Determinants of Auditor Reliance on Decision Aids
Cam Cockrell
Ph. D student
University of Kentucky
Thanks to Bob Ramsay for his comments and guidance on this paper. I also wish to thank Dan
Stone for his comments and suggestions. Also, thanks to the participants of the University of
Kentucky brownbag for providing a multitude of helpful comments.
Abstract
Recently, audit firms have instituted striking increases in the levels of technology used
during an audit. Firms continue to accept and incorporate new decision aids into their audits. In
order to maximize the benefits of using decision aids, a vital concern for audit firms is to
understand the implications for and determinants of decision aid reliance by auditors. Although
extant research has attempted to isolate some of these factors, studies in this area have not been
crafted from a uniform theory. This literature review synthesizes research on auditor decision
aid reliance, and places it within the context of the Technology Acceptance Model (Davis 1989)
and the Theory of Technology Dominance (Sutton and Arnold 2002). The results of this paper
suggest a model of auditor decision aid reliance for empirical testing and future research.
1
1. Introduction and Motivation
1.0 Introduction
Decision aids have the potential to add significant value to an audit. From choosing to
accept a new client, to planning the engagement and making judgments throughout the audit,
decision aids can help auditors prevent omissions and mistakes (Abdolmohammadi and Usoff
2001). In order for a decision aid’s benefit to be fully realized, some level of reliance must exist.
Prior literature has gleaned valuable information about different factors that influence auditor
reliance on decision aids; however, no uniform theory exists to unite these determinants of
reliance and provide insight as to driving force between them. The purpose of this paper is to
develop a framework for auditor decision aid reliance which draws from theory and extant
literature.
1.1 Structure
This paper is structured into five sections. The first section contains the introduction, and
motivation. The next part of the paper discusses different types of decision aids and how they
map into the audit (Section 2). The following segment of the paper (Section 3) describes auditor
characteristics, and explains what is unique about auditors and their tasks, as they relate to
decision aids. Next, I synthesize the literature under review (Section 4) and conclude with
suggestions for future research (Section 5).
1.2 Motivation
The auditing environment is unique from many other decision-making contexts. The
regulatory environment surrounding an audit creates a need to justify decisions at all levels
(Ashton 1992). Individual auditors must justify their decisions to their superiors; the audit firm
2
must justify its decisions during peer review; and both individuals and the firm may have to
justify decisions during litigation.
The emphasis that an audit places on the justification requirement cannot be overlooked
when examining decision aids within an auditing context. As will be discussed in more detail,
many over and under-reliance problems are related to justification issues. Experience can cause
auditors to believe they have a better feel for particular situations than decision aids, thus leading
to under-reliance. Similarly, inexperienced auditors may not question decision aid outcomes, or
might be in decision-making situations that they have not yet been trained to handle, thus leading
to over-reliance.
Both under and over reliance on a decision aid can be problematic, as illustrated in figure
1 below. Depending upon the task, different costs can be associated with each type of error.
Accountants avoid relying upon decision aid outcomes (e.g. Ashton 1990; Ashton 1992;
Boatsman et al. 1997). This becomes problematic when a decision aid outperforms human
judgment on a particular task, causing under-reliance and suboptimal decision-making. It is
optimal to supplement decision aid outcomes with professional judgment, but not to rely solely
on either one (Kleinmuntz 1990).
Rely
Do Not
Rely
Figure 1
Decision Aid is
Decision Aid is
correct
incorrect/misleading
1) Efficient use of 2) Reliance is a
aid reliance
problem (Type I
error)1
3) Non-reliance is 4) Efficient use of
a problem (Type II self reliance
error)
1
This can be thought of as a miss by the decision aid. Alternatively, in the reliance when decision aid is correct cell,
a suboptimal decision to rely may still be made. For example, an inexperienced auditor performing some task may
not question the decision aid or be able to incorporate their professional knowledge into the decision-making process
within the task domain. A further discussion of this scenario can be found in section 6 of this paper.
3
Examining higher-level auditors’ views toward tasks that may benefit from decision aid
use may prove valuable in evaluating the culture surrounding an audit. One might expect
reliance to be improved in a culture that views decision aids as more useful than not. In
scrutinizing how managers and partners view decision aid usefulness, Abdolmohammadi and
Usoff (2001) found that between 1988 and 1996, higher level auditors increased their view of
what tasks could benefit from decision aid use from 11% of audit tasks identified to 16%. This
trend shows that decision aid usefulness may be gaining momentum as the degree of
technological acceptance increases. However, despite these findings, managers and partners
evaluated over half of the tasks identified in the study as needing human processing only.2 This
outcome indicates that decision aids are not be perceived by higher-ranking auditors to be as
useful as trained auditor judgment in most situations.
For reliance implications to be a relevant research topic, auditors must have some
flexibility to accept, modify, or reject the outcome of decision aids. In practice, this contingency
is likely based on myriad factors including firm culture, aid type, and task factors. It is probable
that decisions involving more flexible inputs are less likely to be restricted in terms of reliance.
While firm policies evolve over time, recent studies support the existence of flexibility to alter
decision aid outcomes in multiple situations.
A study done by Bell et al. (2002) on a client acceptance/retention decision aid used by
KPMG (KRisk), indicates a level of freedom in the decision to rely on the aid’s outcome. KRisk
was designed such that individual risk assessments made by the aid can be adjusted up (more
2
Abdolmohammadi and Usoff define programmable tasks as those for which at least 50% of managers and partners
questioned indicated some sort of decision aid as the optimal way to perform the task. The 11% to 16% increase
therefore indicates those tasks where at least 50% of respondents indicated that “human processing only” was not
optimal.
