Directory UMM :Data Elmu:jurnal:A:Accounting, Organizations and Society:Vol24.Issue8.Nov1999:
Accounting, Organizations and Society 24 (1999) 673±687
www.elsevier.com/locate/aos
The eects of a modest incentive on information overload in
an investment analysis task
Brad Tuttle a,*, F. Greg Burton b
a
University of South Carolina, The Darla Moore School of Business, The H. William Close Building, Columbia SC 29208, USA
b
University of Nebraska, USA
Abstract
This paper investigates the eects of performance based monetary incentives on cue usage within the information
overload paradigm. Participants suggested appropriate stock prices for hypothetical companies based on either six or
nine non-correlated information cues. The presence of monetary incentives motivated increased response times compared to participants who did not receive incentives. This in turn resulted in higher levels of information usage than has
been observed in previous studies. The results support the view that information processing capacity imposes a limit on
the amount of information processed per unit of time rather than on the amount of information that can be processed
in total. # 1999 Elsevier Science Ltd. All rights reserved.
1. Introduction
As producers and users of information, accountants care about the eects of information overload on decisions. Too much information is
thought to aect decision quality by producing
less accurate and less consistent responses. Some
even suggest that overwhelming an individual with
information can result in the use of less information than otherwise would occur. Accountants
neither desire to overload decision makers who
use their information, nor do they want to be
overwhelmed when making their own decisions.
Two streams have developed with regards to
information overload. One stream takes an organizational approach by recognizing that for some
* Corresponding author. Tel.: +1-803-777-6639; fax: +1803-777-6876.
E-mail address: tuttle@darla.badm.sc.edu (B. Tuttle)
decisions, what matters is the ability of the organization to process information rather than the
ability of any one individual (Schick, Gordon, &
Haka, 1990). The other stream takes an individual
approach by recognizing that many decisions are
made by individuals who must process information under various contextual in¯uences such as
time constraints (Snowball, 1980) and incentives
(Awasthi and Pratt, 1990). The present study
focuses on this latter, individual decision maker
approach.
Studies of information load eects on individual
decision making have generally relied upon a
model proposed by Schroder, Driver, and Streufert (1967) for their theoretical basis. This model
suggests that task performance will initially
improve as more information is received. But, as
the amount of information begins to exceed the
decision maker's capacity to process it, performance eventually declines. The point at which
0361-3682/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved.
PII: S0361-3682(99)00017-3
674
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
many individuals are unable to use additional
information is considered to be at approximately
seven cues but that some high ability individuals
are able to use as many as nine (Chewning &
Harrell, 1990; Miller, 1956). The model proposed
by Schroder et al. (1967) is important to accountants who prepare reports and design information
systems because they often determine how information is presented and, therefore, used by
decision makers.
One view of information processing capacity is
that it limits the amount of information that a
decision maker can process (Casey, 1980; Chewning & Harrell, 1990; Shields, 1983; Stocks & Harrell, 1995; Stocks & Tuttle, 1998). When the
amount of information exceeds this limit, (i.e.
information overload) then performance declines
(cf. Wood, 1986). If this is true, then accountants
must seek ways to reduce high levels of information in order to best support individual decision
making. Alternatively, information load has been
de®ned in terms of information per unit of time
(cf., Schick et al., 1990; Snowball, 1980). This view
implies that more information can be processed if
more time is taken, hence, information overload is
a function of both time as well as the amount of
information. Under this view, it may be possible
to motivate individuals to increase their decision
time so as to avoid cognitive information processing limits, thus allowing them to use all the
available information, even at high levels. If this is
the case, then accountants should consider decision time and motivational factors before predicting the aects of information load on decisions.
These arguments are consistent with Bonner (1994)
who relates performance to a combination of task
complexity and motivation. We know of no study,
however, that has manipulated subject motivation
with a design capable of measuring information
usage while strictly controlling for information
level and decision time. That is the aim of the
current study.
The paper proceeds by ®rst developing the
hypotheses. In this section, hypotheses regarding
information load and incentive eects on decision
time are presented. The following sections describe
the experiment, the analysis and the results. A ®nal
section discusses the implications of the ®ndings.
2. Hypothesis development
2.1. Capacity approach to information overload
Schroder et al. (1967) proposed a conceptual
model of human information processing in which
the level of information processing follows an
inverted U-shaped curve when plotted against
information load. The U-shaped feature of the
model is seen to be the result of information processing limitations on the part of individuals and is
consistent with various information processing
models (Newell & Simon, 1972; Miller, 1956).
Chewning & Harrell (1990) express this idea succinctly: ``The capability of a human decision
maker to integrate information into a decision is
believed to be limited and is thought to follow a
bell-shaped curve.'' As the amount of input information provided to the decision maker is increased,
the amount of information the individual uses in
the decision initially rises. However, beyond the
limits of human information processing capacity,
further increases in the amount of information
provided to a decision maker result in decreased
information usage. These assertions are consistent
with Wood's (1986) theory of task complexity and
Bonner's (1994) model of task complexity in which
increases in the number of inputs to a decision by
de®nition increase task complexity.
Several studies investigate this proposition by
examining decision performance under diering
levels of information (Abdel-khalik, 1973; Casey,
1980; Chervany & Dickson, 1974; Iselin, 1988;
Shields, 1983; Snowball, 1980). Information load,
in these studies, is manipulated in various ways
such as by varying the level of data aggregation,
by including or not including notes to ®nancial
statements, and by diversifying the kinds of information presented. These studies ®nd mixed results
for judgment or predictive accuracy possibly due to
dierences in the value of the inputs to decisions.
Importantly, none of these studies directly measure
information processing (the focus of the current
study), and as such do not directly examine the
Schroder et al. (1967) model.
A limited number of studies have directly
examined the eects of information load on cue
usage (a measure of information processing).
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
These studies, along with the present study, are
summarized in Table 1. Following the policy capturing paradigm, a limited number of cues are
manipulated in a within-subject factorial design to
be high or low. Subject responses are regressed on
the cue values and a count of signi®cant parameters is deemed to re¯ect cue usage. All three
studies used a ®nancial distress prediction task
with various ®nancial ratios as cues. In no study
were incentives provided or time constraints
imposed by the researchers.
Chewning and Harrell (1990) manipulated the
number of input cues to be four, six, and eight and
were very careful to ensure that all input cues were
decision relevant. They also used three dierent
groups of subjects who diered according to
domain-related experience and knowledge: undergraduate accounting students, graduate accounting
students, and practicing auditors. As can be seen in
Table 1, cue usage improved dramatically between
the four and six cue conditions but did not continue
to improve beyond the six cue condition. These ®ndings were unaected by the subjects' level of domain
knowledge and experience. Interestingly, average
decision consistency, as measured by the adjusted-R2
statistic, declined signi®cantly in the eight cue condition. A reduction in decision consistency may be
evidence of information overload when it occurs
together with a failure to increase cue usage.
Stocks and Harrell (1995) ®nd similar results
also using experienced professional subjects. Input
cues were manipulated to be six or nine and subjects completed the exercise individually or in
groups of three. As can be seen from Table 1, the
number of cues used by individuals did not
increase in the nine cue condition from the six cue
condition and decision consistency decreased. It
appears that the individuals suered information
overload consistent with an information processing capacity constraint. In addition, cue usage
increased when decisions were made by groups
rather than by individuals. This is consistent with
the notion that the information processing capacity of individuals is less than that of groups.
Nevertheless, the average number of cues used by
the groups in the nine cue condition was only 6.4,
thereby, suggesting that even groups have limited
information processing capacity.
675
Stocks and Tuttle (1998) use the same task as
Stocks and Harrell (1995) but manipulated whether the input cues were categorized into top versus bottom 1/3 of industry (as in previous studies)
or presented as numbers (percentile of industry randomly selected to be within top or bottom 1/3). Cue
usage and decision consistency in the categorical
data condition are very similar to previous studies
and strengthen the conclusion that information
overload occurs with nine input cues. Subjects who
received numeric input cues, rather than categorized
cues, appeared to suer information overload even
at the six cue level. Presumably, the numeric data
require an additional processing step to interpret
their valence (i.e. is number good or bad?) thus
resulting in information overload much quicker.
These results are consistent with the notion that
information overload results because of limited
information processing capacity.
In summary, individuals appear to have limited
capacity to process accounting related information. On average, only about six pieces of information are consistently incorporated into a single
judgment or prediction. Across the three studies
just reviewed, the average proportion of cues used
by individuals who received six cues ranges from
50% (2.9/6) to 85% (5.1/6) of the total cues available. On the other hand, individuals who received
nine cues used only 30% (2.7/9)±60% (5.4/8) of
the available cues. These studies suggest that
information overload occurs by the eight or nine
cue level Ð that even when more than six information cues are available, only about six are used.
They further suggest that the proportion of cues
that are actually used decline once the number of
available cues increases beyond about six. These
conclusions hold true whether information is presented numerically or in categorized form, whether subjects are students or experienced
professionals in the decision domain, and whether
decisions are made by individuals or by groups.
