Accounting, Organizations and Society 24 (1999) 317±331
Towards a meta-theory of accounting information systems
Elaine G. Mauldin a, Linda V. Ruchala b, *
a
School of Accountancy, University of Missouri-Columbia, 320 Middlebush Hall, Columbia, MO 65211, USA
b
University of Nebraska-Lincoln, 380 CBA, Lincoln, NE 68588-0488, USA
Abstract
The purpose of this paper is to articulate a model for accounting information systems (AIS) research that synthesizes
the primary theoretical perspectives of the extant literature. Building on the three orientations used in prior research
(technological, organizational and cognitive approaches) and adopting an explicit systems perspective, we develop a
model that links system design alternatives to the three orientations and to task performance. The model places a
central focus on the accounting task and suggests a matching process between requirements of the task and system
design alternatives at multiple levels of analysis. We also demonstrate how the application of the model suggests future
research opportunities, organized around four research propositions. # 1999 Elsevier Science Ltd. All rights reserved.
Keywords: Accounting information systems (AIS); Information technology; Expert systems; Decision support systems; Decision aids
1. Introduction
Extant accounting information systems (AIS)
research has evolved from the source disciplines of
computer science, organizational theory and cognitive psychology. An advantage of this evolution
is a diverse and rich literature with the potential
for exploring many di€erent interrelationships
among technical, organizational and individual
aspects of judgment and decision performance.
AIS research also spans from the macro to the
micro aspects of the information system (Birnberg
& Shields, 1989). The broad scope of AIS applicability suggests a potentially large sphere of in¯uence for research done in this area. Conversely, the
di€used theoretical basis also has resulted in a
* Corresponding author. Tel.:+1-402-472-8812; fax:+1402-472-4100; e-mail: [email protected]
``nebulous'' focus for AIS research (McCarthy,
1987) and an ``absence of identity'' (Sutton, 1992).
For AIS researchers, the ``absence of identity''
problem has been exacerbated by the fact that
initial applications of information technology
(IT)1 to accounting systems were transaction processing systems that tended to mirror historically
developed manual processes. Sub-disciplines of
accounting (e.g. auditing, ®nancial, managerial,
and tax) often did not perceive a need to incorporate systems considerations into their research.
Now, as IT grows more advanced, AIS applications are recognized as fundamentally changing
1
We follow March and Smith's (1995, p. 252) de®nition of
information technology as ``Technology used to acquire and
process information in support of human processes. It is typically instantiated as IT systemsÐcomplex organizations of
hardware, software, procedures, data, and people, developed to
address tasks faced by individuals and groups, typically within
some organizational setting.''
0361-3682/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved.
PII: S0361 -3 682(99)00006 -9
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E.G. Mauldin, L.V. Ruchala / Accounting, Organizations and Society 24 (1999) 317±331
task processes and providing complex decision
support, as opposed to simply increasing the speed
and accuracy of traditional accounting tasks. As a
result, there is a growing need to integrate AIS
research more closely with the other sub-disciplines
of accounting research.
Practicing accountants are being asked to focus
their attention on the design of AIS to add competitive value to information and re-de®ne the
scope of AIS (Brecht & Martin, 1996). An integration of AIS research within sub-disciplines
could also provide direction for practice. The
comparative advantage of accounting researchers
within the study of IT lies in their institutional
accounting knowledge. Systems researchers can
contribute insights into the development of systems utilizing technology, and the other sub-areas
can contribute insights into the task characteristics
in the environment. For instance, systems researchers have extensively investigated group decision
support systems (GDSS), but they have only
recently been considered in auditing. On the other
hand, auditing research has extensively investigated the role of knowledge and expertise. The
merging of the two sets of ®ndings may be relevant
to AIS design, training, and use.2
The changes in the nature and purpose of AIS,
combined with the di€used theoretical perspectives, suggest a need for an organizing paradigm
that provides a means for integrating extant AIS
research and mining the synergetic opportunities
that di€ering perspectives hold. We use the term
``meta-theory'' to suggest the integration and
synthesis of the technical, organizational, and
cognitive orientations into an overarching model
for AIS research. Prior AIS research (e.g. Cushing,
1990; Reneau & Grabski, 1987) has adopted or
adapted well-regarded frameworks from the management information systems (MIS) and IT areas
(e.g. Ives, Hamilton & Davis, 1980; Mason &
2
Bamber (1993, p. 15) reaches a similar conclusion in discussing research opportunities for behavioral AIS research:
``This research will be most fruitful if it focuses on those
domain-speci®c variables unique to accounting systems and on
the accounting decision context... Moreover, as new information technology is integrated into all accounting sub-®elds,
collaborative research will blur the distinctions between sub®elds.''
Mitro€, 1973; Gorry & Scott-Morton, 1971). The
meta-theory model extends prior research by
addressing the following four limitations of existing
IT frameworks suggested by March and Smith
(1995, p. 255):
. failure to recognize that ``the nature of the
task to which the IT is applied is critical'';
. failure to ``recognize the adaptive nature of
arti®cial phenomena; the phenomena itself is
subject to change'';
. failure to account for the ``design science
research being done in the ®eld'';
. failure to ``provide direction for choosing
important interactions to study'' and treating
all interactions as equal.
