an instantiation of a non-abstraction interface.
Based on prior behavioral accounting and computer science studies, Dunn
developed hypotheses as to why the abstraction hierarchy interface should assist
users with various cognitive processes involved in preparation of financial
statements from a database, thereby enhancing their performance of that task.
She conducted a laboratory experiment to evaluate the two instantiations and to
justify her proposed hypotheses. The hypotheses were not supported, opening up
a research avenue to develop alternative hypotheses as to why the instantiation
did not work as predicted.
Organization
Level Validation Studies
The study of organization level
phenomena as a means of validating accounting systems or models has likewise
been given little attention in accounting systems research with the exception of Weber (1986). Studies
in this category would have as their primary intent a determination of whether
database, semantically-modeled, or REA systems prove their alleged advantages.
By nature, studies in this category could be either field studies or
econometric analyses such as are found in the financial accounting markets
literature.
One approach that can be taken in
organizational studies is that used by Weber (1986). Termed Economic Darwinism
by Zimmerman (1995), this approach suggests that an activity engaged in by
surviving and presumedly economically-rational organizations over extended
periods of time must be yielding benefits in excess of its cost (though it may
not be necessarily optimal). This suggests, for example, if existing firms are
using the REA model or its constructs in some conceptual or compromised
fashion, such implementations must have benefits that exceed their costs. A
second approach to organization level studies is to observe implementations of
events, database, semantically-modeled, or REA accounting systems and to
measure specific indicators of IS success, such as economic performance,
productivity, competitive advantage, etc. David (1995) takes such an approach.
She conducted a field study in which she evaluated companies' accounting
information systems as to the extent they incorporate REA semantics and structure.
She also measured various IS success indicators. She then compared each
system’s degree of REA correspondence to its success indicators to gain
evidence as to the specific benefits the REA model can produce.
Section
Summary
In this section, we proposed a framework based on March and Smith (forthcoming) for
evaluating future research projects in the domain of accounting information
systems. It is certainly our opinion that
a substantial amount of both empirical and non-empirical work remains to be done.
Design science emphasizes computer science traditions; potential new
projects should concentrate on building new constructs, models, and methods and
then evaluating them with specific metrics against the database, semantic, or
structuring orientations of existing accounting models. Natural science
emphasizes traditional social science research methods; potential new projects
should concentrate on developing theories about existing constructs, models,
methods, and instantiations, and then justifying the theories through empirical
tests. We believe that there is considerably more potential for natural science
work in accounting systems research than there is for design science work.
There has been far less natural science research done in accounting systems,
and the social science research methods are much more familiar to a wider
spectrum of accounting researchers.
V. Conclusion
This paper has concentrated on past research work in
accounting information systems; however, its most important implications are
clearly for the future. A half-century ago, Goetz (1939) and Schmalenbach
(1948) foresaw the need for accounting systems to adapt as competitive business
environments themselves change. To a large extent, their suggestions lay fallow
for many years until the enabling effect of information technology, especially
with regard to the possibilities for database implementations, began to affect
AIS research and practice. The events ideas of Sorter (1969) were not
themselves the blueprint for modern semantic models of enterprise economic
phenomena, but their public airing was interpreted by 1970s researchers like
Colantoni et al. (1971) as a call for research into more disaggregate
transaction processing systems using database technology. These database accounting
systems eventually led to the introduction of semantic modeling by McCarthy
(1979), an innovation that produced accounting systems whose structures and
philosophies for use were congruent with the earlier ideas of researchers like
Goetz (1939, 1949). McCarthy’s REA work (1982) extended semantic work further
and resulted in an object template of economic resources, events, and agents
that was proposed to model enterprise economic activities when such phenomena
were patterned in a repetitive and integrated fashion. The primitive entity and
relationship types of the REA framework were derived with semantic abstraction
methods, but their definitions and use were partially explained with terms and
ideas derived from the work of Ijiri (1975) and Mattessich (1964). Weber (1986)
examined the validity of the REA model empirically and found that its major
elements were incorporated into software packages at the infological or high
semantic level.
Future work on semantically-modeled accounting systems
should proceed on two fronts. Within the design science arena, REA ideas
should be expanded with new constructs, methods, and instantiations, while
simultaneously being challenged with extended or alternative new models. An
example of research doing the former is Geerts and McCarthy (1992), while
research doing the latter is Geerts and McCarthy (1994). In the arena of natural
science research, the new accounting information system artifacts being
proposed by REA theorists and other modelers need abundant doses of empirical
examination, such as that done by Dunn (1995) and David (1995). For progress in
this field to occur at a faster pace, both design science work and natural
science work are important. At present, however, we believe that the relative
paucity of empirical results and the relative abundance (and acceptability) of
academic AIS researchers with skills and interest in pursuing natural science
research projects warrant concentration in the AIS community on work of that
type.
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TABLE 1
Categorization of Accounting Frameworks
|
Year |
Title |
Author |
Ideas |
|
|
Events
Accounting |
|
|
|
1969 |
An
‘events’ approach to basic accounting theory |
Sorter |
Events
accounting Disadvantages
of Value theory Operational
rules |
|
1970 |
Towards
an "events" theory of accounting |
Johnson |
Forecast
and observational verification criteria Definition
of permissible aggregation Mathematical
model |
|
1962 |
An
inductive approach to accounting theory |
Schrader |
Difference
between observed data and manipulated data |
|
|
Database
Accounting |
|
|
|
1971 |
A
unified approach to the theory of accounting and information systems |
Colantoni,
Manes and Whinston |
Introduction
of database concepts Event
coding Key
algebra |
|
1975 |
A
structuring of an events-accounting information system |
Lieberman
and Whinston |
Three
part structure User-defined
database characteristics Self-organizing
database capabilities |
|
1976 |
Design
of a multi-dimensional accounting system |
Haseman
and Whinston |
Hierarchical
organization of events database Definition
of restructuring functions |
|
1977 |
A
relational approach to accounting models |
Everest
and Weber |
Data
independence Normalization |
Semantically-Modeled Accounting
|
1939 |
What's
wrong with accounting? |
Goetz |
Maintain
an unadulterated Basic Historical Record |
|
1949 |
Management
planning and control |
Goetz |
Basic
Pecuniary Record plus a legal-financial supplement |
|
1948 |
Pretiale
Wirtschaftslenkung, Volume 2 [Pretiale Lenkung] |
Schmalenbach |
Develop
a basic accounting system with no particular objective (a Grundrechnung) |
|
1979 |
An
entity-relationship view of accounting models |
McCarthy |
Second
generation data modeling Artifact-free
design |
|
|
REA
Accounting |
|
|
|
1982 |
The
REA accounting model: A generalized
framework for accounting systems in a shared data environment |
McCarthy |
REA
accounting model Generalization
hierarchies Semantic
expressiveness Enterprise-wide
conceptual schema |
FIGURE 1
REA Template