These conclusions are further supported by evidence that decision consistency declines as the
number of input cues goes from six to nine. We
formalize these conclusions regarding the eects of
limited information processing capacity on cue
usage and decision consistency with the following
hypotheses:
676
Consistency (adjusted R2)
Study
Task
Subjects
Independent variables
Cue usage
Chewning and
Harrell (1990)
Financial distress
prediction
1. Graduate accounting
students
1. Information load: 4, 6,
and 8 cues
Cue load:
4
6
8
Cue load:
4
6
8
2.9
3.2
4.9
5.1
5.5
6.5
0.80
0.77
0.70
2. Domain related
knowledge and experience
Undergraduate
Graduate
Overall
2. Undergraduate
accounting students
3. Auditors
Auditor
Overall
2.7
3.0
4.9
5.0
5.2
5.8
Bank loan ocers
1. 6 vs. 9 information cues
Cue load:
6
9
Cue Load:
6
9
2. Individual vs. group decisions
Individual
Group
4.6
5.3
4.4
6.4
Individual
Group
0.76
0.84
0.64
0.81
1. 6 vs. 9 information cues
Cue load:
6
9
Cue Load
6
9
2. Categorical vs. numeric data
Numeric
Categoric
2.9
4.5
2.7
5.4
Numeric
Categoric
0.53
0.74
0.43
0.60
1. Information load 6 vs. 9 cues
Cue load:
6
9
Cue Load:
6
9
2. Monetary incentives
provided: yes vs. no
No incentive
4.9
6.2
No incentive
0.77
0.71
Incentive
5.6
7.6
Incentive
0.85
0.85
Stocks and
Harrell (1995)
Stocks and
Tuttle (1998)
Present study
Financial distress
prediction
Financial distress
prediction
Estimate stock price
Upper division
accounting students
Undergraduate
accounting students
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
Table 1
Studies of information load eects on cue usage
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
H1. Subjects who receive nine information cues
will use no more information (i.e. a lesser proportion of the available cues) than will subjects who
receive six information cues.
H2. Decision consistency will decline for subjects
who receive nine information cues when compared
to subjects who receive six information cues.
H1 and H2 provide comparability between the
present study and previous studies.
2.2. The eect of incentives on information usage
Intuition and economic theory suggest that performance based ®nancial incentives should lead to
improvements in performance. Studies, however,
are mixed on this point. Recent research suggests
that incentives can only improve performance if
two conditions are met: (1) the incentives stimulate increased eort, and (2) the task is one in
which a positive relationship exists between eort
and performance (Kennedy, 1993, 1995; Libby &
Lipe, 1992; Stone & Ziebart, 1995). In relation to the
®rst criterion, expectancy theory suggests that performance based incentives will motivate eort
because they increase the attractiveness of achieving
the desired outcome (Vroom, 1964). Both agency
theory (Baiman, 1982, 1990; Eisenhardt, 1989) and
the organization control systems literature
(Anthony & Govindarajan, 1998) argue that economic rewards contingent on performance can be
expected to in¯uence eort. Kennedy (1993) argues
that an individual will not exert eort in making a
judgment unless doing so seems worthwhile. She
further conjectures that additional cognitive eort
can perhaps be motivated by ®nancial rewards. In
short, when an incentive is attractive, the ®rst criterion will likely be met producing more eort.
In relation to the second criterion, it is particularly important to consider the relationship
between eort and performance for cognitively
intensive tasks such as making choices, predictions, and judgments. For instance, Libby and
Lipe (1992) found that announcing incentives
before encoding resulted in greater recall of a list
of internal controls than announcing incentives
after encoding but before recall. Presumably,
677
increases in eort during memory search could not
compensate for a lack of eort during the study
period. Often, suboptimal performance is blamed
on cognitive limitations that are inherent in the
individual and are considered invariant to eort
(see Camerer, 1995 and Shanteau, 1989 for
reviews). As early as 1956, Miller (1956) suggested
that individuals' capacity for processing information is limited and provided a battery of examples
to support this proposition. All three cue usage
studies reviewed in the prior section are consistent
with this view. However, none of this work investigates the possibility that information processing
is limited in capacity only when considered within
a speci®c time period. That is, extending the time
period in which information is processed should
also extend the amount of information that can be
processed. In essence, cognitive limitations may
constrain the rate of information processing rather
than its total amount.
The notion that cognitive capacity limits information processing but only when considered
within a ®xed time period is consistent with the
de®nition of information load adopted by early
researchers in the area. For instance, Schroder et
al. (1967, p. 55) de®ne information load to be ``the
number of dimensions of information presented in
a given time span''. This de®nition has carried into
subsequent work. Snowball (1980, p. 323) de®nes
information load as, ``the quantity of information
impinging upon the processing organism per unit
of time''. Likewise, Schick et al. (1990, p. 203)
state, ``the amount of data to be processed per unit
of time is generally conceived as the information
load''. Consistent with this de®nition, information
processing capacity should be viewed as a limit
to the amount of data that can be processed
per unit of time. No prior study, however, has
empirically examined information overload per
unit of information processing time.
It is reasonable to assume that anything that
motivates additional time on an information processing task will result in more information being
processed. Applied to the present research, these
arguments suggest that monetary incentives will
motivate additional eort in terms of time spent
performing the task thus resulting in more information cues being used. If information processing
678
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
capacity limits the ¯ow rather than the level of
information that can be processed, then the number of cues used by subjects who receive outcomebased incentives will be greater than the number of
cues used by subjects who do not receive incentives. This is formally stated in the following
hypotheses:
H3. The number of cues used by subjects who
receive outcome-based incentives will be greater
than the number of cues used by subjects who do
not receive incentives.
3. Method
3.1. Design
The experiment consisted of a 22 factorial
design in which all treatments were manipulated
between subjects. The ®rst independent variable is
whether or not monetary performance based
incentives were provided the subjects. The second
independent variable is the number of information
cues available to the subjects, six versus nine.
3.2. Materials
H3a. Decision time will be greater for subjects
who receive incentives than for subjects who do
not receive incentives.
H3b. Decision time and cue usage will be positively correlated.
Notice that it is unnecessary that information
usage per unit of time increase for H3 to be supported. Some evidence, however, from the decision
choice literature, suggests that when motivated to do
so individuals switch to more eortful information
processing strategies in order to improve performance (Payne, 1976; 1982; Payne, Bettman, &
Johnson, 1988). These studies show dierences in
information usage depending on the strategy adopted. If this is the case, then it may be possible for
highly motivated subjects to adopt judgment strategies that use more information per unit of time by
thinking very hard and very quickly. However,
because more eortful strategies tend also to take
more time, it remains unclear from prior research
whether information processing per unit of time
increases or is constant. In order to investigate this
issue, we test whether incentives will lead to
increased information usage per unit of time presumably as a result of individuals adopting more
cognitively demanding strategies. If so, then the
point at which information overload occurs would
be higher under incentives.
H4. The number of cues used per unit of time by
subjects who receive incentives will be greater than
the number of cues per unit of time used by subjects
who do not receive incentives.
Materials were created that required the subjects
to estimate stock prices for a set of hypothetical
companies. Nine information cues were selected
after consulting the literature from ®nance on fundamental stock analysis (Carter & Van Auken,
1990; Damodaran, 1996; Estep, 1987; Fogler, 1993;
Muller, 1994). The information cues relate to three
commonly used factors: economy-wide indicators,
industry-speci®c indicators, and company-speci®c
indicators. Cues were selected for the six cue condition by randomly deleting one cue from each
factor area of the nine-cue condition. Instructions
for the nine-cue condition (showing all information
cues) are presented in the Appendix along with the
reduced cue set for the six cue condition.
The subjects were informed that studies have
shown (Jacoby, Mazursky, Troutman, & Kuss,
1984) that together, economic indicators account
for about 28% of the variability in real stock prices. Industry indicators account for 10% of the
changes in stock prices and company speci®c
indicators, together, account for about 62%. The
instructions were to weight the factors accordingly
and to be sure to use all the information in the
decisions. The subjects were told, for the purposes
of this study, to vary the price of the stocks
between $10 and $100.
Thirty-two cases were created based on a 1/2
factorial design in the six-cue condition and on a
1/16 factorial design in the nine-cue condition.
Because of this and because all nine cues were
chosen so that they could believably vary independently of the other cues, no two cues are redundant
by design. Cue values took one of two values and
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
679
were worded to facilitate their interpretation as to
their likely aect on stock prices. Three additional
practice cases were developed from the unused
portion of the factorial designs. One practice case
represented a relatively weak stock, one a moderate
instance, and the third represented a relatively
strong stock. Hypothetical companies were used in
order to achieve an orthogonal design. Uncorrelated cues are necessary to unambiguously measure
cue usage (Ashton, 1981).
Feedback for each case, as well as the criterion
for the performance based incentive, was developed by varying the stock price uniformly over the
$10±$100 range based upon the 28, 10 and 62%
instructions. Cues within each factor were equally
weighted. For instance, a case in which all economic factors cues are high, but all other cues are
low should be priced at $35.20 [(($100ÿ$10)
28%)+$10]. For subjects receiving an economic
incentive, cash payments were based on a percentage of the absolute dierence between their
response and the criterion stock price (i.e. the
response that would have resulted had they accurately applied the instructions and feedback)
across the 32 cases, subtracted from $6.00. The
average payment was $4.17 for about 30 min of
subject time.
The 32 experimental cases, along with the 3
practice cases, were coded into a computer program for two purposes. First, using a computer
allowed the subjects to receive feedback about
the correct stock price. We note that feedback
about stock prices is abundant, cheap, and
often immediate in the world outside the lab.
Second, and most importantly, the computer
recorded the decision time (and separately the
time viewing the feedback) associated with each
response.
3.4. Procedure
3.3. Subjects
Checks were obtained regarding the subject's
understanding of their instructions and their
interest in the exercise. After completing their
decisions, the subjects were asked to recall the
weights for the three factors: economic indicators
(28%), industry indicators (10%), and company
speci®c indicators (62%) in multiple choice format. Approximately 95% of subjects provided
correct responses to this check. Six subjects
The subjects were enrolled in upper division
accounting classes of two major U.S. state universities. On average, they had completed 6.6
accounting courses, 2.8 economic courses, and 1.4
®nance courses. The mean age was 24.3 years,
52% of the subjects were female and all but two
were accounting majors.
The subjects were randomly assigned to either the
six or nine cue conditions. Because of the diculty
of paying some subjects but not others, the incentive
manipulation occurred between semesters.