At the center of the approach in this paper is a
task-orientation and system perspective that
recognizes key interrelationships among major
categories of contingency factors; technological,
organizational and cognitive.3 A primary result of
this paper is the proposition that an AIS research
model should be reoriented around a task focus. A
signi®cant body of prior research into judgment
and decision making performance suggests that
performance is highly dependent upon contextual
elements of the task (Payne et al., 1993; Solomon
& Shields, 1995); the evaluation of an AIS should
be considered in conjunction with task achievement.
This reorientation around task also aids an
understanding of what March and Smith call ``the
adaptive nature of arti®cial phenomena''. Task
requirements establish the set of system design
characteristics4 and alternatives possible for use.
Changes in the level of performance needed and/
or requirements of the task motivate adapting IT
and changing the set of options available for
3
Similar structures have been suggested by prior research
examining decision processes. Newell and Simon (1972) identi®ed three factors in human problem solving as decision-maker
characteristics, task demands, and organizational context.
Payne et al. (1993) suggested three major classes of factors
in¯uencing decision strategy as the problem, the person, and
the social context.
4
Systems design characteristics are de®ned as the set of
hardware, software, procedures, data, and people, developed to
address a particular organizational task (March & Smith,
1995).
E.G. Mauldin, L.V. Ruchala / Accounting, Organizations and Society 24 (1999) 317±331
system design and implementation.5 As March
and Smith (1995, p. 252) state: ``technologies are
often developed in response to speci®c task
requirements using practical reasoning and
experiential knowledge''.
The task orientation of the proposed model also
suggests an explicit role for design research within
the AIS domain. March and Smith (1995) suggest
that prior frameworks have omitted a role for
``design science''Ðthe building and evaluating of
technological models, methods and instantiations.
A focus on the task and on task performance
makes explicit the need to develop systems that
improve task outcomes and to evaluate implementations based upon their incremental task
improvement.
Finally, integrating the three source disciplines
traced in this paper around a task focus is also
intended to help identify which interactions might
be more important to study than others and the
forms that interactions may take. This approach
recognizes key interrelationships between technology, organization science and cognitive processes.
AIS technologies, in the form of databases, expert
systems and decision aids, represent tools for solving accounting problems. As such, these tools
must be selected and implemented considering the
task requirements and cognitive and organizational features embedded within that implementation. Users of AIS have di€ering problem and
organizational constraints that necessitate di€ering
AIS approaches. Hopwood (1989, p. 15) states:
``. . .accounting oriented knowledge is more likely
to be gained by research which systematically
attempts to study the interdependency and interpenetration of the technical and the human and
the organizational.''
The remainder of this paper proceeds as follows. The paper begins by providing an overview
of the AIS meta-theory model with illustrations of
the model's design principles. Then, four research
5
Changes in the level of performance needed by a ®rm
could include the need to achieve an equivalent level of performance for less cost as well as an increase in performance.
Value-chain analysis and business process reengineering are
two examples of initiatives motivating the adapting of IT to
make task processes and outcomes more ecient or e€ective.
319
propositions are developed and future research
implications presented.
2. A meta-theory model for AIS research
AIS encompass systems that are either used by
accountants, by other decision-makers employing
accounting information, or in tasks that involve
the application of accounting data (Reneau &
Grabski, 1987). The model developed in this paper
builds on previous frameworks in the management
information systems (MIS) (e.g. Gorry & ScottMorton, 1971; Ives et al., 1980; Mason & Mitro€,
1973), and AIS (Cushing, 1990; Reneau &
Grabski, 1987) literature. The meta-theory model
is illustrated in Fig. 1 and will be explicated
through a discussion of four organizing principles,
one for each major component of the model. The
discussion of each organizing principle includes a
description of the theoretical roots of each component. Representative examples of prior research
are used to underscore the need for an extended
integrated framework. Taken together, these four
principles constitute a nomological network with
task characteristics at the core.
2.1. Organizing principle No. 1: task focus
The ®rst organizing principle of the framework
is that AIS research should be task focused. Fig. 1
illustrates this principle through the centrality of
task characteristics to system design alternatives
and task performance. The importance of task
focused research is increasingly recognized, both
in psychology and accounting (Einhorn &
Hogarth, 1981, 1993; Waller & Jiambalvo, 1984).
Gibbins and Jamal (1993, p. 453) argue that
research without task considerations may be
uninformative: ``Without careful observation of
the task, it is dicult to relate the research results
to important questions about the task and
accountants' performance of it, such as e€ectiveness, eciency, expertise, and value.'' Payne et al.
(1993) emphasize that decision-makers can invoke
di€erent strategies depending upon even seemingly
minor task features, such as the number of alternatives or outcomes. A task focus is necessary for
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E.G. Mauldin, L.V. Ruchala / Accounting, Organizations and Society 24 (1999) 317±331
Fig. 1. A meta-theory model for AIS research.
designing and implementing systems in consonance with human judgment and decision processes and organizational strategies. Thus, to
understand and improve AIS strategies, research
must ®rst understand the task demands. This
organizing principle extends prior AIS models, such
as Reneau and Grabski (1987), which have subsumed task elements within broader categories of
environmental factors constraining IS development.
