Melco Management Accounting Research
Discussion Paper Series
No.MDP2017-002

The Effects of Interaction among Quantity, Characteristics of Accounting
Measures, and Manager's Cognitive Style on Performance Evaluations

January 2017
Koichi Hioki
Assistant Professor in Marketing,
The Graduate School of Business Administration, Kobe University
Eiichiro Suematsu*
Professor in Management Accounting,
The Graduate School of Humanities and Social Sciences, Saitama University
255 Shimo-okubo, Sakura-ku, Saitama 338-8570
e-mail: suematsu@mail.saitama-u.ac.jp
Hiroshi Miya
Professor in Management Accounting,
The Graduate School of Business Administration, Kobe University*Corresponding author

The Melco Foundation
Nagoya, Japan
Discussion Paper Series of the Melco Management Accounting Research, in order to promote
the study of management accounting, has published this unfinished paper onto the Web. Pleaseobtain the permission of the author when citing this paper.

The Effects of Interaction among Quantity, Characteristics of
Accounting Measures, and Manager's Cognitive Styleon Performance Evaluations

Koichi Hioki,
Assistant Professor in Marketing,
The Graduate School of Business Administration, Kobe University

Eiichiro Suematsu (corresponding author),
Professor in Management Accounting,
The Graduate School of Humanities and Social Sciences, Saitama University,e-mail: suematsu@mail.saitama-u.ac.jp

Hiroshi Miya,
Professor in Management Accounting,
The Graduate School of Business Administration, Kobe University

Abstract
This study aims to investigate appropriate numbers and kinds of accounting
measures in Balanced Scorecard for managers to be utilized in their decision
making. The authors apply an experimental method with 54 participants who work
for a utility company in Japan. The results showed that under information overload
in which many measures are handled simultaneously, managers who have high
Need for Cognition can no longer use either financial or non-financial measureseffectively, while they can use these measures when there is no information
overload. Managers with low Need for Cognition do not use measures of customer
perspective and other non-financial perspectives even when information overload
does not occur. This study concluded that we need to pay careful attention todifferences in managers’ Need for Cognition as well as how many and what kindof measures should be provided to managers when designing multi-measures for
managers in a management accounting system, such as the Balanced Scorecard.

1Keywords BSC, Information Overload, Number of Measures, Characteristics of Measures, Need
for Cognition

1. Introduction
How many and what kind of accounting measures should be provided to managers? This isa very interesting question in managerial accounting systems that promotes appropriate decisions
by managers. We investigate how many measures and what kind of measures should be provided
to managers to promote appropriate decision making and performance evaluation in the Balanced
Scorecard (BSC). In addition, we investigate the relationship between managers’ personalcognitive styles and measures that should be provided to them via the BSC.
In this study, first, we clarify how many measures managers can use in the BSC. Prior
studies regarding agency theory and BSC suggest that providing managers with a larger number ofmeasures leads to appropriate decision making and performance evaluation (Holmström, 1979;
Holmström and Milgrom, 1991; Milgrom and Roberts, 1992, pp.228–229; Feltham and Xie,
1994; Kaplan and Norton, 1996, 2001, 2004; Malina and Selto, 2001;). Some BSC research,however, has indicated that while many measures are provided by the BSC, managers use alimited portion of financial and non-financial measures rather than all of them (Lipe and Salterio,
2000; Ittner, Larcker, and Meyer, 2003). These two inconsistent claims might suggest that decisionaccuracy improves as the number of measures increases; however, decision accuracy would be
exacerbated when the number of measures increases beyond a certain number of measures.
Psychological studies have suggested that there is a limit to the quantity of information to process(limitation of cognitive capacity) and we cannot adequately perform information processing undersituations in which the quantity of information exceeds a certain quantity of information (e.g.,
Miller, 1956; Tuttle and Burton, 1999; Swain and Haka, 2000; Eppler and Mengis, 2004;). Thissituation is known as information overload, under which we do not necessarily use all provided
information, as the quantity of information exceeds the limitation of cognitive capacity. Therefore,
we ignore some information in order to release cognitive capacity. From this perspective, we
assume that due to the limitations of cognitive capacity, managers might not be able to use allmeasures provided by the BSC. In this study, we examine how many measures cause information
overload in the BSC.
Second, we clarify what kind of measures should be provided to managers in the BSC. In
this study, we focus on the characteristics of measures. Lipe and Salterio (2000) indicated thatmanagers tend to focus on common measures for departments when they evaluate the departments,
rather than on unique measures for each department. In addition, Ittner et al. (2003) suggested that
financial measures are the most frequently used, followed by measures of customer perspectives of2