4
risky) if its risk determination seems inappropriately low, but not vice-versa. However, the final
output of the aid cannot be altered, though the auditor may “initiate a dialogue with the
appropriate reviewing partner(s) to discuss the ‘business case’ for continuing or accepting a
client with a high risk score” (Bell et al. 2002 p. 109). Therefore, it can be argued that in one of
the most important and presumably restrictive decisions an audit firm makes, some degree of
flexibility exists regarding reliance upon a decision aid.
The riskier the decision, the more likely the auditor must initiate dialogue with a superior
in order not to rely on the decision aid outcome. However, the auditor using the aid often makes
less risky reliance decisions without the assistance of superiors. Discussions with practitioners in
the areas of sample size determination, and executing the audit plan indicates that decisions are
often made to override a decision aid’s suggested outcome. With the audit program, the senior
often selects alternative procedures when a checklist decision aid suggests a step that the auditor
deems unnecessary or unduly difficult to complete. Likewise, if a tool such as ACL or IDEA
suggests a large sample size that an auditor feels is excessive for a particular test of details, the
auditor may attempt to justify choosing a smaller sample size. Although these examples of
flexibility with decision aids exist, it is presumable that a decision not to rely must be
documented, and is certainly subject to justification should the reviewer question it.
The ultimate decision for an auditor to rely on a decision aid outcome is subject to many
influences not limited to justification and experience. This leads to some interesting questions,
including the following: “Under what conditions do auditors choose to rely or not rely on the
outcome of a decision aid?” This paper synthesizes prior literature, examines the determinants
of auditor decision aid reliance, and suggests a unification of theory and extant literature to
develop a cogent model of auditor decision aid reliance.
5
2. Decision Aids
2.0 Introduction of Decision Aids
Ideally, decision aids minimize the total cost of errors for the decisions in which they are
used. However, this goal is difficult to achieve. In order to enhance decision aid success, many
types of decision aids exists. Using an often-cited definition from Rohrmann (1986 p. 365) a
decision aid may be thought of as “any explicit procedure for the generation, evaluation, and
selection of alternatives (courses of action) that is designed for practical application and multiple
use.”
Following this characterization, decision aids may be as simple as a pencil and paper
checklist, or as complex as a large group decision support system or an expert system. In order
for them to be most effective, the type of decision aid used should be contingent upon task
factors. For the purposes of this paper, I focus on three types of decision aids because they map
directly into the audit. These decision aids are deterministic, expert systems, and group support
systems.
2.1 Deterministic Decision Aids
The least sophisticated decision aids listed in the previous discussion are simple or
deterministic aids. These types of decision aids are defined by Messier (1995 p. 214) as aids
“which may or may not be computerized (and) include any tools that aid judgment in a
straightforward algorithmic manner.” Examples of deterministic decision aids include checklists
and questionnaires. Messier and Hansen (1987) point out that deterministic aids are generally
best suited for highly structured problem domains, sometimes for semi-structured ones, and
virtually never for unstructured problems. Furthermore, deterministic aids commonly provide
whole solutions to structured problems (Rose 2002). Due to this feature, evaluating reliance on
6
deterministic decision aids is more concrete relative to other decision aid types. Since an entire
solution is provided by the aid, rather than simply guidance, the reliance decision may
completely alter the decision outcome. Simple/deterministic decision aids are likely to be
attached to tasks that are well defined, less risky, and require less experienced auditors to
perform (Messier 1995). Each of those task factors may have an incremental effect on the
reliance decision beyond decision aid type.
2.2 Expert Systems
At the opposite end of the spectrum from deterministic decision aids are expert systems.
This type of decision aid is defined by Leech and Sangster (2002 p. 66) as “a computer program
intended to mimic the decisions of the human expert.” These are tools designed to augment a
decision maker’s skill – essentially to that of some other highly skilled decision maker within the
given task domain. In effect, it allows the firm to use the expert’s knowledge without using the
expert to make the decision. This enables the firm to spread specialized knowledge throughout
the organization, without overextending the expert(s) from whom the system is designed.
When evaluating task structure, the general applicability of expert systems is opposite
that of deterministic decision aids. Unstructured tasks are usually the most conducive for expert
systems (Messier and Hansen 1987). Expert systems are sometimes applicable for semistructured tasks; however, since building expert systems can be quite expensive, it is often a
better match to use decision support systems in those contexts. Expert systems are used in
highly unstructured tasks, where the acquisition and evaluation of information is “ill defined”
and “highly uncertain” (Messier 1995 p. 215). Because of these characteristics, expert systems
generate suggested rather than deterministic guidance. Evaluating auditor reliance under this
7
circumstance is more challenging, because at issue is how to measure the degree of reliance on
decision aids if the suggested outcomes and final decisions diverge.
2.3 Group Support Systems and Group Decision Making
A third type of decision aid exists that is neither completely deterministic nor based
solely on the decision-making prowess of an expert. A Decision Support System is “an
interactive computer-based software that assists decision makers by using certain models (e.g.
statistical or mathematical models in analytical review procedures) and data to make inferences
for the use of the decision maker” (Abdolmohammadi 1999 p. 54-55). These systems generally
are used in tasks that are semi-structured, and require some flexibility. As one might expect
from the open-ended description of a DSS, they may be applied in virtually any task structure
(Messier 1995). Although some overlap exists between the applicability of a DSS relative to
deterministic aids or expert systems, as tasks become more (less) structured, a DSS loses its
advantage relative to deterministic aids (expert systems) (Messier 1995).