The subjects were taken to the computer lab
during a regularly scheduled class. Participation
was voluntary, however, subjects were provided
with a small number of class points as an inducement to participate. Class points were not tied to
performance. They ®rst read through the printed
instructions before starting the computer program. The computer presented the ®rst practice
cases and asked the subject for their estimate of
the stock price based on the information on the
screen. After indicating their stock price, the
computer prompted them with an ``Okay?'' If the
subject entered anything other than a ``Y'' they
were returned to reenter the stock price. Otherwise, the criterion stock price was displayed as
feedback and remained on the screen until the
subject pressed the space bar. At this point the
computer refreshed the screen with the next case.
The three practice cases were clearly labeled as
such.
After a subject completed all the cases, the
computer displayed a detailed account of their
responses, the correct responses, and the absolute
dierence between the two for each case. At this
point, the experimenter provided the subject with
a post-experiment questionnaire that he or she
completed on paper before being dismissed.
4. Results
4.1. Preliminary analysis
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B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
provided incorrect responses in the no-incentive
condition and one subject in the incentive condition (p=0.007). The frequency of incorrect
responses does not dier between information
level condition. To assure that failure to follow
instructions does not drive the results, the analysis
was reperformed after deleting the subjects who
answered incorrectly to any one of these questions.
The results did not change. The paper presents
results that include all data. Mean agreement with
the statement, ``The stock price task was interesting
and I tried my best to do well'' is 1.5 on a scale with
1=Strongly Agree to 5=Strongly Disagree. As can
be expected, responses to this measure are marginally higher (p=0.07) in the incentive condition than
in the no-incentive condition. No dierences are
observed between information level conditions.
Individual demographic data were analyzed to
determine whether experimental groups are suciently equivalent to allow between group comparisons. No signi®cant dierences between
experimental groups are observed for age, sex,
class standing (graduate, senior, or junior), the
number of ®nance courses taken, or the number of
economic courses taken. Subjects in the no-incentive condition reported taking an average of 2.8
more accounting courses than subjects in the
incentive condition (p=0.003). This is unlikely
to be a concern since having more accounting
knowledge should increase the ability of the
no-incentive group to use the information relative
to the incentive group. Thus, this works against
observing information processing dierences
between groups. SAT scores and cumulative GPA
were also examined for group dierences across
incentive treatments. No signi®cant dierences
(p>0.25) for either SAT or GPA were observed
between groups. We conclude that the experimental groups are suciently equivalent to make
valid comparisons.
4.2. Tests of hypotheses
All hypotheses were tested using 22 ANOVAs
with cue level (6 versus 9) and incentive (no versus
yes) as independent variables. The dependent
variable diered according to the hypothesis being
tested.
Hypothesis 1 predicts that the number of cues
used in the nine-cue condition will be no greater
than the number of cues used in the six cue condition and that the proportion of cues used by the
subjects will be less for the nine-cue condition than
for the six cue condition. For this analysis, cue
usage expressed as the number of cues used and as
the proportion of available cues used by the subject served as dependent variables.1 Cue usage was
obtained by counting the number of signi®cant
parameters (alpha=0.05) when regressing the
subject's responses on the cue values. Panel A of
Table 2 shows the results of the ANOVA and
Panel B shows mean proportion of cues used. As
can be seen, information load signi®cantly aected
the proportion of cues used (p=0.0041) and the
number of cues used (p=0.0001). Overall, subjects
who received 6 cues used 5.4 out of 6 (89.4%) of
the available cues whereas subjects who received 9
cues used only 7 out of 9 (77.6%). Signi®cance
levels do not change when the dependent variable
is modi®ed using the arc sine transformation for
proportions (Neter, Wasserman, & Kutner 1985,
p. 616). On closer examination, the mean cue
usage in the nine-cue condition without incentives
is only 6.2 cues, a ®nding that is very much in the
range of prior studies.
H2 predicts that decision consistency will
decline for subjects receiving nine cues when compared to subjects receiving six cues. In this analysis, adjusted R2 was used as the dependent
variable. Table 3, Panel A shows that information
load did not signi®cantly aect decision consistency (p=0.28) thus H2 is not supported. One
dierence in the present experiment from previous
information load studies is that the subjects were
1
The proportion of cues used by the subjects provides a
critical benchmark against which possible information overload
can be measured. The subjects were told to use all the cues (all
was underlined in their instructions) and that all the information was relevant to the decision. Indeed, the feedback reinforced this fact as every cue ®gured in the feedback.
Furthermore, it is entirely within the realm of possibility for
many decisions to require nine pieces of relevant information,
including the decision made by our subjects. Hence, we believe
it is reasonable to expect the subjects to use, if information
processing capacity permits, 100% of the information in the
nine-cue condition. Anything less than 100% indicates the
possibility of information overload under these conditions.
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
681
Table 2
Cue usage
Panel A Ð ANOVA
Variable
Number of cues
F-value
Information load (6 versus 9 cues)
Incentive (no versus yes)
Load X Incentive
Model (d.f.=3.98)
Proportion of cues
p-value
26.78
10.67
1.44
14.12
0.0001
0.0015
0.2336
0.0001
F-value
p-value
8.62
11.47
0.37
7.50
0.0041
0.0010
0.5433
0.0001
Panel BÐMeans
Information load
6 cues
9 cues
Average
Incentive
Average
No
Yes
4.9
82.3%
6.2
68.6%
5.7
74.2%
5.6
93.0%
7.6
84.0%
6.6
88.4%
5.4
89.4%
7.0
77.6%
Table 3
Decision consistency (adjusted R2)
Panel A Ð ANOVA
Variable
F-value
p-value
Information load (6 versus 9 cues)
Incentive (no versus yes)
Load X incentive
Model (d.f.=3,98)
1.18
15.50
1.05
6.39
0.2797
0.0002
0.3080
0.0005
Panel B Ð means
Information load
Incentive
No
6 cues
9 cues
Average
0.771
0.713
0.737
provided guidance (instructions and feedback) as
to the appropriate decision model to use. It is
likely that without some guidance, decision consistency would be aected by indecision about
which decision model to use. Importantly, there
are more possible decision models from which to
choose when there are nine cues than when there
Average
Yes
0.852
0.850
0.851
0.824
0.792
±
are only six. Hence, previously observed declines
in decision consistency might simply re¯ect the
fact that more decision models are possible with
nine information cues than with six.
H3 predicts that cue usage will increase when
incentives are provided compared to when incentives are not provided. In this analysis, the number
682
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
of cues used by the subject was used as the
dependent variable. As can be seen in Table 2,
incentives signi®cantly aected the number of cues
used (p=0.001). On average, subjects who
received an incentive used 6.6 cues (88.4% of the
available cues). This is compared to subjects not
receiving an incentive who used only 5.7 cues
(74.2% of the available cues).
Further insight into the eect of incentives on
cue usage is obtained by categorizing the subjects
into two groups: those who used all the available
cues (either six or nine) versus those who used less
than all the available cues. Obviously, information
overload can only be claimed when subjects use
less than all the available cues. Of the subjects who
did not receive incentives, only 28.2% used all the
available cues. In comparison, of the subjects who
did receive incentives, 54% used all the available
cues. This dierence is signi®cant (p=0.006). In
the six (nine) cue condition, only 8 of 16 (3 of 23)
subjects used all cues without incentives. However,
with incentives in the six (nine) cue condition, 23
of 31 (11 of 32) subjects used all the cues. Incentives strongly in¯uenced the subjects' to make use
of all available information in a situation that they
otherwise would not.
H3a predicts the process by which cue usage is
aected by incentives, i.e. through increased decision time. Decision time, as recorded by the computer, was used as the dependent variable. As can
be seen from Table 4, incentives signi®cantly
aected decision time (p=0.0011). On average,
subjects with incentives spent more time (960.3 s)
than subjects without incentives (783.4 s). Further
evidence was obtained from the computer record
of time spent viewing feedback. In this case, subjects with incentives spent signi®cantly (p=0.0006)
more time viewing feedback (136.7 s) than did
subjects who did not receive incentives (78.9 s).
Results are consistent throughout all 32 cases in
that the eect of incentives on decision time and
on feedback viewing time is the same (p=0.80)
during the ®rst 16 cases as well as during the last
16 cases. H3a is supported.
H3b predicts that cue usage will increase as
decision time increases. The correlation between
cue usage and decision time across all subjects is
0.40 (p=0.0001). Hence H3b is supported. Interestingly, the correlation between feedback viewing
time and cue usage is not signi®cant (p>0.64).
H4 predicts that cue usage, expressed per unit of
time, will be greater for subjects who receive
incentives than for subjects who do not receive
incentives. In this analysis, the number of cues
used by each subject was divided by decision time
to arrive at cue usage per unit of time. This measure served as the dependent variable. Neither
information load (p=0.31), incentive (p=0.67),
nor their interaction (p=0.39) are signi®cant. H4
is not supported.
Table 4
Decision time (s)
Panel A Ð ANOVA
Variable
Information load (6 versus 9 cues)
Incentive (no versus yes)
Load X incentive
Model (d.f.=3,98)
F-value
11.26
9.26
0.01
6.52
p-value
0.0011
0.0030
0.9366
0.0005
Panel B Ð means
Information load
6 cues
9 cues
Average
Incentive
Average
No
Yes
678.3
856.5
783.4
874.4
1043.5
960.3
807.7
965.3
±
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
Although not hypothesized, it is instructive to
examine the eects of information level and
incentives on performance. As a measure of cognitive performance, mean absolute dierence
scores were calculated for each subject. This was
done by subtracting the subject's response from
the criterion response on each case and then averaging the absolute value of these dierences across
the 32 cases. The criterion was calculated as the
response that results from accurately applying the
weights given in the instructions to the information in each case. Hence, these scores re¯ect how
well the subjects used their instructions and feedback. Using these scores as the dependent variable
in a 22 ANOVA, the eect for incentives is signi®cant (p=0.0047) but information level and the
interaction term are not signi®cant (p>0.59). On
average, those with incentives were closer to the
criterion by $1.90 per decision than were those
without incentives. This result suggests that
incentives, rather than information level, aected
cognitive performance.