2.1.1. De®ning task characteristics
The concept of task is multidimensional. AIS
serve many types of business tasks, including strategic, managerial, and operational (Gorry &
Scott-Morton, 1971), and each type of task can be
found in all of the accounting sub-disciplines.
Although classi®cation by business purpose is
intuitively appealing, shifting the focus to core
task characteristics may provide new insights for
understanding systems. There exists no de®nitive
taxonomy of task characteristics, although several
accounting researchers have stressed the importance of task characteristics in understanding cognitive processes (e.g. Bonner & Pennington, 1991;
Gibbins & Jamal, 1993).
The meta-theory model of AIS de®nes four core
dimensions of task characteristics that have been
considered in prior accounting and AIS research.
The ®rst is the mental processes involved in the task,
such as external information search, knowledge
retrieval, hypothesis generation and evaluation,
problem solving design, estimation or prediction,
and choice. This dimension is analogous to distinctions made in the behavioral auditing and
judgment and decision making literatures, such as
in Bonner and Pennington (1991). The second is
complexity, considered to be a function of both
the amount and clarity of inputs, processing, or
outputs (Bonner, 1994). The third dimension is task
demands as suggested by a task demand continuum
developed by Stone and Gueutal (1985). One end of
the continuum is anchored on physical labor and the
other end is symbolic, abstract knowledge. While
many accounting studies focus purely on the
knowledge end of the spectrum, the entire spectrum
is important since AIS increasingly impacts a variety
of non-accountant decision-makers utilizing
accounting information, such as production and
clerical personnel. The fourth and ®nal dimension is
frequency of occurrence. Regularities in the task
over time lead to di€erent adaptations in the cognitive process and in the evolution of systems (Simon,
1981; Gibbins & Jamal, 1993).
2.1.2. Contributions of a task focus
Carefully de®ning the underlying task requirements, as well as comparing and contrasting those
requirements to tasks previously studied, is a
critical event necessary to further our understanding of information systems in accounting
E.G. Mauldin, L.V. Ruchala / Accounting, Organizations and Society 24 (1999) 317±331
contexts (Peters, 1993). More clearly focused, task
oriented research may improve the ability to
select appropriate technology to study, a major
problem identi®ed in previous AIS research.
Technological advances arrive at an ever-increasing pace such that it is extremely dicult to
determine, a priori, what should and should not
be studied. Weber (1987, p. 7) suggests that
researchers have been ``seduced'' by the excitement of new technology:
As each wave of new technology occurs, the
literature shifts to the predictable descriptions
of the technology, examination of implications, case studies of use, exhortations to
adopt, etc. . . . The end result is a ®eld devoid
of theory and general principles.
March and Smith (1995) argue that the challenge
for future AIS research is to categorize technology
so that research e€orts will not be wasted building
and studying systems that have already been built
and studied ``in kind''. They o€er guidance by
dividing research activities into four categories
(build, evaluate, theorize, and justify), and identifying criteria for research within each domain. For
instance, research that builds systems should be
judged based on value or utility to a community of
users. Building the ®rst application has utility only
if the task application is important. Subsequent
build studies must justify signi®cant improvement.
The ®rst organizing principle of the meta-theory
model provides additional guidance for the design
science research challenge in two ways. First, analysis of task requirements needed for a particular
accounting function may assist in identifying the
potential of a new technology for improving that
function. Second, task analysis may assist in
determining how well research using a particular
task in one area will translate to a task in another
area, thereby helping researchers focus on extensions in the application of technology rather than
replications of ``in kind'' systems.
2.2. Organizing principle No. 2: design process
The centrality of task leads to the second organizing principle illustrated in Fig. 1 by the left
321
arrow leading from task characteristics to system
design alternatives. We suggest that, for AIS,
task requirements become the start of a process that
establishes the set of system design characteristics
needed. It is important to realize that this organizing principle is about designing an Accounting
Information System and not just about computers
and electronic networks. Simon (1997) states: ``in
the post-industrial society, the key problem is
how to organize to make decisionsÐthat is to
process information'' and points out that decision
making is shared between the human and
mechanized components of systems. The metatheory model suggests that task needs must be
considered in choosing among system alternatives
for a division of labor between the human and
computer components.
System design alternatives, however, operate at
di€erent levels of analysis. We adopt March and
Smith's (1995) classi®cation of constructs, models,
methods, and instantiations. Constructs are conceptualizations used to describe problems and
specify their solutions. Constructs operate at a high
level of abstraction. For instance, in an enterprise
wide accounting system, constructs would include
``data should be captured that supports the organization's critical events'' and ``detailed data
should be maintained in one place that can be
accessed by many di€erent users''. Models are
propositions expressing relationships among constructs. The Resources Events Agents (REA)
model developed by McCarthy (1982) and discussed below is an example of an enterprise wide
model. Methods are a set of steps used to perform
a task. Continuing the example, database tables
and data dictionaries are two of the many methods that would be used in an enterprise wide
accounting system. Instantiations are realized
implementations of technology, such as SAP
R/3 for an enterprise wide accounting system
example.
