The eects of a modest incentive on information overload in
an investment analysis task
Brad Tuttle
a,*, F. Greg Burton
baUniversity of South Carolina, The Darla Moore School of Business, The H. William Close Building, Columbia SC 29208, USA bUniversity 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 com-pared 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, accoun-tants care about the eects of information over-load 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 informa-tion 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 orga-nizational approach by recognizing that for some
decisions, what matters is the ability of the orga-nization 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 informa-tion 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 Streu-fert (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, perfor-mance eventually declines. The point at which
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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 accoun-tants who prepare reports and design information systems because they often determine how infor-mation 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; Chewn-ing & Harrell, 1990; Shields, 1983; Stocks & Har-rell, 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 informa-tion 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 proces-sing limits, thus allowing them to use all the available information, even at high levels. If this is the case, then accountants should consider deci-sion time and motivational factors before predict-ing 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 pro-cessing 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 suc-cinctly: ``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 infor-mation 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 infor-mation 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.
These studies, along with the present study, are summarized in Table 1. Following the policy cap-turing 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 para-meters 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: under-graduate accounting students, under-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 ®nd-ings 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 condi-tion. 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 sub-jects 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 proces-sing 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 capa-city 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.
Stocks and Tuttle (1998) use the same task as Stocks and Harrell (1995) but manipulated whe-ther the input cues were categorized into top ver-sus bottom 1/3 of industry (as in previous studies) or presented as numbers (percentile of industry ran-domly 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.
Table 1
Studies of information load eects on cue usage
Study Task Subjects Independent variables Cue usage Consistency (adjustedR2)
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
Undergraduate 2.9 4.9 5.5 Overall 0.80 0.77 0.70
2. Undergraduate accounting students
2. Domain related knowledge and experience
Graduate 3.2 5.1 6.5
3. Auditors Auditor 2.7 4.9 5.2
Overall 3.0 5.0 5.8
Stocks and Harrell (1995)
Financial distress prediction
Bank loan ocers 1. 6 vs. 9 information cues Cue load: 6 9 Cue Load: 6 9
2. Individual vs. group decisions Individual 4.6 4.4 Individual 0.76 0.64
Group 5.3 6.4 Group 0.84 0.81
Stocks and Tuttle (1998)
Financial distress prediction
Upper division accounting students
1. 6 vs. 9 information cues Cue load: 6 9 Cue Load 6 9
2. Categorical vs. numeric data Numeric 2.9 2.7 Numeric 0.53 0.43
Categoric 4.5 5.4 Categoric 0.74 0.60
Present study Estimate stock price Undergraduate accounting students
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
B.
Tuttle,
F.G.
Burton
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Accounting,
Organiz
ations
and
Society
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(1999)
H1. Subjects who receive nine information cues will use no more information (i.e. a lesser propor-tion 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 per-formance 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 stimu-late 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 per-formance 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 eco-nomic 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 cri-terion will likely be met producing more eort.
In relation to the second criterion, it is particu-larly important to consider the relationship between eort and performance for cognitively intensive tasks such as making choices, predic-tions, 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,
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 informa-tion 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 inves-tigates 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 therateof information processing rather than itstotal amount.
The notion that cognitive capacity limits infor-mation 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.
capacity limits the ¯ow rather than the level of information that can be processed, then the num-ber 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 incen-tives. 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.
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 posi-tively correlated.
Notice that it is unnecessary that information usage per unit of time increase for H3 to be sup-ported. 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 perfor-mance (Payne, 1976; 1982; Payne, Bettman, & Johnson, 1988). These studies show dierences in information usage depending on the strategy adop-ted. If this is the case, then it may be possible for highly motivated subjects to adopt judgment strate-gies 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 pre-sumably 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.
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
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 fun-damental 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 con-dition 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 pri-ces. 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.
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. Uncorre-lated 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 devel-oped 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 eco-nomic 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 percen-tage of the absolute dierence between their response and the criterion stock price (i.e. the response that would have resulted had they accu-rately 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 pro-gram 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.
3.3. Subjects
The subjects were enrolled in upper division accounting classes of two major U.S. state uni-versities. 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.