BSC. These studies indicated that managers have selection bias on measure usage. Furthermore,
psychological studies have indicated that characteristics of information are a factor that determines
the definitive quantity of information that causes information overload. We examine whatcharacteristics of measures are used if managers cannot use all measures provided to them in the
BSC.
Third, we test the effects of managers’ cognitive styles. Psychological studies have indicatedthat people have their own personal cognitive styles, that is, personal tendencies of information
processing. Some people like to consider certain problems or enjoy themselves, whereas others do
not. We expect that managers who prefer to consider problems use more measures than those who
do not. In this study, we examine how managers’ cognitive styles influence the usage of measuresin the BSC.
In this study, we investigate the relationships among the number of measures, characteristics
of measures, and personal cognition style by adopting an experimental method. We contribute to
the performance evaluation literature by showing that careful attention should be paid to these
relationships. Various factors determine the quantity of information causing information overload,such as characteristics of information, quality of information, task and the process parameters, andpersonal factors. We study not only how the characteristics of measures determine the number ofmeasures that causes information overload, but also how a manager’s cognitive style determines
the number.
The remainder of the paper is organized as follows. Section 2 summarizes prior research todevelop hypotheses. Thereafter, we describe our experiment in Section 3 and results in Section 4.
The final section discusses our conclusions.

2. Theoretical background and Hypothesis development
Many experimental studies regarding the BSC has attempted to mitigate information
overload, under a condition in which managers are given 16 measures (e.g., Libby, Salterio, andWbb, 2004; Banker, Cang, and Pizzini, 2004, 2011; Cardinals and van Veen-Dirks, 2010; Ding
and Beaulieu, 2011; Humphreys and Trotman, 2011). However, prior psychological studies havementioned that people can recognize far less than 16 measures. Therefore, managers might not beable to utilize all 16 measures, even when accounting systems attempt to mitigate information
overload.
The previous psychological research has revealed that the quantity of information thatcauses information overload is determined by various factors. Jackson and Farzaneh (2012)classified the information overload factors into three factor types: intrinsic factors, extraneous
factors, and the interaction of intrinsic and extraneous factors. Intrinsic factors directly cause
information overload, and extraneous factors affect intrinsic factors and as a result, indirectly cause
information overload. Jackson and Farzaneh (2012) mentioned quantity of information,3

information processing capacity, and available time as intrinsic factors, and characteristics ofinformation, quality of information, task and process parameters, and personal factors as
extraneous factors. In addition, the authors mentioned source of information as intrinsic andextraneous factors. These various factors determine the quantity of information causing
information overload.
We pick up characteristics of information and personal cognitive style from the information
overload factors sorted by Jackson and Farzaneh (2012). We examine how the number ofmeasures causing information overload is affected by the characteristics of each measure (fourperspectives in the BSC) and by managers’ cognitive styles.