The model accommodates research at any of
the various levels of analysis. The model also
recognizes, however, that a system design alternative choice made at a higher level (such as construct) will often subsequently limit choices at
lower levels (models, methods, or instantiations).
Organizing principle No. 2 suggests a matching
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E.G. Mauldin, L.V. Ruchala / Accounting, Organizations and Society 24 (1999) 317±331
process between requirements of the task and
design alternatives at each level of analysis.
2.3. Organizing principle No. 3: contingency factors
The task and system design alternatives may be
central to the model, but they do not exist in a
vacuum. The third organizing principle of the model
is that the e€ects of AIS on task performance must be
examined within the context of cognitive, technological, and organizational contingency factors. Fig. 1
illustrates the three contingency factors as jointly
a€ecting task characteristics. We adopt an explicit
systems perspective where the focus is on the interdependence of the elements of the system (Frances &
Garnsey, 1996).
2.3.1. Cognitive factors
AIS cognitive research focuses on the impact of
AIS on individual and group decisions and draws
on the judgement and decision making and psychology literatures.6 Reviews by Huber (1983),
Reneau and Grabski (1987), Amer, Bailey, and De
(1987) and Weber (1987) all characterize behavioral
AIS research through the mid-80's as lacking a theoretical basis and generating inconsistent results.
More recent AIS research has moved to a closer
investigation of judgment and decision processes
and strategies, mirroring trends in behavioral
research within other accounting areas. The comparative advantage of the cognitive perspective is its
recognition that technology a€ects the decisionmaker in three di€erent ways: intended technical
e€ects, transient e€ects associated with assimilation, and unintended social e€ects (Kiesler, 1986).
An example of the investigation of unintended
e€ects is cognitive research focusing on identifying
changes in cognitive strategies in the presence of
automated decision aids designed to improve task
performance. Chu (1991) ®nds that a decision aid
6
We thank a reviewer for noting that there is also an
impact of cognitive factors on AIS design and evaluation in
that the design and evaluation are by people. The cognitive
functioning of individuals doing the AIS design and evaluation
also has interesting potential research implications. In a sense,
the model captures this issue if we view AIS design and evaluation as a task having its own alternative design structures
and cognitive, technological and organizational components.
does not reduce the potentially biasing use of
simplifying strategies as task complexity increases.
Subjects using a decision aid generated alternatives using an incremental, ``satis®cing'',
approach signi®cantly more often than non-users,
suggesting that the decision aid may induce users
to adopt a particular process in a manner that system designers may not have intended. Kottemann,
Davis and Remus (1994) ®nds that subjects
making production-planning decisions with a
decision aid expressed in¯ated con®dence beliefs
in their performance, even though their actual
performance was lower than subjects without the
aid. One possible cause of this e€ect is a tendency
to assume that the computer is ``right''.
The ®ndings of these studies illustrate that the
intended technical e€ect of improved performance
is not automatic with the introduction of a decision aid. Subtle task and training di€erences can
a€ect both performance and research results. The
meta-theory model helps to extend prior AIS cognitive research in two ways. First, Libby and
Luft's (1993) expertise paradigm is adopted to
represent the cognitive contingency factors in the
model. The expertise paradigm concludes that task
performance is contingent upon ability, knowledge, motivation and environment. The Libby and
Luft model is useful to explicate some of the
interrelations among the cognitive characteristics
(including knowledge and motivation) with the
other two contingent factors identi®ed in the metatheory model (technological and organizational
factors) and with the task characteristics. The
expertise paradigm has been widely embraced as the
basis for current cognitive auditing research in
expertise development (e.g. Bonner & Walker, 1994;
Nelson, 1993) and is generalized to AIS. Second,
the model provides a clearer focus on accounting
speci®c tasks, as well as organizational contexts, a
research extension suggested by Bamber (1993).
2.3.2. Technological factors
Information technology refers to the tools and
techniques that have been developed speci®cally to
acquire and process information in support of
human purposes (March & Smith, 1995). The
meta-theory model incorporates Sutton's (1992)
representation of technology as consisting of three
E.G. Mauldin, L.V. Ruchala / Accounting, Organizations and Society 24 (1999) 317±331
categories of primary interest to AIS researchers:
information economics, di€usion, and presentation. Information economics is concerned with the
determination of content, considering expected
costs and bene®ts of the system. Information diffusion is concerned with how to get information to
users, while information presentation is concerned
with format issues.
The modeling of a multidimensional accounting
system is one example of information economics
research, highly regarded because it is theoretically
grounded in database management research and
has often been ahead of practice (Sutton, 1992;
Amer et al., 1987). Multidimensional accounting
systems are based on the idea that accounting
should provide disaggregated data about economic events that can allow individual users to
generate information needed for a variety of
judgments and decisions. Although the basic idea
has been around for many years (Goetz, 1939;
Schrader, 1962; Sorter, 1969), it was not until the
1970s that technology began to emerge to enable
implementation of a new accounting model. Godfrey and Prince (1971) demonstrated how computerbased information networks could be used to provide disaggregated data to users. More explicit
models of multidimensional accounting systems
incorporated advances in database technology
(Colantoni, Manes & Whinston, 1971; Lieberman
& Whinston, 1975; Haseman & Whinston, 1976).