A special subtype of DSS applicable to auditing is Group Support Systems (GSS). The
defining feature of a GSS is that multiple decision makers integrate information electronically to
work toward some decision. That is, virtually any system where individuals working as teams
on the same project can integrate decision-making information. Examples of such systems
include, but are not limited to video conferencing, online chat sessions between teammates, and
file-sharing software. Using Rohrmann’s decision aid definition, if the GSS is part of an explicit
procedure to generate, evaluate, and choose amongst decision alternatives, then it is (or is part
of) a decision aid.
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3. Auditor Characteristics, Tasks, and how they relate to Decision Aids
3.0 Introduction of Auditor decision making
When attempting to apply the findings of general decision-making research to auditors,
one will find that a few differences exist. Solomon and Shields (1995) point out that the
tendency in the literature has been for audit decision-making studies to build on a non-audit
framework, rather than investigate the cause for subtle differences between the two. Although
not all of these differences directly relate to the reliance issue, there is some significance to these
contrasts. For example, Solomon and Shields suggest that auditors tend to show lower rates of
overconfidence than non-auditor decision makers do. The result of this may be that auditors are
not subject to the same tendencies of reliance as non-audit decision makers. In addition to
individual factors, such as overconfidence, many other influences exist in the auditing domain
that are unique, or amplified vis-à-vis a generic decision making environment.
3.1 Auditor Characteristics
When compared to partners, senior managers are even less willing to accept decision aids
as appropriate for decision support use, ceteris paribus (Abdolmohammadi 1991). Perhaps this
is due to time pressure felt by senior management to complete the audit successfully, and the
presumption that decision aided activities may take a longer time to complete than strictly human
processed decisions. The factors, discussed in the preceding paragraph, qualifying one most to
be an expert auditor emphasize a knowledgeable, experienced and confident auditor who is not
afraid of relying on his or her own ability to make a tough decision. Those characteristics that
may be consistent with reliance on a decision aid to assist in a decision-making task – such as
being perceptive, flexible, and logical, are relatively deemphasized. While it is difficult to argue
that any of these highlighted qualities are negative, the above ranking of attributes indicate a
9
perception that auditors become experts using qualities that encourage (discourage) self (decision
aid) reliance.
In an environment where legal liability and is so contingent upon justifying decisions,
auditors certainly have pressure on them to try to optimize those decisions. Ashton (1990)
provides a model (pressure-arousal-performance) demonstrating that the presence of incentives,
feedback, and pressure actually reduce decision aid reliance. A possible reason for this behavior
is that incentives drive decision makers to differentiate themselves in an effort to outperform the
decision aid, thereby placing too much confidence in their own ability. Decision makers might
even use decision aids as a starting point, and then apply heuristics in an attempt to outperform
other decision makers (i.e. their peers). An alternative explanation is that decision makers who
feel the need to separate themselves from colleagues may sometimes ignore the aids completely,
in an attempt to optimize their individual decisions. These reasons may be related to underreliance on decision aids when decision makers have high self confidence in performing given
tasks (Arkes et al. 1986; Ashton 1990; Whitecotton 1996).
3.2 Auditor Tasks
Another important consideration to take into account when examining auditor decision
aids is the task dimension. High-level auditors tend to view decision aid appropriateness on a
continuum, as more (less) appropriate for use when the task is highly (un-) structured
(Abdolmohammadi 1991). If this view is prevalent throughout the audit firms, it assists in
explaining the lack of reliance on certain decision aids related to unstructured tasks, such as
fraud evaluation aids (e.g. Hansen et al. 1996). Given the diversity of disparate tasks required to
complete an audit, and the sensitivity to task structure, one may reasonably conclude that auditor
reliance on the outcome of a decision aid is correlated with the type of task being performed.
10
Indeed, Bonner et al. (1996) find that decision aid use leads to better decision quality when a
mismatch between task structure and auditor knowledge structure exists. They also point out
that decision aids that improve decision quality in one task may not improve it in others.
Recall from the discussion in section 3 that the appropriateness of a decision aid depends
on the task structure. This brings into question what portion of non-reliance may be attributable
to how the decision aid relates to task structure. Despite this important aspect of auditor reliance
on decision aids, there is a dearth of research related to differentiated tasks. However, much of
the current research is performed relative to one specific task (e.g. fraud, client acceptance,
etc…). The weakness in this approach is that no individual study provides evidence on how
specific factors influence auditor reliance on decision aids in general.
Although many studies focus on finding ways to ensure auditor reliance on decision aids,
few examine cases where auditor non-reliance is correct with respect to decision aid outcome.
Within a fraud context, Nieschwietz et al. (2000) provide examples from extant research of six
decision models compared to auditor findings. These results show that decision aids are
significantly better at minimizing errors occurring from a rejection of fraud when fraud is
actually present, while auditors are significantly better at minimizing errors occurring from a
conclusion that fraud exists when in reality it does not. Despite making type II errors more than
half of the time when fraud is present, the ultimate overall accuracy, measured by the number of
times the existence of fraud is correctly identified as present or not present, is comparable
between total self-reliance and decision aid reliance. The reason for that outcome is that
incidents of actual fraud are perceived to be infrequent.