Number of measures
Prior accounting research has justified that a large number of measures causes information
overload. Swain and Haka (2000) showed that when many information cues (i.e., the amount of
information available) of budgeting alternatives and dimensions are provided, managers do notnecessarily use all of them. Ittner et al. (2003) clarified that managers subjectively neglect, add, andchange the weight of a performance measure evaluation for their subordinates when they have
many performance measures. In addition, Lipe and Salterio (2000) used an experimental method
to show that managers provided with 16 measures did not necessarily use all measures. Thesestudies might suppose that when the number of measures exceeds a limit of managers’ cognitive
capacity, they do not necessarily use all of them owing to information overload.
Prior research in psychology has clarified that approximately seven kinds of information are
the limit of our cognitive capacity for simultaneous processing. (e.g., Miller, 1956). Tuttle andBurton (1999) referred to prior research regarding the information overload effect on information
usage. According to Tuttle and Burton (1999), many studies have clarified that most individuals
cannot use more than seven kinds of information, and even high-ability individuals cannot use all
information when more than nine kinds of information are provided. Tuttle and Burton (1999)
indicated the same results in their experiment that gave six or nine cues to participants to estimate
stock prices. Nine cues were not necessarily used by the participants in their study to estimate stock
prices.
The appropriate number of measures in the BSC was examined by Ding and Beaulieu(2011). They manipulated the information load by changing the number of measures provided for
managers from 2 to 8 and then 16. Ding and Beaulieu (2011) found that mood congruency bias toevaluate the performance of subordinates can be canceled out by financial incentives when theinformation load is low (i.e., the number of measures is less than eight). Their result assumed that
more than seven measures cause information overload, and was consistent with the results ofTuttle and Burton (1999). In this study, we expect that seven measures provided to managers in the
BSC is the number causing information overload. We formalize the following hypothesis.
4H1: More than seven measures cause information overload in the BSC system.

Financial versus non-financial measures
Ittner et al. (2003) mentioned that many BSC studies have assumed that each perspective
has different characteristics; financial perspective is an outcome measure and an externally oriented
measure, customer perspectives is a leading measure and an externally oriented measure, andinternal business processes and learning and growth perspectives are leading measures and internalmeasures. Ittner et al. (2003) suggested that financial perspective is the most frequently usedmeasure, followed by customer perspective. They explained that this measure usage tendency is
because managers consider external measures as measures of desired outcomes. Their results are
consistent with other studies indicating that it is easier to understand a direct relationship between
cause and result than an indirect relationship (e.g., Luft and Shields, 2001; Farrell et al., 2007). Luftand Shields (2001), Ittner et al. (2003), and Farrell et al. (2007) suggested that measures of internalbusiness processes and learning and growth perspectives have a more ambiguous relationship withfinancial perspective than do measures of customer perspective. In other words, the relationshipbetween financial perspective and customer perspective might be more obvious than that between
financial perspective and other non-financial perspectives.
Jackson and Farzaneh (2012) mentioned that ambiguity of information, as one kind of
information characteristic, is an information overload-causing factor. Therefore, when information
overload occurs due to a larger number of measures, we can assume that managers abandon
ambiguous measures, and use only measures of financial perspective and customer perspective inorder to release cognitive resources (Jackson and Farzaneh, 2012).
We formalize the following hypotheses regarding the characteristics of measure (financial ,customer, internal control, and learing and growth measures) on measure usage.
H2a: Managers under the information overload condition try to use both financial andcustomer measures, rather than internal control measures and learning and growth measures, toreduce information load.
H2b: In the case that excess measures are provided, managers give up using financial andcustomer measures as well as other measures (internal processes, and learning and growth).

Personal cognitive style
Many psychological studies have indicated that there are individual differences in thestrategy of information processing. Need for Cognition (NFC) is a famous example of cognitive
style. NFC is the motivation to think about a certain problem or enjoy oneself. People with highNFC try more actively to process complicated information and to understand content than do thosewith low NFC. Thus, we employ NFC as a parameter to measure the strategy of accounting
information processing by managers. We expect that managers with high NFC try actively to use
5all measures provided in the BSC than do those with low NFC.
Drolet, Luce and Simonson (2009) showed that under a situation with a large quantity of
information processing, people with high NFC cannot adequately carry out information processing
and tend to make compromised decisions compared to a situation with a small quantity of
information processing. Drolet et al. (2009) mentioned that this information processing tendency ispresumably because people with high NFC try to use information actively and they expend a great
deal of their cognitive capacity when provided with excessive information, and thus, they
compromise on decision making. In other words, managers with high NFC might give up using
measures when provided with excess amounts of measures. Thus, we formalize the following
hypothesis.
H3: Managers with high NFC use more measures than do those with low NFC in the BSC
system.