McCarthy (1982) introduced the ResourcesEvents-Agents (REA) accounting model, extending
prior database models by clearly including semantic
modeling (based on economic exchanges not debits
and credits) and more fully developing the structure of the accounting model. The REA model can
be used as a starting point in enterprise wide
database designs.
Advances in future research may be stimulated
through the meta-theory model's integration of
technology with organization and cognitive perspectives. A problem identi®ed in prior technology-based research is the fact that technological
performance is related to the environment in which
it operates. Incomplete understanding or consideration of that environment may lead to inappropriately designed applications of technology, resulting
in undesirable side e€ects, such as under-utilization
323
of technology (March & Smith, 1995). The challenge for future research is to not only determine
what technology works, but also understand why
technology utility may di€er in di€erent applications. For example, Dunn and McCarthy (1997)
suggest that validation of the REA model at either
the individual or organization level is the greatest
need for future research.
The double-sided arrow between technology
and cognitive characteristics in Fig. 1 recognizes
that, on the one hand, technology may enhance
knowledge and provide decision support while on
the other hand, cognitive strategies and individual-level factors could a€ect the technological
design and mode of operation chosen. For example, one area of MIS research involves system
design features to enhance ¯exibility. Advances in
technology may also increase the ease in adapting
technology to individual preferences, analogous to
the ``smart'' systems found in automobile design
in which individual climate, lighting and ergonomic preferences can be accommodated.
2.3.3. Organizational factors
The organizational level interactions are represented within Fig. 1 as the set of linkages from
technology to task performance through the organizational and task boxes. The link between technological and organizational factors is also two-way
since technology may allow changes to the organization structure, for example, and the organization
context will determine the technology chosen.
Organizational factors include organizational
strategy, structure, and the internal and external
business environment. Prior organizational research
focuses on the design and implementation of AIS,
as well as the relationship between AIS choice and
organizational performance. Accounting research
into the organizational determinants of AIS have
generally been based on three theories: contingency, agency and transaction cost economics.
2.3.4. Contingency theory
Early contingency research, summarized by Otley
(1980), found no universally appropriate management accounting system (MAS) applicable to all
organizations in all circumstances. Conceptual frameworks developed by Waterhouse and Tiessen
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E.G. Mauldin, L.V. Ruchala / Accounting, Organizations and Society 24 (1999) 317±331
(1978) and Reneau and Grabski (1987) agree that the
internal and external environment and production
technology are two primary categories of contingent
variables to be considered in choosing an AIS.
Empirically, Chenhall and Morris (1986), Gul
(1991), and Gul and Chia (1994) examine the
contingent relationship between internal and
external environmental factors (organizational
interdependence, decentralization, and perceived
environmental uncertainty) and choice of an AIS
(information scope, timeliness, aggregation, and
integration). The general conclusion from these
studies is that greater organizational interdependence, decentralization, and perceived environmental uncertainty are factors associated with
either a greater perceived need for more sophisticated AIS or higher ®rm performance with more
sophisticated AIS. Other contingency research
(Chong, 1996; Kim, 1988; Mia & Chenhall, 1994)
includes the role of production technology and
task variables in assessing the performance and
satisfaction with MAS. Kim (1988) ®nds, in a
hospital setting, that when tasks are highly predictable and impersonal communication is used,
MAS user satisfaction is higher. Mia and Chenhall
(1994) and Chong (1996) ®nd that the use of broad
scope MAS positively a€ects performance only in
the presence of high task uncertainty.
These contingency theory ®ndings are particularly germane in the current environment of rapid
technological developments. Many business articles
seem to imply that all organizations should adopt
the latest technology (Brecht & Martin, 1996;
Magretta, 1998; Piturro, 1998), yet empirical ®ndings suggest that this could be counter-productive.
For instance, Gul and Chia (1994) and Chong
(1996) argue that, where either environmental or
task uncertainty is low, broad scope MAS may
have an adverse e€ect on performance due to
information overload. The meta-theory model provides a clearer task focus with a systems perspective that may guide future MAS choice research.
While recognizing that contingency theory is a
valid approach to the study of organizational factors and technology, contingency theory has been
criticized for lacking a substantive basis to suggest
which variables, and which combinations of variables, are important (Fisher, 1995; Otley, 1980;
Weber, 1987). That is, it is dicult to argue
against inclusion of any of the contingent variables, yet equally dicult to determine their completeness or to know which combinations of
factors make sense and are more important. For
management control design in general, Fisher
(1995) calls for more comprehensive models that
provide a theoretical basis for multiple interactions among variables.
2.3.5. Economics based theories
The meta-theory model adopts suggestions from
Tiessen and Waterhouse (1983) and Spicer and
Ballew (1983) that transaction cost economics
(TCE) and agency theory may sharpen the focus
of contingency theory by providing a justi®cation
for identifying factors that a€ect MAS choice.