Despite the above findings, the costs of type I and type II errors are a relevant
comparison, and differ by task. The potential cost of concluding there is no occurrence of fraud
11
when in fact fraud exists is higher than concluding that fraud is present when it is not (Hansen et
al. 1996). Because of this assertion, much of the literature has focused on type II errors. In the
Hansen, et al. model, type II errors are weighted at 10 times more costly than type I errors to
reflect this cost. The prevailing thought is that while there are inefficiency costs associated with
increased work, and potential damage in the auditor-client relationship from type I errors, the
potential governmental sanctions and litigation costs resulting from type II errors far outweigh
those costs (e.g. Enron). Alternatively, a type I vs. type II sample size determination error may
not have the same differential cost impact as compared to the fraud scenario.
Hanson et al. (1996) find that when fraud does not exist, the auditor is rarely ever wrong,
while decision aids have a much higher frequency of false positive errors. However, when fraud
does exist, auditors rarely conclude that fraud exists, causing a costly type II error. Because
auditors are trained to use professional skepticism, they may be predisposed to become
increasingly cynical as they see more errors made by a decision aid. When auditors see a
substantial number of type I errors from decision aids, they may be more apt to distrust the aid.
When type I and type II errors have different costs, and the decision aid is a better predictor of
the more costly error, this distrust may become problematic.3 The implication is that decision
aid design research might also need to focus on reducing each type of error, rather than focusing
on the more costly type, so that auditors will rely more optimally on them.
3
When type I and type II errors have different costs, the bias of the decision aid may be calibrated so that the cost of
making an error is reduced at the expense of overall accuracy. For example, if a type II error is more expensive than
a type I error, the decision aid may be biased in a way that it is more (less) likely to register a false alarm (miss).
This could have the effect of lowering the cost of making an error with the decision aid, but reducing the overall
accuracy of the aid.
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4. Auditor Decision Aid Reliance Research
4.0 Organizing Framework
Due to the multitude of factors that make both auditors and their tasks uniquely different
from others, it is difficult to classify extant research on the factors themselves. However, major
differences do exist between studies, particularly between those that examine factors causing
over- or under-reliance decisions and those focusing on usability or decision performance. To
organize the literature under review, I use the framework in figure 2 (below) which is slightly
modified, but based on Rose (2002) in which differing perspectives are used. Under-reliance,
over-reliance, usability, and performance perspectives are used as the organizing perspectives.
Figure 2: Organizing Framework for Auditor Decision Aid Reliance
Decision
Aid
Auditor
Making
Decision
1, 2
3
3
Decision
Process
4
Decision
Outcome
1 – Over-reliance
2 – Under-reliance
3 – Usability
4 – Performance
These perspectives are based on the core determinants of the decision making outcome.
Studies that focus on over or under-reliance by the auditor on the decision aid are characterized
as over or under-reliance papers within table 1 (Panel A and B, below). Usability may affect the
decision process through the decision aid or directly from the decision maker to the process.
Research that has reliance implications based on either of these effects is classified as usability
13
research within table 1 (Panel C, below). Also, some studies focus directly on the decision
outcome and evaluate the performance of the decision process. These studies are classified as
performance papers within table 1, (Panel D, below).
4.1 Synthesis of Current Literature
Under-reliance
Statistical models tend to generate relatively more reliable outcomes than do individual
decision makers (Kleinmuntz 1990). Despite this, auditors often avoid reliance on decision aids
that are based on statistical models (Ashton 1992; Boatsman et al. 1997). While only three of the
ten studies appear in this section, most of the other studies also take the perspective that
increasing reliance usually equates to better decision quality.
Boatsman, et al. (1997) encountered interesting problems with decision aid reliance.
Auditors whose first reaction was to rely on a decision aid often shifted their final judgments
away from decision aid outcomes. Some users appeared to ignore the aid altogether. Boatsman
et al. (1997) point out that intentional shifting appears to be subject to the same factors as in
Ashton’s pressure-arousal-performance model. This supports a combination of incentives,
feedback, and pressure resulting in lower reliance within an auditing / fraud determination
context. Eining, et al. (1997) provide a possible solution for the ignoring problem, through
constructive dialogue. Combining active involvement and continuous feedback elements,
constructive dialogue may lead to higher reliance.
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Table 1 -- Empirical Studies -- Auditor Reliance/Non-reliance on Decision Aids (1995-Present)
Panel A: Under-reliance
Study
Research Issue(s)
Context
Data / Participants
Outcome
Boatsman,
Moeckel, and
Pei (1997)
Will users change to
rely, ignore,
intentionally shift, or
remain in agreement
with a decision aid
under different
decision
consequences?
 Fraud determination
 Told that decision
aid outperforms firm
experts
 Auditor makes
initial judgment, has
to justify answers
within the aid, then
makes a final
determination
 Five cases each—
one warm up, two
fraud, and two nofraud
 Electronic use of
decision aid with
24 yes/no red flag
questions
 Auditors split into
five different
treatment groups
and one control
group.
 118 audit seniors in
an international
accounting firm
As a whole, when the
initial judgment is no
fraud, and the aid predicts
fraud, ignoring the aid is
significant
Does predictive ability
information influence
decision makers
differently depending
on their locus of
control?