5. Discussion
Some limitations and strengths of the study
should be considered along with its implications.
There are many situations in which decision
makers are provided with an explicit decision
model to use prior to performing a judgment task.
For instance, some organizations communicate to
decision makers their ideas about the relative
importance of various information cues in order
to improve consistency or compliance. In other
instances, decision aids, training, or institutional
characteristics suggest the relative importance of
the information cues. It is possible in these cases,
that the eects of incentives may be strongest. For
this reason, care should be taken when extending
the ®ndings to situations where explicit cue
weights are not provided. Another limitation is
that the study was performed in the lab using student subjects. Ideally, participants should be randomly selected from the population of interest.
Hence, some caution is warranted before drawing
conclusions to other populations and settings. On
the other hand, previous studies ®nd no dierence
683
in levels of information processing between students and professional accountants. In addition,
the present study provides a high degree of internal validity required to examine its theoretical
issues. It is the ®rst information overload study to
precisely measure decision time, which is critical to
achieve the theoretical construct of information
overload.
The ®ndings from the present experiment illuminate those of prior studies. Most notably this is
true in relation to the eect of incentives on cue
usage. In the absence of incentives, patterns of cue
usage closely follow the previous ®ndings. In this
case, the maximum number of information cues
used by the typical individual does not increase
beyond about six, regardless of the number of cues
presented. In the presence of incentives, however,
average cue usage increased well beyond the six
cue barrier to 7.6 cues when 9 cues were available.
In addition, over half of the subjects in the study
used all the available information in their decisions when given an incentive to do so but only
about a fourth did so without incentives.
Theoretically, information load depends on
both the number of information cues as well as the
amount of decision time. From this perspective,
information processing is conceptually about the
rate at which information is processed Ð not
about the total amount of information that can be
processed. By providing a rather modest incentive,
the participants in this study were induced to
increase their decision time, thereby, increasing
the amount of information ¯ow to their decisions.
The ®ndings raise the question whether providing
sucient incentives and sucient time will cause
individuals to process all available information
regardless of its amount. Certainly, situations exist
that lack incentives and where time is limited. But
just as certain, instances arise in which incentives
are very strong and time for deliberation is readily
available. Some of these decisions include where to
invest one's retirement, where to locate the future
manufacturing facility, who in the organization
should be promoted, and which company's stock
to buy or sell. These types of decisions involve
many information cues of which ®nancial
accounting information often is an important
component.
684
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
The ®ndings are important to accountants who
participate in the development of information
systems and provide much of the information used
in business. The possibility that decision makers
can use all the relevant information is in direct
contrast to the idea that an absolute limit exists to
the amount of information humans can process.
The ®ndings suggest that criticisms over too much
information in ®nancial statements are causing
information overload may be overly strong. Ways
of reducing the level of ®nancial information
include summarizing or omitting information, but
these alternatives can come at the cost of reduced
decision quality. For decisions with large economic consequences, the cost of reduced decision
quality may be very undesirable.
Unlike prior studies, decision consistency was
unaected by information level. This probably
occurred because indecision about which decision
model to use was less in the present study. In previous studies, subjects were told to use all the cues
and that all cues were important, but no guidance
was given on how to weight each piece of information. Hence, the negative correlation between decision consistency and information level observed in
prior studies may be the result of indecision about a
greater number of alternative models available when
more information is present. The present study
controlled for this condition by providing all subjects with guidance about the form of the decision
model that they should use. Within this controlled
setting, incentives were found to have a signi®cant
and positive eect on decision consistency. These
results suggest that individuals can be motivated to
be more consistent in the decision making.
The ®ndings suggest that organizations can
improve information usage (and decision performance) by providing incentives that decision
makers see as being directly linked to their decisions. This should motivate them to take sucient
time to completely process all the available information before coming to conclusions. It is interesting to note that improvements in cue usage
(and cognitive performance) were observed at
both the six and nine-cue levels. Hence, it appears
that organizations can improve information usage
in situations where relatively few cues are provided as well as in situations in which common
prescriptions would suggest that information
overload is possible.
The study found no evidence that incentives or
information level aect information usage per unit
of decision time. This ®nding is consistent with the
theoretical construct of information overload Ð a
cognitive limit to the amount of information that
can be processed per unit of time. Because it is a
cognitive limitation, individuals may not be able
to process more information per unit of time simply because more information is available. Neither
can they process more information per unit of
time because they have incentives to do so. The
®ndings suggest that, even when motivated, individuals may not think harder and faster to solve
problems. Rather, every indication is that individuals extend their information processing time to
achieve higher levels of cue usage.
The idea that information processing is not different under incentives (i.e. people don't process
more than ®ve to seven cues at a time) but that
processing is longer (i.e. people may cycle through
processing dierent combinations of ®ve to seven
cues) speaks to the research by Payne and his
associates regarding eort/bene®t trade-os in
decision making. This stream of studies (e.g.
Payne, 1976, 1982; Payne et al., 1988) suggests
that task characteristics and external in¯uences,
such as time pressure, cause people to adopt more
or less eortful decision strategies. Combined with
the results of the present study, they suggest that
incentives might also motivate more eortful
decision strategies in certain tasks. Future research
is needed to explore this possibility.
The ®ndings also imply that when time is constrained, increasing incentives may not help people
process more information. Clearly many important decisions exist in which time is sometimes
arbitrarily limited. In these cases and if better
judgments are wanted, more time rather than
more incentive may be required. On the other
hand, when time is unconstrained but eciency
(i.e. the amount of time spent) is an issue, increasing incentives may actually end up costing more
than it bene®ts. This implies that individuals
should consider time availability along with incentives and that incentives must be implemented with
care in order to have the desired eect.
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
The present study paints a much brighter picture
of human information processing than is depicted
by some of the prior literature. As such, it opens the
way for future studies to more rigorously examine
the concept of information overload in accounting.
Appendix
Instructions for nine-cue, incentive condition
Welcome. We hope you have fun, earn some
money, and learn something today. You will participate in a simulated market where you will indicate
the price you would be willing to trade shares of
stock in dierent hypothetical companies. Just like a
real stock market, you should pay close attention to
all the information you receive because you will
actually be paid a percentage of your pro®ts in
CASH at the conclusion of the experiment.
Before indicating your prices in each case, you
will be given information about the ®rm whose
stock is being considered. This information will be
as follows:
Economic indicators:
Projected growth
in GNP
Interest rates
Employment
Industry indicators:
Industry growth
(Sales)
Industry compared to
Foreign competition
Value suggesting
lower stock price
Value suggesting
higher stock price
Weak
Strong
Unfavorable
(high)
Weak
Favorable
(low)
Strong
Weak
Strong
Weak
Strong
Company speci®c indicators:
Pro®t margin
Below average
Long-term debt
Unfavorable
Position
Asset turnover
Below average
Quality of ®rm's
Below average
management
685
explain it to you. Notice, that above average, strong
and favorable indicators normally suggest higher
stock prices and that below average, weak and
unfavorable indicators suggest lower stock prices.
Studies have shown that, together, Economic
Indicators account for about 28% of the variability in real stock prices. Industry Indicators
account for 10% of the changes in stock prices
and Company Speci®c Indicators, together,
account for about 62%. Our simulated stock
market works exactly the same. Therefore, to earn
the most pro®ts, you should weight the indicators
accordingly and be sure to use all the information
you receive in your decisions about how much to
buy and sell the stocks. The price of the stock for
the hypothetical companies in this study can vary
between $10 and $100.
You will ®rst complete 3 practice cases in which
your decisions will not aect your pro®ts. These
practice cases are to help you get familiar with the
computer program and you should feel free to
concentrate on learning how the computer works.
Every case represents a new and dierent company. Therefore, each company is like starting
over. Once the practice cases are over, you will
complete 32 more cases but at that point your
decisions will aect your pro®ts.
After you respond to each case, the computer
will display information about the best estimate of
the stock price for that company. The best estimate of the stock price will be determined by
applying the indicator weights to the information
you receive about each company over a possible
range of $10±$100 in stock prices. The indicator
weights are repeated below:
Economic Indicators
Industry Indicators
Firm Speci®c Indicators
28%
10%
62%
Above average
Favorable
Above average
Above average
If you have any questions about the meaning of
an indicator, please ask and we will be glad to
At the conclusion of the entire experiment, you
will be paid cash depending on how close your
responses are to the best estimates of the stock
prices across all the companies. Therefore, the
closer you come to the best estimate of the stock
price for each company, the more you will be paid.
686
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
Reduced cue set used in six cue conditions:
Value suggesting
lower stock price
Economic indicators:
Projected growth
Weak
in GNP
Employment
Weak
Industry indicators:
Industry growth
Weak
(sales)
Company speci®c indicators:
Pro®t margin
Below average
Long-term debt
Unfavorable
Position
Quality of ®rm's
Below average
management
Value suggesting
higher stock price
Strong
Strong
Strong
Above average
Favorable
Above average
References
Abdel-khalik, A. R. (1973). The eect of aggregating accounting reports on the quality of the lending decision: an empirical investigation. Journal of Accounting Research,
(Supplement), 104±138.
Anthony, R., & Govindarajan, V. (1998). Management control
systems. Homewood, IL: Irwin.
Ashton, R. H. A. (1981). Descriptive study of information
evaluation. Journal of Accounting Research, 19(1), 42±61.
Awasthi, V., & Pratt, J. (1990). The eects of monetary incentives on eort and decision performance: the role of cognitive
characteristics. The Accounting Review, 65(4), 797±811.