TCE and agency theory share similar roots in
economic theory but di€er somewhat in their
assumptions and research focus. Agency theory
already enjoys a prominent role in the investigation
of information system choice. Analytical research
has focused on the role and value of information
in organizations and choice of information systems
under conditions of moral hazard (e.g. Arya,
Glover & Sivaramakrishnan, 1997; Baiman, 1975).
TCE focuses on the structure of the underlying
economic transactions: standardization, frequency,
uncertainty and investment in unique, or speci®c,
assets. TCE is a comparative theory and, so, would
be suited to empirical cross-sectional investigations of organizational di€erences in the design and
implementation of MAS (Williamson, 1985).
Reference to TCE and agency theory could also
improve the operationalization of constructs used in
prior contingency research. For example, environmental uncertainty has been consistently operationalized using a validated instrument measuring
perceived uncertainty. However, a wider range of
uncertainty could be examined through comparison of multiple industries in varying locations.
All three of the theories that comprise the
organization science orientation bring to AIS
research increased recognition of the ways in
which technology may be impacted by, and may
impact, strategic, contractual and competitive
decisions of the ®rm. Their competitive advantage
is in situating the ®rms' information needs in a
E.G. Mauldin, L.V. Ruchala / Accounting, Organizations and Society 24 (1999) 317±331
broader strategic and dynamic business context.
The meta-theory model presents a comprehensive
view of AIS by integrating theoretically defensible
categories of variables with a task focus.
2.4. Organizing principle No. 4: task performance
The ®nal organizing principle is that the AIS outcome is task performance. While principle one begins
with the task orientation, principle four ends with an
outcome orientation, representing the ful®llment of
the task. Task performance is chosen as the outcome variable because both organizational and IT
researchers de®ne information in terms of the value
it holds for human action (March & Smith, 1995;
Simon, 1997). However, task performance may be
operationalized from multiple standpoints, including the eciency, e€ectiveness (cost- or process-),
output quantity and/or quality, or timeliness of the
action taken. Performance criteria will vary
according to the level of analysis being conducted,
corresponding to the system design alternatives
discussed above. For instance, at the construct
level, task performance may be strategic e€ectiveness while at the instantiation level it may be individual decision-maker performance.
The meta-theory model for AIS research depicted in Fig. 1 is a comprehensive, dynamic model
that formally recognizes complex interactions
between the four components of the model, task
demands, system design alternatives, contingency
factors, and task performance. The model provides
an overview perspective to understand prior research
in AIS. The expected contribution of the metatheory model is the stimulation of research
emphasizing the systemic nature of AIS interactions among the components of the model, as
outlined in the organizing principles above. To that
end, the following section illustrates four research
propositions suggested by the meta-theory model.
3. Research propositions
3.1. Research proposition No. 1
The ®rst research proposition is that a temporal
ordering of technology, cognitive and organizational
325
in¯uences does not exist; these coexist and evolve
simultaneously. This proposition is based on the
systemic perspective of interdependence described
in the third organizing principle of the model.
One striking example of the coexistence of multiple in¯uences is found in the application of process improvement (Davenport, 1993) or business
process reengineering (Hammer & Champy, 1993).
These techniques are heavily dependent upon
information technology to transform business
processes. However, the central point in such
analyses is not to change the fundamental business
task, but rather to gain synergy through the use of
technology in the process of task accomplishment.
Hammer and Champy (1993, p. 47) clearly state
this point: ``Information technology acts as an
enabler that allows organizations to do work in
radically di€erent ways.'' Research in MIS has
found that the biggest obstacle to successful reengineering is change management (Clemens,
Thatcher & Row, 1995). Change management
involves both organization and individual factors,
indicating the importance of the systemic perspective instead of a temporal ordering. The meta-theory model can guide future research examining the
interdependence of the three contingency factors.
For example, future research could identify how
control systems are impacted by, and impact,
reengineering e€orts at either a macro or a micro
level. Speci®cally on the micro level, agency theory
could provide one approach to examining interdependencies between technology, cognitive, and
organizational in¯uences. The idea is that technology may be used as a control mechanism to
ensure that the agent's actions are consistent with
the goals of the principal by placing limits on the
action decision-makers may take. Using AIS for
increased monitoring has implications for reducing information asymmetry, changing existing
relationships between the principal and agent.
This approach, largely unexplored in the extant
literature, is particularly complex since it involves
technology, organization control structures, and
ability, motivation, and knowledge issues from the
cognitive perspective. Mauldin (1999) examines
compensation schemes in an expert system environment and concludes that the introduction of
technology changes not only the e€ort e€ects of
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E.G. Mauldin, L.V. Ruchala / Accounting, Organizations and Society 24 (1999) 317±331
incentives, but also the type of individual attracted
to the organization. She also identi®es task di€erences in these relationships. Much remains to be
done in this area including examination of other
control structures, such as amount and types of
®rm supervision.
A di€erent approach to testing the ®rst research
proposition would be to hold both task and technology constant to more carefully identify the
e€ects of cognitive and organizational in¯uences.
For example, a bond-rating task7 shares many
common task demands with a variance analysis
task. Both require the integration of multiple cues
including retrieval, recognition, comparison, and
projection, and both are done on a recurring basis.