 Bond rating
predictions
 Mechanical
Decision Aid
 Similar experiment
to Ashton (Ashton
1990; Ashton 1992)
 Laboratory
experiment
 91 auditors from a
big six audit firm,
each with
approximately three
years of experience
(predictive ability)
 61 masters level
business students
(decision maker
involvement)
Internal control locus
participants demonstrated
significantly less reliance
on the decision aid than
did external control locus
participants.
Kaplan,
Reneau, and
Whitecotton
(2001)
Does decision maker
involvement in the
decision aid’s
development affect
reliance by users based
on their locus of
control?
15
As a whole, when the
initial judgment is fraud,
and the aid predicts fraud,
intentional shifting is
significant
Internal locus of control
participants, who were
involved with the decision
aid’s development,
demonstrated significantly
higher reliance on the
decision aid.
Decision Aid Reliance
Implications
 Ignoring decision aids
remains a large problem.
There was no clear reason
for ignoring the aid.
 Adds credence to Ashton’s
(1990) pressure-arousalperformance hypothesis.
 Understanding how to
address individual
differences may lead to
greater decision aid reliance
 Allowing decision makers
to stay involved with a
decision aid’s development
may increase reliance for
internal locus of control
individuals
Panel A continued
Study
Research Issue(s)
Lowe and
Reckers
(2000)
Will auditors provided
with negative outcome
information about a
firm provide greater
inventory
obsolescence
judgments about the
company?
If cued about potential
legal scenarios, will
auditor’s assessments
of inventory
obsolescence be
different?
If an auditor is
required to generate
multiple potential
outcomes, will that
affect inventory
obsolescence
assessments?
Panel B: Over-reliance
Anderson,
Will auditors rate
Moreno, and
decision aid
Mueller
explanations more
(2003)
reasonable than client
explanation when both
are insufficient to
account for the full
fluctuation?
Context
Data / Participants
Outcome
 Publicly traded
manufacturing
company with a
recent decline in
performance
 Financial
statement ratios
slightly above
debt covenants
 Potential
obsolescence of
one of the firm’s
highest selling
items
 Participants asked
for likelihood of
adjustments
needed to issue an
unqualified
opinion
 Design consists of
four groups.
Foresight groups
were further
manipulated via
outcome knowledge
(none, single, and
multiple) with the
final group consisting
of a hindsight group.
 131 audit seniors
from a big 5 firm
with an average of 3.3
years experience
The decision aid
prompting auditors to
consider potential legal
scenarios significantly
affected auditor judgment
of inventory obsolescence
relative to those receiving
no cue, but not from those
in the hindsight condition.
 Medium size
manufacturing
client
 Perform analytical
procedures
 Lab experiment
 51 randomly assigned
big 5 auditors with a
mean of 74 months
audit experience
Auditors receiving
explanations from decision
aids rated the explanations
as more sufficient than
those receiving client
explanation
16
Decision Aid Reliance
Implications
 Foresight decision aids may
have the ability to influence
reliance by prompting
auditors to consider
potential scenarios that nonforesight aids do not.
Prompting auditors to
consider multiple potential
outcomes appeared to have
a marginal incremental
effect relative to cuing one
possible outcome.
 Inappropriate sufficiency
judgments about decision
aid information, possibly
due to its perceived
objectiveness as a source
 Potential for ending the
information search too early
in the analytical procedure
stage of an audit
Panel B Continued
Study
Research Issue(s)
Swinney
(1999)
Do auditors over rely
on expert systems
when they are
incorrect?
Context
 Auditor
evaluation of
loan loss reserves
by a financial
institution
Do auditors rely more
heavily on negative
outcomes than positive
outcomes?
Panel C: User Preference
Dilla and
What are the
Stone (1997)
differential effects on
auditors of using
numeric cues and
responses relative to
linguistic cues and
responses?
Do the cue and
response types affect
cognitive effort?
 Computer-based
experiment with
custom software,
with risks based
on professional
literature
 Participants obtain
hidden cue
information by
prompting the
system, and
respond by
making risk
assessments.
Data / Participants
Outcome
 Cases distributed to
one control and two
treatment groups
Results suggest an over
reliance on expert systems
 29 practicing auditors
from three national
accounting firms
Decision Aid Reliance
Implications
 Due to the sophistication of
the decision aid, auditors
may over rely on incorrect
expert systems outcomes
Auditors relied on negative
information from the
expert system to a greater
degree than positive
information from the
expert system
 Auditors rely more heavily
on negative outcomes from
an expert system than
positive outcomes
 2 x 2 betweenparticipants crossed
design (cues x
responses) balanced
by firm
 71 auditors (60 from
big 6 firms, and 11
from smaller national
firms; 5 staff, 36
seniors, 24 managers
and 6 partners)
Linguistic cue
representation increases
agreement on cue weights,
but not judgment
consistency.
Numeric response
representation increases
judgment consistency, but
not agreement on cue
weights.
Linguistic (numeric) cue
(response) representation
increases time spent per
cue acquired
17
 The differential cognitive
demands by cue type may
have implications tied to the
technology acceptance
model ‘perceived ease of
use’ construct, ultimately
affecting reliance.
Panel C continued
Study
Research Issue(s)
Eining, Jones,
and
Loebbecke
(1997)
Will auditors rely to a
different degree on
checklists, logit
models, and expert
systems?
Will constructive
dialogue within expert
systems increase
auditor reliance?
Messier,
Kachelmeier,
and Jensen
(2001)
Do auditors work
backward from a
desired outcome to
justify their decisions?