Baiman, S. (1982). Agency research in m
www.elsevier.com/locate/aos
The eects of a modest incentive on information overload in
an investment analysis task
Brad Tuttle a,*, F. Greg Burton b
a
University of South Carolina, The Darla Moore School of Business, The H. William Close Building, Columbia SC 29208, USA
b
University of Nebraska, USA
Abstract
This paper investigates the eects of performance based monetary incentives on cue usage within the information
overload paradigm. Participants suggested appropriate stock prices for hypothetical companies based on either six or
nine non-correlated information cues. The presence of monetary incentives motivated increased response times compared to participants who did not receive incentives. This in turn resulted in higher levels of information usage than has
been observed in previous studies. The results support the view that information processing capacity imposes a limit on
the amount of information processed per unit of time rather than on the amount of information that can be processed
in total. # 1999 Elsevier Science Ltd. All rights reserved.
1. Introduction
As producers and users of information, accountants care about the eects of information overload on decisions. Too much information is
thought to aect decision quality by producing
less accurate and less consistent responses. Some
even suggest that overwhelming an individual with
information can result in the use of less information than otherwise would occur. Accountants
neither desire to overload decision makers who
use their information, nor do they want to be
overwhelmed when making their own decisions.
Two streams have developed with regards to
information overload. One stream takes an organizational approach by recognizing that for some
* Corresponding author. Tel.: +1-803-777-6639; fax: +1803-777-6876.
E-mail address: tuttle@darla.badm.sc.edu (B. Tuttle)
decisions, what matters is the ability of the organization to process information rather than the
ability of any one individual (Schick, Gordon, &
Haka, 1990). The other stream takes an individual
approach by recognizing that many decisions are
made by individuals who must process information under various contextual in¯uences such as
time constraints (Snowball, 1980) and incentives
(Awasthi and Pratt, 1990). The present study
focuses on this latter, individual decision maker
approach.
Studies of information load eects on individual
decision making have generally relied upon a
model proposed by Schroder, Driver, and Streufert (1967) for their theoretical basis. This model
suggests that task performance will initially
improve as more information is received. But, as
the amount of information begins to exceed the
decision maker's capacity to process it, performance eventually declines. The point at which
0361-3682/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved.
PII: S0361-3682(99)00017-3
674
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
many individuals are unable to use additional
information is considered to be at approximately
seven cues but that some high ability individuals
are able to use as many as nine (Chewning &
Harrell, 1990; Miller, 1956). The model proposed
by Schroder et al. (1967) is important to accountants who prepare reports and design information
systems because they often determine how information is presented and, therefore, used by
decision makers.
One view of information processing capacity is
that it limits the amount of information that a
decision maker can process (Casey, 1980; Chewning & Harrell, 1990; Shields, 1983; Stocks & Harrell, 1995; Stocks & Tuttle, 1998). When the
amount of information exceeds this limit, (i.e.
information overload) then performance declines
(cf. Wood, 1986). If this is true, then accountants
must seek ways to reduce high levels of information in order to best support individual decision
making. Alternatively, information load has been
de®ned in terms of information per unit of time
(cf., Schick et al., 1990; Snowball, 1980). This view
implies that more information can be processed if
more time is taken, hence, information overload is
a function of both time as well as the amount of
information. Under this view, it may be possible
to motivate individuals to increase their decision
time so as to avoid cognitive information processing limits, thus allowing them to use all the
available information, even at high levels. If this is
the case, then accountants should consider decision time and motivational factors before predicting the aects of information load on decisions.
These arguments are consistent with Bonner (1994)
who relates performance to a combination of task
complexity and motivation. We know of no study,
however, that has manipulated subject motivation
with a design capable of measuring information
usage while strictly controlling for information
level and decision time. That is the aim of the
current study.
The paper proceeds by ®rst developing the
hypotheses. In this section, hypotheses regarding
information load and incentive eects on decision
time are presented. The following sections describe
the experiment, the analysis and the results. A ®nal
section discusses the implications of the ®ndings.
2. Hypothesis development
2.1. Capacity approach to information overload
Schroder et al. (1967) proposed a conceptual
model of human information processing in which
the level of information processing follows an
inverted U-shaped curve when plotted against
information load. The U-shaped feature of the
model is seen to be the result of information processing limitations on the part of individuals and is
consistent with various information processing
models (Newell & Simon, 1972; Miller, 1956).
Chewning & Harrell (1990) express this idea succinctly: ``The capability of a human decision
maker to integrate information into a decision is
believed to be limited and is thought to follow a
bell-shaped curve.'' As the amount of input information provided to the decision maker is increased,
the amount of information the individual uses in
the decision initially rises. However, beyond the
limits of human information processing capacity,
further increases in the amount of information
provided to a decision maker result in decreased
information usage. These assertions are consistent
with Wood's (1986) theory of task complexity and
Bonner's (1994) model of task complexity in which
increases in the number of inputs to a decision by
de®nition increase task complexity.
Several studies investigate this proposition by
examining decision performance under diering
levels of information (Abdel-khalik, 1973; Casey,
1980; Chervany & Dickson, 1974; Iselin, 1988;
Shields, 1983; Snowball, 1980). Information load,
in these studies, is manipulated in various ways
such as by varying the level of data aggregation,
by including or not including notes to ®nancial
statements, and by diversifying the kinds of information presented. These studies ®nd mixed results
for judgment or predictive accuracy possibly due to
dierences in the value of the inputs to decisions.
Importantly, none of these studies directly measure
information processing (the focus of the current
study), and as such do not directly examine the
Schroder et al. (1967) model.
A limited number of studies have directly
examined the eects of information load on cue
usage (a measure of information processing).
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
These studies, along with the present study, are
summarized in Table 1. Following the policy capturing paradigm, a limited number of cues are
manipulated in a within-subject factorial design to
be high or low. Subject responses are regressed on
the cue values and a count of signi®cant parameters is deemed to re¯ect cue usage. All three
studies used a ®nancial distress prediction task
with various ®nancial ratios as cues. In no study
were incentives provided or time constraints
imposed by the researchers.
Chewning and Harrell (1990) manipulated the
number of input cues to be four, six, and eight and
were very careful to ensure that all input cues were
decision relevant. They also used three dierent
groups of subjects who diered according to
domain-related experience and knowledge: undergraduate accounting students, graduate accounting
students, and practicing auditors. As can be seen in
Table 1, cue usage improved dramatically between
the four and six cue conditions but did not continue
to improve beyond the six cue condition. These ®ndings were unaected by the subjects' level of domain
knowledge and experience. Interestingly, average
decision consistency, as measured by the adjusted-R2
statistic, declined signi®cantly in the eight cue condition. A reduction in decision consistency may be
evidence of information overload when it occurs
together with a failure to increase cue usage.
Stocks and Harrell (1995) ®nd similar results
also using experienced professional subjects. Input
cues were manipulated to be six or nine and subjects completed the exercise individually or in
groups of three. As can be seen from Table 1, the
number of cues used by individuals did not
increase in the nine cue condition from the six cue
condition and decision consistency decreased. It
appears that the individuals suered information
overload consistent with an information processing capacity constraint. In addition, cue usage
increased when decisions were made by groups
rather than by individuals. This is consistent with
the notion that the information processing capacity of individuals is less than that of groups.
Nevertheless, the average number of cues used by
the groups in the nine cue condition was only 6.4,
thereby, suggesting that even groups have limited
information processing capacity.
675
Stocks and Tuttle (1998) use the same task as
Stocks and Harrell (1995) but manipulated whether the input cues were categorized into top versus bottom 1/3 of industry (as in previous studies)
or presented as numbers (percentile of industry randomly selected to be within top or bottom 1/3). Cue
usage and decision consistency in the categorical
data condition are very similar to previous studies
and strengthen the conclusion that information
overload occurs with nine input cues. Subjects who
received numeric input cues, rather than categorized
cues, appeared to suer information overload even
at the six cue level. Presumably, the numeric data
require an additional processing step to interpret
their valence (i.e. is number good or bad?) thus
resulting in information overload much quicker.
These results are consistent with the notion that
information overload results because of limited
information processing capacity.
In summary, individuals appear to have limited
capacity to process accounting related information. On average, only about six pieces of information are consistently incorporated into a single
judgment or prediction. Across the three studies
just reviewed, the average proportion of cues used
by individuals who received six cues ranges from
50% (2.9/6) to 85% (5.1/6) of the total cues available. On the other hand, individuals who received
nine cues used only 30% (2.7/9)±60% (5.4/8) of
the available cues. These studies suggest that
information overload occurs by the eight or nine
cue level Ð that even when more than six information cues are available, only about six are used.
They further suggest that the proportion of cues
that are actually used decline once the number of
available cues increases beyond about six. These
conclusions hold true whether information is presented numerically or in categorized form, whether subjects are students or experienced
professionals in the decision domain, and whether
decisions are made by individuals or by groups.