3. Experiment
Experimental design
We designed a 3 (the number of provided measures; 4 vs. 7 vs. 10) × 2 (cognitive styles)
factorial between participants. The number of measures that cause information overload is
expected to be less than nine, according to prior information overload studies. Therefore, we set 3
cases that have 4, 7, and 10 measures, and included the characteristics of measures in the threecases in order to find out the amount of measures causing information overload. Participants weredivided into three groups: the first group was provided with only 4 measures belonging to thefinancial perspective; the second group was provided with 7 measures, of which 4 belong to thefinancial perspective and 1 each to the customer, internal business processes, and learning andgrowth perspectives; and the last group was provided with 10 measures, of which 4 belong to thefinancial perspective and 2 each to the other three perspectives. We employed NFC (Cacioppo andPetty, 1982) to measure managers’ cognitive styles. NFC refers to the motivation to think about acertain problem or enjoy oneself.

Experimental materials
Referring to a scenario shown in Banker et al. (2011), we built a case of an Americanclothing sales company. Before building the scenarios, we asked two managers of a Japanesemajor department store if they could accept the measures used as common measures in Lipe andSalterio (2000), Banker et al. (2004), and Humphreys and Trotman (2011). Based on theirsuggestion, we changed two of the financial measures, as well as one of the customer measures.
All other measures were the same as the common measures in Lipe and Salterio (2000), Banker etal. (2004), and Humphreys and Trotman (2011).
In all conditions, the number of measures in which actual performances are better than the6targets is the same number of measures in which the actual performances are worse than the targets(Table 1). In the four-item condition, the actual performances of two financial measures were better
than the targets by 8.5%, and actual performances of the other two financial measures were worse
than the targets by 8.5%. In the seven-item condition, three measures of the other perspectives
were added to the measures in the four-item condition. The actual performance of one of the added
three measures was better than the target by 8.5%, the second was worse than the target by 8.5%,
and the last was the same as the target. In the 10-item condition, three measures of the otherperspectives were added to the 7-item condition. The actual performance of one of the added threemeasures was better than the target by 8.5%, the second was worse than the target by 8.5%, andthe last was the same as the target. Thus, if managers were to use all of the provided measures,
performance evaluations by managers should not differ among the three conditions.

data
A utility company in Japan cooperated with our research; a total of 73 employees of thecompany participated in the experiment. The data of 19 participants were excluded from ouranalyses due to their low scores on recognition task (below and equal to the chance level). For the
analysis, we applied data of 54 participants (10 females and 44 males, mean age =37.30, SD = 8.03,
mean of managerial experience = 2.63 years, SD = 4.40).