3.4. Procedure
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 induce-ment to participate. Class points were not tied to performance. They ®rst read through the printed instructions before starting the computer pro-gram. 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. Other-wise, 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
provided incorrect responses in the no-incentive condition and one subject in the incentive condi-tion (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 margin-ally 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 su-ciently equivalent to allow between group com-parisons. 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-incen-tive 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 experi-mental 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 con-dition 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 sub-ject served as dependent variables.1Cue 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 com-pared to subjects receiving six cues. In this analy-sis, adjusted R2 was used as the dependent variable. Table 3, Panel A shows that information load did not signi®cantly aect decision con-sistency (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
provided guidance (instructions and feedback) as to the appropriate decision model to use. It is likely that without some guidance, decision con-sistency 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
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 incen-tives are not provided. In this analysis, the number
Table 3
Decision consistency (adjustedR2)
Panel A Ð ANOVA
Variable F-value p-value
Information load (6 versus 9 cues) 1.18 0.2797
Incentive (no versus yes) 15.50 0.0002
Load X incentive 1.05 0.3080
Model (d.f.=3,98) 6.39 0.0005
Panel B Ð means
Information load Incentive Average
No Yes
6 cues 0.771 0.852 0.824
9 cues 0.713 0.850 0.792
Average 0.737 0.851 ±
Table 2 Cue usage
Panel A Ð ANOVA
Variable Number of cues Proportion of cues
F-value p-value F-value p-value
Information load (6 versus 9 cues) 26.78 0.0001 8.62 0.0041
Incentive (no versus yes) 10.67 0.0015 11.47 0.0010
Load X Incentive 1.44 0.2336 0.37 0.5433
Model (d.f.=3.98) 14.12 0.0001 7.50 0.0001
Panel BÐMeans
Information load Incentive Average
No Yes
6 cues 4.9 5.6 5.4
82.3% 93.0% 89.4%
9 cues 6.2 7.6 7.0
68.6% 84.0% 77.6%
Average 5.7 6.6
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. Incen-tives 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 deci-sion time. Decideci-sion time, as recorded by the com-puter, 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, sub-jects 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. Inter-estingly, 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 mea-sure 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 F-value p-value
Information load (6 versus 9 cues) 11.26 0.0011
Incentive (no versus yes) 9.26 0.0030
Load X incentive 0.01 0.9366
Model (d.f.=3,98) 6.52 0.0005
Panel B Ð means
Information load Incentive Average
No Yes
6 cues 678.3 874.4 807.7
9 cues 856.5 1043.5 965.3
Although not hypothesized, it is instructive to examine the eects of information level and incentives on performance. As a measure of cog-nitive 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 aver-aging 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 informa-tion in each case. Hence, these scores re¯ect how well the subjects used their instructions and feed-back. Using these scores as the dependent variable in a 22 ANOVA, the eect for incentives is sig-ni®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 stu-dent subjects. Ideally, participants should be ran-domly 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
in levels of information processing between stu-dents and professional accountants. In addition, the present study provides a high degree of inter-nal 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 illu-minate 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 deci-sions when given an incentive to do so but only about a fourth did so without incentives.
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 eco-nomic 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 pre-vious 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 informa-tion. Hence, the negative correlation between deci-sion 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 sub-jects 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 perfor-mance) by providing incentives that decision makers see as being directly linked to their deci-sions. This should motivate them to take sucient time to completely process all the available infor-mation before coming to conclusions. It is inter-esting 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 pro-vided 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 sim-ply 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, indi-viduals may not think harder and faster to solve problems. Rather, every indication is that indivi-duals extend their information processing time to achieve higher levels of cue usage.
The idea that information processing is not dif-ferent 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 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 parti-cipate 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:
If you have any questions about the meaning of an indicator, please ask and we will be glad to
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 varia-bility 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 com-pany. 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 esti-mate 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 28%
Industry Indicators 10%
Firm Speci®c Indicators 62%
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. Value suggesting
lower stock price
Value suggesting higher stock price
Economic indicators: Projected growth in GNP
Weak Strong
Interest rates Unfavorable
(high)
Favorable (low)
Employment Weak Strong
Industry indicators: Industry growth (Sales)
Weak Strong
Industry compared to Foreign competition
Weak Strong
Company speci®c indicators:
Pro®t margin Below average Above average
Long-term debt Position
Unfavorable Favorable
Asset turnover Below average Above average
Quality of ®rm's management
Reduced cue set used in six cue conditions:
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