These conclusions are further supported by evidence that decision consistency declines as the
number of input cues goes from six to nine. We
formalize these conclusions regarding the eects of
limited information processing capacity on cue
usage and decision consistency with the following
hypotheses:
676
Consistency (adjusted R2)
Study
Task
Subjects
Independent variables
Cue usage
Chewning and
Harrell (1990)
Financial distress
prediction
1. Graduate accounting
students
1. Information load: 4, 6,
and 8 cues
Cue load:
4
6
8
Cue load:
4
6
8
2.9
3.2
4.9
5.1
5.5
6.5
0.80
0.77
0.70
2. Domain related
knowledge and experience
Undergraduate
Graduate
Overall
2. Undergraduate
accounting students
3. Auditors
Auditor
Overall
2.7
3.0
4.9
5.0
5.2
5.8
Bank loan ocers
1. 6 vs. 9 information cues
Cue load:
6
9
Cue Load:
6
9
2. Individual vs. group decisions
Individual
Group
4.6
5.3
4.4
6.4
Individual
Group
0.76
0.84
0.64
0.81
1. 6 vs. 9 information cues
Cue load:
6
9
Cue Load
6
9
2. Categorical vs. numeric data
Numeric
Categoric
2.9
4.5
2.7
5.4
Numeric
Categoric
0.53
0.74
0.43
0.60
1. Information load 6 vs. 9 cues
Cue load:
6
9
Cue Load:
6
9
2. Monetary incentives
provided: yes vs. no
No incentive
4.9
6.2
No incentive
0.77
0.71
Incentive
5.6
7.6
Incentive
0.85
0.85
Stocks and
Harrell (1995)
Stocks and
Tuttle (1998)
Present study
Financial distress
prediction
Financial distress
prediction
Estimate stock price
Upper division
accounting students
Undergraduate
accounting students
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
Table 1
Studies of information load eects on cue usage
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
H1. Subjects who receive nine information cues
will use no more information (i.e. a lesser proportion of the available cues) than will subjects who
receive six information cues.
H2. Decision consistency will decline for subjects
who receive nine information cues when compared
to subjects who receive six information cues.
H1 and H2 provide comparability between the
present study and previous studies.
2.2. The eect of incentives on information usage
Intuition and economic theory suggest that performance based ®nancial incentives should lead to
improvements in performance. Studies, however,
are mixed on this point. Recent research suggests
that incentives can only improve performance if
two conditions are met: (1) the incentives stimulate increased eort, and (2) the task is one in
which a positive relationship exists between eort
and performance (Kennedy, 1993, 1995; Libby &
Lipe, 1992; Stone & Ziebart, 1995). In relation to the
®rst criterion, expectancy theory suggests that performance based incentives will motivate eort
because they increase the attractiveness of achieving
the desired outcome (Vroom, 1964). Both agency
theory (Baiman, 1982, 1990; Eisenhardt, 1989) and
the organization control systems literature
(Anthony & Govindarajan, 1998) argue that economic rewards contingent on performance can be
expected to in¯uence eort. Kennedy (1993) argues
that an individual will not exert eort in making a
judgment unless doing so seems worthwhile. She
further conjectures that additional cognitive eort
can perhaps be motivated by ®nancial rewards. In
short, when an incentive is attractive, the ®rst criterion will likely be met producing more eort.
In relation to the second criterion, it is particularly important to consider the relationship
between eort and performance for cognitively
intensive tasks such as making choices, predictions, and judgments. For instance, Libby and
Lipe (1992) found that announcing incentives
before encoding resulted in greater recall of a list
of internal controls than announcing incentives
after encoding but before recall. Presumably,
677
increases in eort during memory search could not
compensate for a lack of eort during the study
period. Often, suboptimal performance is blamed
on cognitive limitations that are inherent in the
individual and are considered invariant to eort
(see Camerer, 1995 and Shanteau, 1989 for
reviews). As early as 1956, Miller (1956) suggested
that individuals' capacity for processing information is limited and provided a battery of examples
to support this proposition. All three cue usage
studies reviewed in the prior section are consistent
with this view. However, none of this work investigates the possibility that information processing
is limited in capacity only when considered within
a speci®c time period. That is, extending the time
period in which information is processed should
also extend the amount of information that can be
processed. In essence, cognitive limitations may
constrain the rate of information processing rather
than its total amount.
The notion that cognitive capacity limits information processing but only when considered
within a ®xed time period is consistent with the
de®nition of information load adopted by early
researchers in the area. For instance, Schroder et
al. (1967, p. 55) de®ne information load to be ``the
number of dimensions of information presented in
a given time span''. This de®nition has carried into
subsequent work. Snowball (1980, p. 323) de®nes
information load as, ``the quantity of information
impinging upon the processing organism per unit
of time''. Likewise, Schick et al. (1990, p. 203)
state, ``the amount of data to be processed per unit
of time is generally conceived as the information
load''. Consistent with this de®nition, information
processing capacity should be viewed as a limit
to the amount of data that can be processed
per unit of time. No prior study, however, has
empirically examined information overload per
unit of information processing time.
It is reasonable to assume that anything that
motivates additional time on an information processing task will result in more information being
processed. Applied to the present research, these
arguments suggest that monetary incentives will
motivate additional eort in terms of time spent
performing the task thus resulting in more information cues being used. If information processing
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B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
capacity limits the ¯ow rather than the level of
information that can be processed, then the number of cues used by subjects who receive outcomebased incentives will be greater than the number of
cues used by subjects who do not receive incentives. This is formally stated in the following
hypotheses:
H3. The number of cues used by subjects who
receive outcome-based incentives will be greater
than the number of cues used by subjects who do
not receive incentives.
3. Method
3.1. Design
The experiment consisted of a 22 factorial
design in which all treatments were manipulated
between subjects. The ®rst independent variable is
whether or not monetary performance based
incentives were provided the subjects. The second
independent variable is the number of information
cues available to the subjects, six versus nine.
3.2. Materials
H3a. Decision time will be greater for subjects
who receive incentives than for subjects who do
not receive incentives.
H3b. Decision time and cue usage will be positively correlated.
Notice that it is unnecessary that information
usage per unit of time increase for H3 to be supported. Some evidence, however, from the decision
choice literature, suggests that when motivated to do
so individuals switch to more eortful information
processing strategies in order to improve performance (Payne, 1976; 1982; Payne, Bettman, &
Johnson, 1988). These studies show dierences in
information usage depending on the strategy adopted. If this is the case, then it may be possible for
highly motivated subjects to adopt judgment strategies that use more information per unit of time by
thinking very hard and very quickly. However,
because more eortful strategies tend also to take
more time, it remains unclear from prior research
whether information processing per unit of time
increases or is constant. In order to investigate this
issue, we test whether incentives will lead to
increased information usage per unit of time presumably as a result of individuals adopting more
cognitively demanding strategies. If so, then the
point at which information overload occurs would
be higher under incentives.
H4. The number of cues used per unit of time by
subjects who receive incentives will be greater than
the number of cues per unit of time used by subjects
who do not receive incentives.
Materials were created that required the subjects
to estimate stock prices for a set of hypothetical
companies. Nine information cues were selected
after consulting the literature from ®nance on fundamental stock analysis (Carter & Van Auken,
1990; Damodaran, 1996; Estep, 1987; Fogler, 1993;
Muller, 1994). The information cues relate to three
commonly used factors: economy-wide indicators,
industry-speci®c indicators, and company-speci®c
indicators. Cues were selected for the six cue condition by randomly deleting one cue from each
factor area of the nine-cue condition. Instructions
for the nine-cue condition (showing all information
cues) are presented in the Appendix along with the
reduced cue set for the six cue condition.
The subjects were informed that studies have
shown (Jacoby, Mazursky, Troutman, & Kuss,
1984) that together, economic indicators account
for about 28% of the variability in real stock prices. Industry indicators account for 10% of the
changes in stock prices and company speci®c
indicators, together, account for about 62%. The
instructions were to weight the factors accordingly
and to be sure to use all the information in the
decisions. The subjects were told, for the purposes
of this study, to vary the price of the stocks
between $10 and $100.
Thirty-two cases were created based on a 1/2
factorial design in the six-cue condition and on a
1/16 factorial design in the nine-cue condition.
Because of this and because all nine cues were
chosen so that they could believably vary independently of the other cues, no two cues are redundant
by design. Cue values took one of two values and
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
679
were worded to facilitate their interpretation as to
their likely aect on stock prices. Three additional
practice cases were developed from the unused
portion of the factorial designs. One practice case
represented a relatively weak stock, one a moderate
instance, and the third represented a relatively
strong stock. Hypothetical companies were used in
order to achieve an orthogonal design. Uncorrelated cues are necessary to unambiguously measure
cue usage (Ashton, 1981).
Feedback for each case, as well as the criterion
for the performance based incentive, was developed by varying the stock price uniformly over the
$10±$100 range based upon the 28, 10 and 62%
instructions. Cues within each factor were equally
weighted. For instance, a case in which all economic factors cues are high, but all other cues are
low should be priced at $35.20 [(($100ÿ$10)
28%)+$10]. For subjects receiving an economic
incentive, cash payments were based on a percentage of the absolute dierence between their
response and the criterion stock price (i.e. the
response that would have resulted had they accurately applied the instructions and feedback)
across the 32 cases, subtracted from $6.00. The
average payment was $4.17 for about 30 min of
subject time.
The 32 experimental cases, along with the 3
practice cases, were coded into a computer program for two purposes. First, using a computer
allowed the subjects to receive feedback about
the correct stock price. We note that feedback
about stock prices is abundant, cheap, and
often immediate in the world outside the lab.
Second, and most importantly, the computer
recorded the decision time (and separately the
time viewing the feedback) associated with each
response.
3.4. Procedure
3.3. Subjects
Checks were obtained regarding the subject's
understanding of their instructions and their
interest in the exercise. After completing their
decisions, the subjects were asked to recall the
weights for the three factors: economic indicators
(28%), industry indicators (10%), and company
speci®c indicators (62%) in multiple choice format. Approximately 95% of subjects provided
correct responses to this check. Six subjects
The subjects were enrolled in upper division
accounting classes of two major U.S. state universities. On average, they had completed 6.6
accounting courses, 2.8 economic courses, and 1.4
®nance courses. The mean age was 24.3 years,
52% of the subjects were female and all but two
were accounting majors.
The subjects were randomly assigned to either the
six or nine cue conditions. Because of the diculty
of paying some subjects but not others, the incentive
manipulation occurred between semesters.