Will the use of a
supplemental
worksheet help
circumvent the
working backward
problem?
Context
Data / Participants
Outcome
 Fraud
determination
 Expert system
provided
constructive
dialogue
 Computerized
 Laboratory
experiment
 Four groups—one
control, one checklist,
one logit, and expert
system
 Three total cases—
one low, one
medium, and one
high risk
 93 auditors from a big
six accounting firm
averaging 3.2 yrs.
experience and 23
audit engagements
Control group and
checklist group did not
significantly differ.
 Between-participants
design with two
experimental factors
(AICPA nonstatistical
sample guidance
[four levels] and
population size [two
levels])
 149 auditors (44 staff,
82 seniors, and 20
managers) with an
average of 3.6 years
experience from two
big six firms
Working backward
phenomenon exists
causing auditors to choose
sample sizes significantly
less than what is sufficient
 Sample size
determination
based on strong
internal controls
 Supplies
inventory from a
small consumer
appliance
manufacturer
18
Members in the logit group
relied on the decision aid
to a greater degree than did
the control group.
Members in the expert
system group performed
better than the logit group.
Sample size was increased
and consistency enhanced,
but the working backward
problem was not solved
Decision Aid Reliance
Implications
 The design of a decision aid
can ultimately increase
auditor reliance in the aid
 Use of an expert system
with constructive dialogue
can allow auditors to make
decisions regarding
additional audit actions
required by the evaluation
of risk
 As auditors perceive a
decision aid outcome to be
counter-intuitive, they are
likely to find a solution they
find intuitive and work
backward through the aid to
justify their decision.
 Design of decision aids
should take into account this
problem to make outcomes
seem more intuitive
Panel D: Performance
Study
Research Issue(s)
Bedard and
Graham
(2002)
Does the orientation of
a decision aid (positive
vs. negative) affect
auditor judgments of
the risk factors for an
existing client?
Context
Data / Participants
Outcome
 Identification of
specific risk factors
related to auditors’
existing clients.
 Risks are evaluated
and audit planning
decisions made.
 Cases distributed to
matched pairs of
auditors who had
prior experience
working together.
 Decision aid
manipulated by
positive and
negative orientation
(wording).
 46 big 5 auditors
(19 seniors, 25
managers, and two
partners) 6.9 years
avg. experience
Results suggest that the
framing of the decision aid
influences auditors.
 Computerized
decision aid
 Auditors perform an
analytical review,
where the client’s
inventory turnover
substantially
decreased from the
prior year.
 Two-way betweenparticipants design,
where client risk is
either high or low,
and goal framing is
inclusive or
exclusive
respective to
potential
explanations.
 65 big 5 auditors
(19 staff, 20 senior,
20 managers, 2
partners, and 4 did
not indicate)
An inclusive goal frame
(high-risk client) lead to a
significantly larger number
of potential explanations.
Will auditors plan an
audit differently as a
result of using a
decision aid with
positive relative to
negative orientation?
Mueller and
Anderson
(2002)
Does a higher client
risk level (or inclusive
goal framing) lead to a
greater number of
potential explanations
during an analytic
procedure task?
Is there an interaction
effect between client
risk and goal framing
on the number of
potential explanations
during an analytic
procedure task?
19
Negative framing
influences auditors to
recall more risks about
existing clients than
positive framing. This
leads to a greater amount
of audit tests chosen in the
planning phase.
No interaction effect was
found between goal
framing and client risk.
Decision Aid Reliance
Implications
 Framing did not affect the
auditors’ initial
inherent/control risk
assessments. It is possible
that this is evidence of a
form of non-reliance on the
decision, though other
causes cannot be dismissed.
 Auditors appear to be
relying on the aid by
implementing more tests
under negative framing,
though auditor inputs differ
based on their framing
condition.
 Biases built into a decision
aid may influence reliance,
but make the decision aid
less effective/efficient,
depending on the task and
error costs.
Another factor that may lead to greater reliance is how differences in individual decisionmakers’ personalities may affect decision aid reliance. Kaplan, et al. (2001) show that auditors
with an internal locus of control were more likely to under-rely on decision aid outcomes. Thus,
we may be able to now focus on internal locus of control auditors and design decision aids to
increase their reliance, since those with an external locus of control demonstrate significantly
higher reliance.
Over-reliance
The differences between type I and type II reliance errors in the current studies highlight
the importance of understanding the precise factors leading to both under and over-reliance, and
the conditions under which they obtain. Certain consequences not yet discussed have been
identified in the current research regarding both over and under reliance. Anderson et al. (2003)
find that auditors may view decision aids as more objective than the client as a source of
information. Because of this, client explanations were rated lower than those generated by the
decision aid were. Overrating decision aided explanations may improperly divert auditors away
from investigating possible irregularities (Anderson et al. 2003).
As previously stated, most decision aid studies are motivated by emphasizing improving
decision quality through increasing reliance. A few studies, however, have begun examining
over-reliance issues. Early studies in this realm indicate that in certain contexts, auditors may in
fact over-rely on decision aids – specifically when using expert systems (Swinney 1999) and
during the analytical procedures phase (Anderson et al. 2003). Swinney’s finding that auditors
over-rely on an expert system in a loan loss reserve context could is a prime example of a
mismatch between auditor task experience and sophistication of a decision aid. This type of
reliance is suboptimal however, as users are not supplementing decision aid outcomes with their
20
professional knowledge. Under this scenario, the decision effectively becomes automated, and
the user is contributing little to the decision process. Further research must be done in this area
to understand the implications of this type of reliance, and how to avoid it.