Procedures
The experimental sessions were administered in a class. Each participant was given abooklet containing a fictional company scenario. The participants were divided into three groups;
the first was provided with only 4 measures belonging to the financial perspective (named thefour-item condition in Table 1), the second was provided with 7 measures, of which 4 belong to thefinancial perspective and 1 each to the customer perspective, internal business process perspective,
and learning and growth perspective (named the 7-item condition in Table 1), and the third wasprovided with 10 measures, of which 4 belong to the financial perspective and 2 each to the otherperspectives (named the 10-item condition in Table 1).
In the scenario, each participant was assigned as an executive officer of the company. After
participants completed reading the scenario, they were presented with 4/7/10 performancemeasures of a business division they managed (see Table 1). Participants were asked to evaluate
the performances both of the business division they managed and its director, and to score thoseout of 100 (0–100 points) based on the performance measures in 1 minute. Promptly after
completing the questions, participants rated their levels of confidence in their evaluations using
7-point scales (1: not at all; 7: very confident). In addition, participants were asked to rate theirperceptions of importance of each performance measure using 5-point scales (1: not at all; 5: verymuch). Participants were asked to complete to answer these questions in 2 minutes.
7After completing these questions, each participant enclosed the scenario and questionnaire in
an envelope. Promptly, participants were asked to perform a recognition task as a surprise task. In
this task, participants were presented with 10 performance measures and were asked to rate
whether each measure was better or worse than its targeted value on a three-point scale(better/equal/worse). If they thought the measures were not provided in the scenario or did notrecall them, they were asked to answer “no show” or “have no idea” instead. Participants wereasked to complete this task in 2 minutes (see Table 2). The recognition test was conducted because
we considered that the score of the recognition test should be proxy variables indicating how muchthe participants gave due consideration to the measures and tried to use them.
Finally, participants answered NFC questionnaires and demographic questions. At the end of
the experimental session, participants were fully debriefed and thanked for their participation.
Table 1. Targets of three conditions*
Sales margins
Operating profit on sales*
Sales growth*
Inventory turnover*

Current price relative to the price of previous year*
Customer satisfaction rating*

Time to process customer returns

ActualShown incondition

Perspective

46%

49.91%

4, 7 & 10item

financial24%

26.04%

4, 7 & 10item

financial35%

32.03%

4, 7 & 10item

financial

6times

5.49times

4, 7 & 10item

financial

+5%

+5%

10 itemcustomer80%

86.80%

7 & 10 item

customer

10 iteminternal businessprocesses

4 minutes 4.34 minutes

Return to suppliers
Hours of emploee training per employee*
Employee satisfaction

Target*6%

6%

7 & 10 item

internal businessprocesses

80 hours

86.8 hours

10 itemlearning & growth75%

68.63%

7 & 10 item

learning & growthNote: An increase in measures with * represents improvement in performance, and those without *have the opposite meaning

8

Table 2. Presented measures in each condition and correct answers in recognition task
4-itemconditionbetterbetterworse
worse
not shownnot shownnot shownnot shownnot shownnot shown

Sales margins
Operating profit on sales
Sales growth
Inventory turnover
Current price relative to the price of previous
Customer satisfaction rating
Time to process customer returns
Return to suppliers
Hours of emploee training per employee
Employee satisfaction

7-itemconditionbetterbetterworse
worse
not shown
betternot shownequal
not shownworse

10-itemconditionbetterbetter
worse
worse
equal
betterworse
equal
betterworse

Note: Items marked as “not shown” were not presented to participants in the evaluating task

4. Results
NFC score. We created a composite score by averaging the 15 items measuring NFC(Cronbach’s α = .85). An analysis of variance (ANOVA) test showed no differences in the NFC
level in three conditions: (F(2, 51) <1, ns., M4-item = 4.76, SD4-item = 0.86; M7-item = 4.45, SD7-item =0.74; and M10-item = 4.65, SD10-item = 0.87).

Regression model. We translated the conditions to a dummy code named Cond, coded 0 for
the 4-item condition, 1 for the 7-item condition, and −1 for the 10-item condition. To test the
impact of the number of measures (4 vs. 7 vs. 10; recoded 0, 1, and −1, respectively), NFC, andtheir interactions, we made a hierarchical multiple regression, entering the two main effects in thefirst step and the interaction term in the second step. Prior to these analyses, dummy codes and
NFC were centered (i.e., the mean of the variable was subtracted by each score; see Cohen and
Cohen, 1983).

Evaluations of director, business division, and confidence. To test the impact of thenumber of measures, NFC, and these interactions on evaluations of director and business division,
we did four hierarchical multiple regressions. In all regressions, there was no significant effect of
the impact of the number of measures, NFC, and these interactions.