The subjects were taken to the computer lab
during a regularly scheduled class. Participation
was voluntary, however, subjects were provided
with a small number of class points as an inducement to participate. Class points were not tied to
performance. They ®rst read through the printed
instructions before starting the computer program. The computer presented the ®rst practice
cases and asked the subject for their estimate of
the stock price based on the information on the
screen. After indicating their stock price, the
computer prompted them with an ``Okay?'' If the
subject entered anything other than a ``Y'' they
were returned to reenter the stock price. Otherwise, the criterion stock price was displayed as
feedback and remained on the screen until the
subject pressed the space bar. At this point the
computer refreshed the screen with the next case.
The three practice cases were clearly labeled as
such.
After a subject completed all the cases, the
computer displayed a detailed account of their
responses, the correct responses, and the absolute
dierence between the two for each case. At this
point, the experimenter provided the subject with
a post-experiment questionnaire that he or she
completed on paper before being dismissed.
4. Results
4.1. Preliminary analysis
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B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
provided incorrect responses in the no-incentive
condition and one subject in the incentive condition (p=0.007). The frequency of incorrect
responses does not dier between information
level condition. To assure that failure to follow
instructions does not drive the results, the analysis
was reperformed after deleting the subjects who
answered incorrectly to any one of these questions.
The results did not change. The paper presents
results that include all data. Mean agreement with
the statement, ``The stock price task was interesting
and I tried my best to do well'' is 1.5 on a scale with
1=Strongly Agree to 5=Strongly Disagree. As can
be expected, responses to this measure are marginally higher (p=0.07) in the incentive condition than
in the no-incentive condition. No dierences are
observed between information level conditions.
Individual demographic data were analyzed to
determine whether experimental groups are suciently equivalent to allow between group comparisons. No signi®cant dierences between
experimental groups are observed for age, sex,
class standing (graduate, senior, or junior), the
number of ®nance courses taken, or the number of
economic courses taken. Subjects in the no-incentive condition reported taking an average of 2.8
more accounting courses than subjects in the
incentive condition (p=0.003). This is unlikely
to be a concern since having more accounting
knowledge should increase the ability of the
no-incentive group to use the information relative
to the incentive group. Thus, this works against
observing information processing dierences
between groups. SAT scores and cumulative GPA
were also examined for group dierences across
incentive treatments. No signi®cant dierences
(p>0.25) for either SAT or GPA were observed
between groups. We conclude that the experimental groups are suciently equivalent to make
valid comparisons.
4.2. Tests of hypotheses
All hypotheses were tested using 22 ANOVAs
with cue level (6 versus 9) and incentive (no versus
yes) as independent variables. The dependent
variable diered according to the hypothesis being
tested.
Hypothesis 1 predicts that the number of cues
used in the nine-cue condition will be no greater
than the number of cues used in the six cue condition and that the proportion of cues used by the
subjects will be less for the nine-cue condition than
for the six cue condition. For this analysis, cue
usage expressed as the number of cues used and as
the proportion of available cues used by the subject served as dependent variables.1 Cue usage was
obtained by counting the number of signi®cant
parameters (alpha=0.05) when regressing the
subject's responses on the cue values. Panel A of
Table 2 shows the results of the ANOVA and
Panel B shows mean proportion of cues used. As
can be seen, information load signi®cantly aected
the proportion of cues used (p=0.0041) and the
number of cues used (p=0.0001). Overall, subjects
who received 6 cues used 5.4 out of 6 (89.4%) of
the available cues whereas subjects who received 9
cues used only 7 out of 9 (77.6%). Signi®cance
levels do not change when the dependent variable
is modi®ed using the arc sine transformation for
proportions (Neter, Wasserman, & Kutner 1985,
p. 616). On closer examination, the mean cue
usage in the nine-cue condition without incentives
is only 6.2 cues, a ®nding that is very much in the
range of prior studies.
H2 predicts that decision consistency will
decline for subjects receiving nine cues when compared to subjects receiving six cues. In this analysis, adjusted R2 was used as the dependent
variable. Table 3, Panel A shows that information
load did not signi®cantly aect decision consistency (p=0.28) thus H2 is not supported. One
dierence in the present experiment from previous
information load studies is that the subjects were
1
The proportion of cues used by the subjects provides a
critical benchmark against which possible information overload
can be measured. The subjects were told to use all the cues (all
was underlined in their instructions) and that all the information was relevant to the decision. Indeed, the feedback reinforced this fact as every cue ®gured in the feedback.
Furthermore, it is entirely within the realm of possibility for
many decisions to require nine pieces of relevant information,
including the decision made by our subjects. Hence, we believe
it is reasonable to expect the subjects to use, if information
processing capacity permits, 100% of the information in the
nine-cue condition. Anything less than 100% indicates the
possibility of information overload under these conditions.
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
681
Table 2
Cue usage
Panel A Ð ANOVA
Variable
Number of cues
F-value
Information load (6 versus 9 cues)
Incentive (no versus yes)
Load X Incentive
Model (d.f.=3.98)
Proportion of cues
p-value
26.78
10.67
1.44
14.12
0.0001
0.0015
0.2336
0.0001
F-value
p-value
8.62
11.47
0.37
7.50
0.0041
0.0010
0.5433
0.0001
Panel BÐMeans
Information load
6 cues
9 cues
Average
Incentive
Average
No
Yes
4.9
82.3%
6.2
68.6%
5.7
74.2%
5.6
93.0%
7.6
84.0%
6.6
88.4%
5.4
89.4%
7.0
77.6%
Table 3
Decision consistency (adjusted R2)
Panel A Ð ANOVA
Variable
F-value
p-value
Information load (6 versus 9 cues)
Incentive (no versus yes)
Load X incentive
Model (d.f.=3,98)
1.18
15.50
1.05
6.39
0.2797
0.0002
0.3080
0.0005
Panel B Ð means
Information load
Incentive
No
6 cues
9 cues
Average
0.771
0.713
0.737
provided guidance (instructions and feedback) as
to the appropriate decision model to use. It is
likely that without some guidance, decision consistency would be aected by indecision about
which decision model to use. Importantly, there
are more possible decision models from which to
choose when there are nine cues than when there
Average
Yes
0.852
0.850
0.851
0.824
0.792
±
are only six. Hence, previously observed declines
in decision consistency might simply re¯ect the
fact that more decision models are possible with
nine information cues than with six.
H3 predicts that cue usage will increase when
incentives are provided compared to when incentives are not provided. In this analysis, the number
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B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
of cues used by the subject was used as the
dependent variable. As can be seen in Table 2,
incentives signi®cantly aected the number of cues
used (p=0.001). On average, subjects who
received an incentive used 6.6 cues (88.4% of the
available cues). This is compared to subjects not
receiving an incentive who used only 5.7 cues
(74.2% of the available cues).
Further insight into the eect of incentives on
cue usage is obtained by categorizing the subjects
into two groups: those who used all the available
cues (either six or nine) versus those who used less
than all the available cues. Obviously, information
overload can only be claimed when subjects use
less than all the available cues. Of the subjects who
did not receive incentives, only 28.2% used all the
available cues. In comparison, of the subjects who
did receive incentives, 54% used all the available
cues. This dierence is signi®cant (p=0.006). In
the six (nine) cue condition, only 8 of 16 (3 of 23)
subjects used all cues without incentives. However,
with incentives in the six (nine) cue condition, 23
of 31 (11 of 32) subjects used all the cues. Incentives strongly in¯uenced the subjects' to make use
of all available information in a situation that they
otherwise would not.
H3a predicts the process by which cue usage is
aected by incentives, i.e. through increased decision time. Decision time, as recorded by the computer, was used as the dependent variable. As can
be seen from Table 4, incentives signi®cantly
aected decision time (p=0.0011). On average,
subjects with incentives spent more time (960.3 s)
than subjects without incentives (783.4 s). Further
evidence was obtained from the computer record
of time spent viewing feedback. In this case, subjects with incentives spent signi®cantly (p=0.0006)
more time viewing feedback (136.7 s) than did
subjects who did not receive incentives (78.9 s).
Results are consistent throughout all 32 cases in
that the eect of incentives on decision time and
on feedback viewing time is the same (p=0.80)
during the ®rst 16 cases as well as during the last
16 cases. H3a is supported.
H3b predicts that cue usage will increase as
decision time increases. The correlation between
cue usage and decision time across all subjects is
0.40 (p=0.0001). Hence H3b is supported. Interestingly, the correlation between feedback viewing
time and cue usage is not signi®cant (p>0.64).
H4 predicts that cue usage, expressed per unit of
time, will be greater for subjects who receive
incentives than for subjects who do not receive
incentives. In this analysis, the number of cues
used by each subject was divided by decision time
to arrive at cue usage per unit of time. This measure served as the dependent variable. Neither
information load (p=0.31), incentive (p=0.67),
nor their interaction (p=0.39) are signi®cant. H4
is not supported.
Table 4
Decision time (s)
Panel A Ð ANOVA
Variable
Information load (6 versus 9 cues)
Incentive (no versus yes)
Load X incentive
Model (d.f.=3,98)
F-value
11.26
9.26
0.01
6.52
p-value
0.0011
0.0030
0.9366
0.0005
Panel B Ð means
Information load
6 cues
9 cues
Average
Incentive
Average
No
Yes
678.3
856.5
783.4
874.4
1043.5
960.3
807.7
965.3
±
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
Although not hypothesized, it is instructive to
examine the eects of information level and
incentives on performance. As a measure of cognitive performance, mean absolute dierence
scores were calculated for each subject. This was
done by subtracting the subject's response from
the criterion response on each case and then averaging the absolute value of these dierences across
the 32 cases. The criterion was calculated as the
response that results from accurately applying the
weights given in the instructions to the information in each case. Hence, these scores re¯ect how
well the subjects used their instructions and feedback. Using these scores as the dependent variable
in a 22 ANOVA, the eect for incentives is signi®cant (p=0.0047) but information level and the
interaction term are not signi®cant (p>0.59). On
average, those with incentives were closer to the
criterion by $1.90 per decision than were those
without incentives. This result suggests that
incentives, rather than information level, aected
cognitive performance.