Usability
The usability of a decision aid can also affect user reliance. Both usability and
performance variables have direct implications on decision aid design. In some cases, rather
than under-rely on a decision aid, auditors may manipulate or misuse decision aids to justify their
intuitive decisions. The Messier et al. study (2001) shows that there is a “working backward”
phenomenon, first found by Kachelmeier and Messier (1990). In their study, Messier et al.
(2001) found that during a sample size determination task, auditors choose an intuitive sample
size and then work backward through the decision aid to justify their choice. This dysfunctional
behavior relates to the auditor not understanding the statistical reasons for the aid’s results. To
remedy this problem, Messier et al. (2001) suggest statistical training, or auditor involvement in
an internal decision aid design. It is notable that the idea of involvement with the aid’s
development can increase reliance is consistent with the findings of Kaplan et al. (2001).
Another way of handling the working backward problem is through the type of decision
aid used. For example, Bamber et al. (1998) point out that group support systems are less likely
to have working backward problems. Although system designs that discourage working
backward can mitigate that problem, they may be potentially less intuitive to auditors who may
prefer the transparency of seeing how the decision aid works. In other words, instead of
remedying the working backwards problem, there is a potential to transform it into an underreliance problem. Still, current studies continue to show that the design of decision aids,
including such features as constructive dialogue, can increase reliance (e.g. Eining et al. 1997).
21
Another study that may have preference implications is Dilla and Stone (1997). In their
study, they found that linguistic (numeric) cues (response) increased the time spent per cue
acquired relative to numeric (linguistic) cues (response). In light of this finding, it is possible
that users notice the cue or response type difference, and perceive some relative disparity in ease
of use. However, since Dilla and Stone found differences in judgment consistency based on cue
or response type, a perceived usefulness construct may also have an impact. To determine if
auditor reliance implications exist based on numeric and linguistic differences, more research
must be done in this area.
Performance
The performance dimension refers to those studies that have reliance implications based
on certain features within the decision aid or between the aid and task. The studies within this
subsection tend to be less direct in terms of evaluating reliance, but have implications for it
nonetheless. Many of the studies are based on the framing of linguistic cues within a decision
aid.
One factor often studied within the literature is the effect of framing a decision aid in
different ways. Within these studies, it is common to examine the impact of how information is
presented on the actions of the user. Lowe and Reckers (2000) examined a decision aid
prompting auditors to consider the possibility of potential legal scenarios if an inventory
obsolescence problem exists and is not found by the audit team. They found that framing the
decision aid to prompt auditors to think about their legal liability if errors were made had a
significant impact on the auditors’ judgments.
Other framing studies have either found mixed results, or cautioned against the potential
costs of differing aid structures. Mueller and Anderson (2002) examine a decision aid used
22
during analytical review to determine which alternative explanations to test. They find that the
decision aid with an inclusive goal frame resulted in a larger number of potential explanations in
an analytic procedure task relative to an exclusive goal frame.4 Bedard and Graham (2002)
found that while framing did not affect auditors’ initial inherent/control risk assessments, it did
affect the number of tests implemented during the planning phase. Within the context of making
multiple decisions, framing effects on reliance at the individual level versus the aggregate
decision level should be evaluated. Although these sorts of biases may be built-in for reliance
purposes, the task and error costs must be considered to assess the cost/benefit of instituting such
factors into the aid.
5. Conclusion and Future Research Implications
5.0 Conclusion and Future Research Questions
The biggest drawback to this area of research is the lack of uniform theory. Various
studies have highlighted different factors that either do or do not influence auditor reliance
within different contexts, yet we still have no set theory of decision aid reliance. Recently,
Arnold and Sutton (1998) have submitted the Theory of Technology Dominance (TTD), which
models the general factors leading to reliance. Audit decision aid research should continue
moving in this direction and empirically test these types of models to establish underlying theory
as to what factors influence reliance. Throughout the course of this review, many determinants
of decision aid reliance have been found in addition to those identified by both the Technology
Acceptance Model and TTD (see figure 3 below).
4
The goal frames differ on whether the auditor should choose to test alternative explanations from a long list of
alternatives (inclusive) or should not (exclusive). Mueller and Anderson point out that deciding which to include or
exclude are logically identical, however the framing may cause auditors to process the decision differently.
23
5.1 Technology Acceptance Model and the Theory of Technology Dominance
At its core, the Technology Acceptance Model (TAM) (Davis 1989) establishes a causal
relationship between potential users of some technology, and their eventual acceptance or
rejection of that technology. TAM posits that the user’s intention directly determines whether
the individual will use the technology or not. This behavioral intention is driven by the user’s
perceptions about both the usefulness and ease-of-use of the technology.