Accuracy rate in recognition task. To investigate how information was used, we
calculated accuracy in recognition tasks. The correct answers are shown in Table 2. First, we
calculated grand accuracy by dividing the number of correct answers by 10. Sequentially, we
calculated the accuracy of the “financial,” “customer,” “internal business processes,” and “leaningand growth” items. Means of accuracy are reported in Table 3. To test the effects of the number of9measures, we did an ANOVA on the grand accuracy rate. As anticipated, the participants in the
4item condition had significantly higher scores than those of the other conditions. However, there
was no significant difference between the 7-item and 10-item conditions.
To test the interaction effects between the number of measures and NFC, we did four
multiple hierarchical regressions by using accuracy rates for each measure of perspectives as
dependent variables and by using the number of measures, NFC, and their interaction as predictors.
The results are shown in Table 4. As expected, in all models, the effect of the number of measures
was a reliable predictor of hit rate1. More importantly, when predicting the accuracy rate ofcustomer perspective measures, there was an interaction effect between the number of measures
and NFC. Simple slopes in which NFC predicted the accuracy rate were obtained for the three
conditions. Given that the experimental condition is an ordinal variable, the values corresponding
to the 4-item (control), 7-item, and 10-item were chosen (0, 1, and −1, respectively) as single
values. The results revealed that during the 7-item condition, as shown in Figure 1, high NFC
participants more effectively used customer relation information than low NFC participants did
when they were given 7 measures (B= 10.79, t (48) = 1.62, p<.10, one-tailed). By contrast, when
they were given 10 or 4 measures, there were no differences for the accuracy rates between highNFC participants and low NFC participants (t4-item (48) =0.86, t10-item (48) =1.13, ns.). This result
could indicate that high NFC participants used customer relations information inefficiently when
they were provided with 10 measures compared to when they were provided with 7 measures.
As shown in Table 3, regarding the total scores of the recognition test, one-factor ANOVA
was performed for the number of measures. According to the result, the total score of the groupprovided with four measures was measurably higher than the scores of the other groups. There wasno significant difference in the total scores between the groups with 7 measures and 10 measures.
The results supported Hypothesis 1.
Then, in order to measure the interaction between the number of measures and NFC foreach perspective, hierarchical regressions were conducted using accuracy rates for each measure ofperspective as dependent variables and by using the number of measures, NFC, and theirinteraction as predictors. As a result, the number of measures was found to be a reliable predictor in
the accuracy rate for the recognition test for all perspectives. In particular, interaction between thenumber of measures and NFC was observed for the customer perspective. It was shown that forparticipants with high NFC, in the case of 7 measures, the accuracy rate of the recognition test formeasures of customer perspective was significantly high compared with the case of 10 measures.
These results support Hypotheses 2a, 2b, 3.

1

Two models (financial and internal business process) were not significant. We used thesemodels simply as a reference.
10

Table 3. Mean accuracy (standard deviation)

Condition
4-item7-item10-item

Grand
94.1%
(8.0)
83.5%
(13.7)
74.7%(12.5)

Financial
87.5%(18.5)
72.1%(23.2)
86.7%(18.6)

Customer
95.5%(14.7)
85.3%(29.4)
66.7%(24.4)

Internalbusiness
processes
100%
(0.0)
88.2%
(21.9)
66.7%(30.9)

Learning
andgrowth
100%
(0.0)
100%
(0.0)
66.7%
(30.9)

Table 4. Summary of hierarchical regression analysis for variables predicting accuracy rates of fourindexes (N = 54)
financialcustomerββvariablestep 1
condition
NFC
step 2condition x NFC
model significancecorrected R2
Finternalbusiness
processes
βlearning
andgrowthβ-.27 +
-.06

.30 *
.03

.33 *
.03

.58 **
.17

.10

.27 *

-.06

-.13

.03
1.63 n.s.