In spite of these task commonalties, there are
likely to be di€erences in organization structure or
cognitive experiences (such as di€erences in performance evaluation, incentive structures, or ability) that lead to di€erences in the e€ects of
technology on performance. Technology for the
common task characteristics could be examined in
the two contrasting environments to begin to
understand the complexities arising in the real
world.
3.2. Research proposition No. 2
The second research proposition is that the
relative signi®cance of technological, cognitive, and
organizational factors di€er for di€erent tasks. A
good illustration of this research proposition is the
use of decision aids for di€erent tasks. The
accounting profession has devoted considerable
attention to the development and implementation
of structured decision aids (Messier, 1995). Bene®ts
commonly associated with aided judgment include
enhanced performance, reduced decision variability, and facilitation of knowledge transfer
(Libby & Luft, 1993). Two types of problems in
decision-aided tasks have been identi®ed by prior
research. The ®rst is non-reliance on the aid even
though the aid has been shown to produce, on
average, better outcomes than un-aided judgment
(Ashton, 1990; Eining, Jones & Loebbecke, 1997;
7
Such as in the information presentation study by Amer
(1991).
Boatsman, Moeckel & Pei, 1997). The second is
inappropriate reliance where the aid's recommendation is used even when the decision-maker is
aware that the aid does not incorporate all relevant features of the environment (Glover, Prawitt
& Spilker, 1997).
The non-reliance problem has been found in
rating tasks where multiple items of information
are used to infer states of the world in a probabilistic setting (determination of bond ratings or
management fraud risk assessments). The inappropriate reliance problem was found in a computation problem (calculation of income tax). The
two tasks di€ered on at least two dimensions of
task demands, cognitive and complexity. However, incentive structures and knowledge and ability of subjects also di€ered in the studies. A likely
explanation of the di€erences in outcome is that
there are di€erent weights on cognitive, organizational, and technology factors related to each task.
The meta-theory model could guide future
research explaining the weightings of each of the
factors for each type of task, to provide direction
for system design alternatives by task.
3.3. Research proposition No. 3
The third research proposition is that there is a
relationship between the di€erent levels of analysis
such that design choices made at a higher level
(such as construct) will a€ect those at a lower level
(such as model). March and Smith (1995) suggest
that system development methods move from
abstract constructions of problems (constructs) to
increasingly concrete representations of user needs
(models), system requirements (methods) and
implementations (instantiations). This hierarchy
logically implies that judgments made at any one
level will limit the subsequent choices available at
a lower level.
Within the accounting arena, an important
research question is whether and how the problem
de®nition at the construct level changes alternative
sets of models, methods and instantiations. Brecht
and Martin (1996) suggest a rede®nition of the
scope of AIS, at the construct level, to include
strategic, management and task/operational information. Future research is needed to evaluate the
E.G. Mauldin, L.V. Ruchala / Accounting, Organizations and Society 24 (1999) 317±331
merits of this revised de®nition against not only
internal requirements, but also external requirements for information, such as real-time, on-line
information retrieval by investors.
Future research is also needed to investigate
speci®c changes that might stem from a change in
the construct level de®nition of AIS. Implementation problems, at the instantiation level, associated
with realizing a rede®nition such as that described
above are largely due to the way accounting
information has traditionally been de®ned. David
(1997) investigates how the changing de®nition at
the construct level requires changes at the model
level. She ®rst de®nes three types of events: economic events (actual transactions changing
resource quantities), business events (information
for planning and control) and information events
(means of communicating information about the
former two events). Then, using a REA framework, she illustrates how a traditional accounting
system (i.e. limited to economic events) could
evolve into a very di€erent model if business and
information events were included. She also discusses the implications of this modeling for process
reengineering, noting that cost-bene®t analyses are
vital in considering alternative models and methods. Future research could empirically validate the
match between a revised de®nition of accounting
information systems at the construct level and an
REA model for implementation.
Just as the construct level e€ects the model level,
decisions made at the model level e€ect the methods and instantiations level. For example, instantiations of arti®cial intelligence (AI), using neural
nets and case-based reasoning, are able to quickly
discern patterns in data that would take years to
discover using older techniques. Several banks and
credit card companies are now using AI to study
patterns of individual credit card usage to detect
transactions that are potentially fraudulent (Widrow,
Rumelhart & Lehr, 1994). It is a revised accounting
model (maintaining a wealth of detail transaction
data) that makes AI methods possible. The decision
to model the accounting system multidimensionally
allows even traditional transaction data to be used
in new instantiations. The meta-theory model
could guide future research in identifying and
examining the e€ects of other such applications.
327
3.4. Research proposition No. 4
The fourth research proposition states that
system design alternatives should explicitly consider complementarities and ®t between strategy
and structure. Complementarities in organizations, introduced by Milgrom and Roberts
(1995), are based on the idea that activities are
complements if doing more of any one of them
increases the returns to doing more of the others.