Figure 3
Factors shown to increase or have no effect/decrease reliance on decision aids5
Increase reliance
No effect or decrease reliance
Active Involvement (Davis and
Counter-Intuitiveness (Messier et al. 2001)
Kottemann 1994)
Continuous Feedback (Davis and
Internal Locus of Control (Kaplan et al. 2001)
Kottemann 1994)
Constructive Dialogue (Eining et al. Incentives, Feedback, and Pressure (combined)
1997)
(Ashton 1990)
Ease of Use (Davis 1989)
Increasing Task Difficulty (Brown and Jones
1998)
Face Validity (Ashton 1990)
One-Time Outcome Feedback (Arkes et al. 1986)
Framing Effects (Lowe and Reckers Pattern Matching (Hoch and Schkade 1996)
2000; Mueller and Anderson 2002)
Intuitiveness (Kachelmeier and
Self-Confidence (Arkes et al. 1986; Ashton 1990;
Messier 1990; Messier et al. 2001)
Whitecotton 1996)
Perceived Usefulness (Davis 1989;
High degree of Task Experience (Whitecotton
Venkatesh and Davis 2000;
1996)
Venkatesh et al. 2003)
Previous Decision Aid Experience
(Whitecotton 1996; Whitecotton and
Butler 1998)
Relative Source Objectiveness
(Anderson et al. 2003)
Rule Description (Davis and
Kottemann 1995)
Sophistication of the Decision Aid
(Swinney 1999)
5
This is a summary of outcomes found and explained throughout the course of this review. Also, note that I include
research that is not specific to auditors, or did not use auditor participants, in italics.
24
The Theory of Technology Dominance (TTD) (Arnold and Sutton 1998) was designed in
part to explain a user’s tendency toward or away from reliance on intelligent decision aids.
When task experience is low, TTD hypothesizes that there will be a tendency toward reliance.
This type of reliance is suboptimal because the user does not have personal task knowledge to
factor in with the decision aid suggestion. When task experience is high, and task complexity,
aid familiarity and cognitive fit are all high, TTD hypothesizes that there will be a tendency
toward reliance.6 This type of reliance is optimal. However, when task experience is high but
task complexity, aid familiarity, or cognitive fit are low, there will be a tendency toward nonreliance. This non-reliance is suboptimal because the decision aid is not being used to
supplement personal task knowledge. TTD further theorizes that over the course of time, a
decision maker with high task experience using an intelligent decision aid will eventually lose
skill related to that task.
5.2 Combining TTD, TAM, and Extant Research
Although much research has been done in the area of decision aids, we still lack a
uniform theory. Auditors have shown a general under-reliance tendency on decision aids. Now,
as technologically advanced decision aids continue to gain acceptance, recent studies show
instances of over-reliance problems as well. Each of these problems presents potential benefits if
solved, and potentially tremendous consequences that remain largely unexplored. Continuing to
identify and understand the factors and consequences under which auditor reliance on decision
aids is influenced remains an important task.
TAM includes usability and performance related constructs that are applicable to decision
aid reliance research. TTD includes a model of factors hypothesized to influence reliance on
Arnold and Sutton define cognitive fit as “the degree to which the cognitive processes used with the decision aid to
complete or solve a task match the cognitive processes normally used by an experienced decision maker” (p. 180
1998)
6
25
decision aids. These factors include the task experience of the user, the complexity of the task,
familiarity of the user with the decision aid, and the cognitive fit between the processes of the
decision aid and those required by the task. The determinants featured in TTD are consistent
with many of the audit based decision aid literature included in this review. TTD and TAM do
not explain all factors found in extant research, however. Absent from these theories are certain
factors that may be found at the individual level, audit firm level, within different tasks, or at the
decision aid level, such as locus of control, which have been demonstrated to affect auditor
reliance (see Kaplan et al. 2001).
Other decision aid related factors affecting auditor reliance, such as framing effects (see
Lowe and Reckers 2000; Mueller and Anderson 2002), may be a function of firm culture, which
is not captured in either of these theories. Accountability to the firm as well as the public creates
a strong incentive to avoid errors. Reminding auditors about the potential consequences to the
firm if certain decision errors are made can influence reliance. The decision to include such
framing effects or language in decision aids is likely determined by the firm culture. Firms that
encourage, or make it tougher to disagree with decision aid suggestions, are likely able to
influence auditor reliance.
In light of these theoretical limitations, I present a model that draws on extant research
and both of the above theories – TTD and TAM in figure 4 below. The model intends to capture
the major constructs parsimoniously and in a manner that can be empirically tested. TTD, TAM,
and the preceding literature imply certain propositions related to each factor listed. Those
implications, which have been discussed throughout the review, include the following. Expected
determinants of increasing decision aid reliance include a moderate degree of task familiarity, an
external locus of control, firm culture with explicit training and expectations for decision aid
26
usage, high decision aid familiarity, strong cognitive fit, high degree of justification, and salient
user perception of decision aid usefulness. As discussed previously, certain combinations of
factors might deter decision aid reliance – particularly those discussed within TTD.
Figure 4: Proposed Model of Auditor Decision Aid Reliance
Decision
Aid
Auditor*
1, 4
2, 7
3, 6
Making
Decision
1
Decision
Process
1 – Task Familiarity
2 – Locus of Control
3 – Firm Culture
4 – Decision Aid Familiarity
5, 7
Decision
Outcome
5 – Cognitive Fit
6 – Justification
7 – Perceived Decision
Aid Usefulness
* – Firm culture and the justification requirement make auditors unique from other decision makers
While literature has begun addressing auditor reliance on decision aids, opportunities for
further research exist. The lack of a unifying theory currently plagues this line of research;
however, TTD encompasses a promising model of decision aid reliance. By fitting TAM and the
reliance model of TTD together, and finding where unrelated factors found in extant literature fit
in, we can move toward developing a universal theory of auditor reliance on decision aids. It is
important to continue refining our understanding of user, task, and decision aid characteristics
that interact to cause reliance judgments.
27
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