5. Discussion
Some limitations and strengths of the study
should be considered along with its implications.
There are many situations in which decision
makers are provided with an explicit decision
model to use prior to performing a judgment task.
For instance, some organizations communicate to
decision makers their ideas about the relative
importance of various information cues in order
to improve consistency or compliance. In other
instances, decision aids, training, or institutional
characteristics suggest the relative importance of
the information cues. It is possible in these cases,
that the eects of incentives may be strongest. For
this reason, care should be taken when extending
the ®ndings to situations where explicit cue
weights are not provided. Another limitation is
that the study was performed in the lab using student subjects. Ideally, participants should be randomly selected from the population of interest.
Hence, some caution is warranted before drawing
conclusions to other populations and settings. On
the other hand, previous studies ®nd no dierence
683
in levels of information processing between students and professional accountants. In addition,
the present study provides a high degree of internal validity required to examine its theoretical
issues. It is the ®rst information overload study to
precisely measure decision time, which is critical to
achieve the theoretical construct of information
overload.
The ®ndings from the present experiment illuminate those of prior studies. Most notably this is
true in relation to the eect of incentives on cue
usage. In the absence of incentives, patterns of cue
usage closely follow the previous ®ndings. In this
case, the maximum number of information cues
used by the typical individual does not increase
beyond about six, regardless of the number of cues
presented. In the presence of incentives, however,
average cue usage increased well beyond the six
cue barrier to 7.6 cues when 9 cues were available.
In addition, over half of the subjects in the study
used all the available information in their decisions when given an incentive to do so but only
about a fourth did so without incentives.
Theoretically, information load depends on
both the number of information cues as well as the
amount of decision time. From this perspective,
information processing is conceptually about the
rate at which information is processed Ð not
about the total amount of information that can be
processed. By providing a rather modest incentive,
the participants in this study were induced to
increase their decision time, thereby, increasing
the amount of information ¯ow to their decisions.
The ®ndings raise the question whether providing
sucient incentives and sucient time will cause
individuals to process all available information
regardless of its amount. Certainly, situations exist
that lack incentives and where time is limited. But
just as certain, instances arise in which incentives
are very strong and time for deliberation is readily
available. Some of these decisions include where to
invest one's retirement, where to locate the future
manufacturing facility, who in the organization
should be promoted, and which company's stock
to buy or sell. These types of decisions involve
many information cues of which ®nancial
accounting information often is an important
component.
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B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
The ®ndings are important to accountants who
participate in the development of information
systems and provide much of the information used
in business. The possibility that decision makers
can use all the relevant information is in direct
contrast to the idea that an absolute limit exists to
the amount of information humans can process.
The ®ndings suggest that criticisms over too much
information in ®nancial statements are causing
information overload may be overly strong. Ways
of reducing the level of ®nancial information
include summarizing or omitting information, but
these alternatives can come at the cost of reduced
decision quality. For decisions with large economic consequences, the cost of reduced decision
quality may be very undesirable.
Unlike prior studies, decision consistency was
unaected by information level. This probably
occurred because indecision about which decision
model to use was less in the present study. In previous studies, subjects were told to use all the cues
and that all cues were important, but no guidance
was given on how to weight each piece of information. Hence, the negative correlation between decision consistency and information level observed in
prior studies may be the result of indecision about a
greater number of alternative models available when
more information is present. The present study
controlled for this condition by providing all subjects with guidance about the form of the decision
model that they should use. Within this controlled
setting, incentives were found to have a signi®cant
and positive eect on decision consistency. These
results suggest that individuals can be motivated to
be more consistent in the decision making.
The ®ndings suggest that organizations can
improve information usage (and decision performance) by providing incentives that decision
makers see as being directly linked to their decisions. This should motivate them to take sucient
time to completely process all the available information before coming to conclusions. It is interesting to note that improvements in cue usage
(and cognitive performance) were observed at
both the six and nine-cue levels. Hence, it appears
that organizations can improve information usage
in situations where relatively few cues are provided as well as in situations in which common
prescriptions would suggest that information
overload is possible.
The study found no evidence that incentives or
information level aect information usage per unit
of decision time. This ®nding is consistent with the
theoretical construct of information overload Ð a
cognitive limit to the amount of information that
can be processed per unit of time. Because it is a
cognitive limitation, individuals may not be able
to process more information per unit of time simply because more information is available. Neither
can they process more information per unit of
time because they have incentives to do so. The
®ndings suggest that, even when motivated, individuals may not think harder and faster to solve
problems. Rather, every indication is that individuals extend their information processing time to
achieve higher levels of cue usage.
The idea that information processing is not different under incentives (i.e. people don't process
more than ®ve to seven cues at a time) but that
processing is longer (i.e. people may cycle through
processing dierent combinations of ®ve to seven
cues) speaks to the research by Payne and his
associates regarding eort/bene®t trade-os in
decision making. This stream of studies (e.g.
Payne, 1976, 1982; Payne et al., 1988) suggests
that task characteristics and external in¯uences,
such as time pressure, cause people to adopt more
or less eortful decision strategies. Combined with
the results of the present study, they suggest that
incentives might also motivate more eortful
decision strategies in certain tasks. Future research
is needed to explore this possibility.
The ®ndings also imply that when time is constrained, increasing incentives may not help people
process more information. Clearly many important decisions exist in which time is sometimes
arbitrarily limited. In these cases and if better
judgments are wanted, more time rather than
more incentive may be required. On the other
hand, when time is unconstrained but eciency
(i.e. the amount of time spent) is an issue, increasing incentives may actually end up costing more
than it bene®ts. This implies that individuals
should consider time availability along with incentives and that incentives must be implemented with
care in order to have the desired eect.
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
The present study paints a much brighter picture
of human information processing than is depicted
by some of the prior literature. As such, it opens the
way for future studies to more rigorously examine
the concept of information overload in accounting.
Appendix
Instructions for nine-cue, incentive condition
Welcome. We hope you have fun, earn some
money, and learn something today. You will participate in a simulated market where you will indicate
the price you would be willing to trade shares of
stock in dierent hypothetical companies. Just like a
real stock market, you should pay close attention to
all the information you receive because you will
actually be paid a percentage of your pro®ts in
CASH at the conclusion of the experiment.
Before indicating your prices in each case, you
will be given information about the ®rm whose
stock is being considered. This information will be
as follows:
Economic indicators:
Projected growth
in GNP
Interest rates
Employment
Industry indicators:
Industry growth
(Sales)
Industry compared to
Foreign competition
Value suggesting
lower stock price
Value suggesting
higher stock price
Weak
Strong
Unfavorable
(high)
Weak
Favorable
(low)
Strong
Weak
Strong
Weak
Strong
Company speci®c indicators:
Pro®t margin
Below average
Long-term debt
Unfavorable
Position
Asset turnover
Below average
Quality of ®rm's
Below average
management
685
explain it to you. Notice, that above average, strong
and favorable indicators normally suggest higher
stock prices and that below average, weak and
unfavorable indicators suggest lower stock prices.
Studies have shown that, together, Economic
Indicators account for about 28% of the variability in real stock prices. Industry Indicators
account for 10% of the changes in stock prices
and Company Speci®c Indicators, together,
account for about 62%. Our simulated stock
market works exactly the same. Therefore, to earn
the most pro®ts, you should weight the indicators
accordingly and be sure to use all the information
you receive in your decisions about how much to
buy and sell the stocks. The price of the stock for
the hypothetical companies in this study can vary
between $10 and $100.
You will ®rst complete 3 practice cases in which
your decisions will not aect your pro®ts. These
practice cases are to help you get familiar with the
computer program and you should feel free to
concentrate on learning how the computer works.
Every case represents a new and dierent company. Therefore, each company is like starting
over. Once the practice cases are over, you will
complete 32 more cases but at that point your
decisions will aect your pro®ts.
After you respond to each case, the computer
will display information about the best estimate of
the stock price for that company. The best estimate of the stock price will be determined by
applying the indicator weights to the information
you receive about each company over a possible
range of $10±$100 in stock prices. The indicator
weights are repeated below:
Economic Indicators
Industry Indicators
Firm Speci®c Indicators
28%
10%
62%
Above average
Favorable
Above average
Above average
If you have any questions about the meaning of
an indicator, please ask and we will be glad to
At the conclusion of the entire experiment, you
will be paid cash depending on how close your
responses are to the best estimates of the stock
prices across all the companies. Therefore, the
closer you come to the best estimate of the stock
price for each company, the more you will be paid.
686
B. Tuttle, F.G. Burton / Accounting, Organizations and Society 24 (1999) 673±687
Reduced cue set used in six cue conditions:
Value suggesting
lower stock price
Economic indicators:
Projected growth
Weak
in GNP
Employment
Weak
Industry indicators:
Industry growth
Weak
(sales)
Company speci®c indicators:
Pro®t margin
Below average
Long-term debt
Unfavorable
Position
Quality of ®rm's
Below average
management
Value suggesting
higher stock price
Strong
Strong
Strong
Above average
Favorable
Above average
References
Abdel-khalik, A. R. (1973). The eect of aggregating accounting reports on the quality of the lending decision: an empirical investigation. Journal of Accounting Research,
(Supplement), 104±138.
Anthony, R., & Govindarajan, V. (1998). Management control
systems. Homewood, IL: Irwin.
Ashton, R. H. A. (1981). Descriptive study of information
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Awasthi, V., & Pratt, J. (1990). The eects of monetary incentives on eort and decision performance: the role of cognitive
characteristics. The Accounting Review, 65(4), 797±811.
Baiman, S. (1982). Agency research in m