In a system with strong complementarities, changing only a few of the system elements at a time
may not achieve all the bene®ts that are available
through a fully coordinated move, and may even
have negative payo€s (Milgrom & Roberts,
1995). Frances and Garnsey (1996) demonstrate
how capitalizing on complementarities in technology, distribution, and employment practices
allowed supermarkets in the United Kingdom to
change the structure of market control, resulting
in dramatically higher net margins than in many
other countries.
Greater recognition of, and empirically testing,
research proposition four is particularly important
in research building or evaluating emerging technologies. For example, the Internet has the potential to completely rede®ne external ®nancial
reporting as we know it today. Elliott (1995) predicts real-time business reporting and investor
access to corporate databases. Research de®ning
complementarities in ®rm strategy and structure
governing existing external relationships and how
they are changing seems particularly appropriate
to avoid negative consequences of such an implementation. There is a huge gap between having the
technology available to provide any user any data
within the ®rm and having a system that will control both the accuracy of the data (important to
the external user) and the legitimacy of, and limitations on, data requests (important to the ®rm).
AI is theoretically capable of providing a vehicle
of control. The meta-theory model could guide
future research analyzing the e€ect of AI on
®nancial reporting concerns.
Another example of capitalizing on complementarities involves group support systems
(GSS), a category of technology that has been
extensively studied within the MIS domain and
328
E.G. Mauldin, L.V. Ruchala / Accounting, Organizations and Society 24 (1999) 317±331
recently considered in audit research. Bamber, Hill
and Watson (1998) review prior audit group
research as well as recent audit GSS research and
provide a series of propositions for future AIS
research, speci®c to auditing. They note inconsistent ®ndings in MIS research and suggest the
importance of evaluating potential GSS applications in a context-speci®c basis. The framework
they develop for further GSS audit research
explicitly encompasses task characteristics as well
as the group setting and expected GSS process
gains and losses (Bamber, Watson & Hill, 1996).
The authors note a lack of GSS research in the
areas of con¯ict resolution and execution tasks,
which are potentially important tasks in the audit
function.
Research proposition four goes one step further
to recommend examination of the adoption and
di€usion of GSS. The process gain and process
loss perspective can help identify complementarities. For example, the e€ects of networking and
workpaper automation through GSS could be
positive, in allowing reviewers more freedom, or
negative in having less personal contact with audit
sta€ and client representatives. With networking
technology, GSS is becoming an important part of
audit environments due to the need for interaction
among the audit team and the geographic dispersion of audit activity. Reaping the bene®ts of this
technology might also require complementary
procedures to mitigate the negative impacts. The
meta-theory model can guide research investigating how the process gain and loss tradeo€s are
made within organizations. Such research would
be relevant to increasing the success of system
innovations, not only in auditing, but also in other
areas.
Research proposition four applies to models as
well as instantiations of system design alternatives.
For example, the development of the REA model
discussed earlier is predicated on reduction of
redundancy and making data structures more ecient. However, Van Alstyne, Brynjolfsson and
Madnick (1995) argue that, under some current
organization structures, an REA type model may
not be e€ective. Future research identifying complementarities in organization structures could,
once again, impact implementation strategies.
4. Conclusions
Technology is a pervasive and growing component of accounting tasks and has been shown to
change work processes. The meta-theory model
for AIS research contained in this paper explicitly
recognizes this factor. Currently, multiple research
methods are being employed to investigate dual
AIS/accounting problems. This is especially evident
in managerial accounting, where experimental, ®eld,
and analytical work has addressed the interrelationships between managerial tasks and AIS design and
use. Less evident is an explicit combined strategy to
employ multi-methods in a manner that most e€ectively uses the strengths of each approach (Peters,
1993). The meta-theory model develops a comprehensive framework that can be used to help organize
and interpret future research.
The meta-theory model may also provide a
vehicle to encourage the integration of AIS
research with the other sub-disciplines of
accounting research. Explicitly considering task
commonalities and di€erences between sub-disciplines may avoid unnecessary duplication of
research as well as accelerate the advance of AIS
research.
This paper develops a meta-theory model for
AIS research that begins with a task focus and
suggests a matching process between requirements
of the task and system design alternatives at four
di€erent levels of analysis. Further, the model
suggests that technological, organizational, and
cognitive contingency factors impact the outcome,
task performance. The three contingency factors
are developed from a review of the extant literature and are complimentary, each focusing on an
important aspect of AIS design and use in
addressing accounting issues. The model provides
a nomological structure that can be used in future
research to identify variable combinations and critical interactions for speci®c task settings. Four
propositions for future research are also identi®ed
using the proposed model. These propositions illustrate questions that have been under-researched
regarding the temporal ordering of contingency
factors, the relative importance of contingency factors for di€erent tasks, the consequences of system
design choices and the complementarities in AIS.
E.G. Mauldin, L.V. Ruchala / Accounting, Organizations and Society 24 (1999) 317±331
Acknowledgements
We appreciate helpful comments received from
Vicky Arnold, John Barrick, Jean Cooper, Severin
Grabski, Scott Jackson, Uday Murthy, Bob
Ramsay, Mark Taylor, the 1997 AIS Research
Symposium participants, and the anonymous
reviewers. The second author also gratefully
acknowledges the ®nancial support of a KPMG
Peat Marwick Research Fellowship.
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