.09
2.83 *.06
2.11 n.s.

.34
10.27 **

Note: +p <.10, *p < .05, **p < .01

11

Figure 1. Interaction effects between number of items and NFC on accuracy rate of customer
relations

5. Discussion and Conclusion
We examined three points in this study: first, providing managers with a lot of financial andnon-financial measures causes information overload, second, managers do not necessarily use all
the measures, and third, the measure usage tendency changes with either the degree of NFC or thecharacteristics of measures. The following three results were obtained.
First, all of the provided measures were not necessarily used on the increase in the numberof measures, since the accuracy rate of the recognition test became worse as the number ofmeasures increased. In other words, even if non-financial measures were added to financial
measures, managers did not use the non-financial measures owing to information overload.
Second, managers with high NFC seemed to use the non-financial measures when 7 measures areprovided. In this study, the accuracy rate for measures of financial perspective is lowest in the7-item condition and the rate for measures of customer perspective in the 7-item condition ishigher than in the 10-item condition. These results suggest that managers tried to use measures offinancial and customer perspectives in the 7item condition. On the contrary, in the case of the12

10-item condition, the accuracy rate for measures of financial perspective was high compared withthat for the 7-item condition. These results suggest that managers ceased to use measures ofperspectives, except for financial perspective, in order to lower their cognition load, and theirattention became focused on measures of financial perspective.
Third, concerning measures of the perspectives of internal business processes, and oflearning and growth, the accuracy rate of the recognition test became worse as the number ofmeasures increased regardless of the degree of NFC. This result indicates that smaller numbers ofmeasures of these two perspectives are used as the number of provided measures increases.
This study indicates that more than seven measures causes information overload. However,in the case of seven measures provided, managers with high NFC try to use both financial andadded non-financial measures (measures in customer perspective only). This result is the same as
the findings of Ittner et al. (2003), who show that the most frequently used perspective is thefinancial perspective, followed by the customer perspective. On the other hand, in the case of ten
measures provided, managers, even those with high NFC, can no longer use either financial or
non-financial measures effectively.
The new findings of this study, which successfully build on the results of Ittner et al. (2003),
are as follows. This study indicates that managers do not necessarily use all of the provided
measures when an excess number of measures is provided, even though the measures belong to
the customer perspective. Therefore, if more measures are provided in the company studied by
Ittner et al. (2003), their findings might be different. Furthermore, this study shows the possibility
that managers with low NFC do not use measures of customer perspective even when a small
number of measures of customer perspective is used in addition to those of the financial
perspective. In other words, we should pay careful attention to differences in NFC when we
consider which measures managers use.
Our study has some limitations. First, we did not include monetary incentive in our
experiment and thus, the number of measures that leads managers to use either financial or
customer measures is different from that indicated by Ittner et al. (2003). Even though thecompany analyzed by Ittner et al. (2003) used more than 25 measures, managers still usedfinancial and customer measures more than the other measures. Our results show that managers
gave up using customer measures (non-financial measures) if the total number of measuresexceeded 10. We consider the reason for the difference between our result and that of Ittner et al.
(2003) to be monetary incentives. Monetary incentives can decrease information load (Ding and
Beaulieu, 2011; Jackson and Farzaneh, 2012). Therefore, in the case of monetary incentives
prepared, the number of measures that lead managers to focus on only financial and customer
measures is larger than the case without it. If we had included monetary incentives in our
experiment, we might have reached the same number of measures as Ittner et al. (2003).
Consideration of monetary incentives is left as a topic for future research. Second, our participants13are employees of a single company, which might have biased the results. Using managers ofmultiple companies or MBA students is another direction for future study.
Acknowledgement
This work was supported by JSPS KAKENHI Grant Number 23530567 and 26380598.
Helpful comments and suggestions from seminar participants of the European Accounting
Association 37th Annual Congress are gratefully acknowledged.

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