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Does Functional Experience Impair the Balance in Balanced Scorecards?

M. Elizabeth HaywoodAssistant Professor

College of Business AdministrationRider University2083 Lawrenceville RoadLawrenceville, NJ 08648-3099(609) 895-5544msullivan@rider.edu

Nathan V. StuartAssistant ProfessorSchool of AccountancyCollege of Business Administration

University of South Florida4202 East Fowler Avenue, BSN 3403Tampa, FL 33620-5500(813) 974-6570nstuart@coba.usf.edu

AbstractIn this paper, we examine the effect that individual characteristics, specifically functionalexperience and level of work experience, have on how managers acquire and use balancedscorecard information. Using Hambrick and Masons information processing theory, we predict

that a managers functional experience will be associated with his or her information-acquisitionbehavior and final choice in an investment decision. We test our hypotheses using a laboratoryexperiment in which participants make a capital investment decision from a menu of availableopportunities. We do not detect an association between individual characteristics andinformation-acquisition behavior but we do detect an association between individualcharacteristics and investment choice. We discuss how our findings help resolve thediscrepancies in previous studies and suggest implications for companies that use the balancedscorecard.

This version: August 9, 2007

We are grateful to Maureen Butler, Uday Murthy, and the workshop participants at theUniversity of South Florida and the 2006 Mid-Atlantic Regional Meeting of the AmericanAccounting Association for helpful comments and suggestions.

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Does Functional Experience Impair the Balance in Balanced Scorecards?

INTRODUCTION

The balanced scorecard is a managerial accounting tool designed to align managerial

decisions with corporate strategy by focusing attention and effort on a balanced set of

performance measures that are both financial and nonfinancial. Balanced scorecard information,

if properly constructed, can enable managers to make decisions that fit best with firm strategy

and it can encourage managers to exert effort to achieve strategic objectives. Most studies to date

on the balanced scorecard address the latter property: how does the balanced scorecard affect

performance evaluation judgments. In this paper, we examine the balanced scorecards effect on

managerial information acquisition and investment choice behavior.

Several experimental studies provide mixed evidence as to whether users (managers) fully

process the available information when making decisions. Lipe and Salterios (2000) seminal

study asks participants to provide performance evaluations for the managers of two divisions of

the same clothing firm. They report results consistent with participants focusing on performance

measures common to the two divisions and ignoring the measures that reflect each divisions

unique strategy when making their evaluations. These results suggest that managers may not use

balanced scorecard information in a truly balanced fashion. Several studies (e.g., Banker et al.

2004; Roberts et al. 2004) modify the Lipe and Salterio (2000) experimental design in an attempt

to mitigate the bias against unique measures, with varying degrees of success.

In this paper, we examine the balanced scorecard in a strategic decision-making context. In

particular, we investigate whether decision makers biases limit their information acquisition

and/or processing in a balanced scorecard setting. Using the information acquisition and

processing theory of Hambrick and Mason (1984), we predict that a managers functional

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experience (i.e., background in a business area such as finance or marketing) affects his or her

information-acquisition behavior and as well as his or her processing of the information into

actual decisions. There is evidence that individuals emphasize information related to their

functional experience (Dearborn and Simon 1958; Ettlie 1990; Hitt and Tyler 1991) as well as

evidence that individuals emphasize information related to otherfunctional areas (Walsh 1988;

Beyer et al. 1997). This study adds to our knowledge of this phenomenon.

We also examine whether length of work experience, another individual characteristic,

interacts with functional experience to affect information acquisition and processing in strategic

decision-making. Hall (1987) suggests that as individuals continue to work in a certain field, they

become more socialized to that particular area. Accounting researchers have examined job tenure

in auditing contexts and have found that the judgments and choices of experienced personnel

differ significantly from those of novices (Choo and Trotman 1991; Bedard and Mock 1992;

Davis 1996; Wright 2001; Earley 2002; Lehmann and Norman 2006). Through work experience,

individuals can either develop information acquisition and processing skills that enable unbiased

decision-making or create inflexible and narrow cognitive routines that preclude unbiased

decision-making. Our study also increases our understanding of this dimension of individual

development.

We conduct an experiment to study decision makers with varied functional background and

length of work experience. MBA students read a case study of a firm that uses the balanced

scorecard, use the computer to acquire information organized in balanced scorecard categories

about options for a capital investment, and make a strategic investment decision based on that

information. We analyze participants information-acquisition behavior and find no differences

associated with functional background or length of work experience. We analyze participants

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investment decisions and find significant differences in investment choice associated with

functional background and length of work experience. Participants with more experience

systematically emphasize specific functional characteristics in their investment choices.

Interestingly, experienced participants do notnecessarily emphasize information related directly

to their functional background.

Our study makes several contributions to the managerial accounting literature on information

acquisition and decision-making. First, our findings suggest that companies should be aware that

the decision processes of more experienced employees may override the balanced nature of the

balanced scorecard. These employees, although they seem to acquire information without bias,

process it in such a way as to bias their actual decisions. Our finding that it is the cognitive

interpretation process that creates the bias, and not the information acquisition process, is unique

to the literature. Second, we find that the nature of this bias differs according to the functional

background of the decision maker. In particular, individuals with financial experience emphasize

the financial characteristics of the decision alternatives while individuals whose functional

background aligns with the customer dimension of the balanced scorecard de-emphasize the

customer-related characteristics of the decision alternatives. This result suggests that companies

may not be able to address the bias we identify with a single approach.

We organize the rest of this paper as follows. In the second section, we develop our theory

and hypotheses regarding functional background, work experience, and information acquisition

and processing. The third section describes our research design and we report our results in the

fourth section. We discuss the results and address the limitations of our study in the fifth section,

and we identify future research opportunities and conclude in the final section.

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MOTIVATION AND HYPOTHESES

The Balanced Scorecard

The balanced scorecard is a management accounting tool that incorporates performance

measures from different strategic functional areas. A recent Bain & Co. survey of global business

(Rigby 2005) suggests that 57% of firms are using the balanced scorecard in one form or

another. Kaplan and Norton (1992, 1996, 2001) initially emphasized, and continue to promote,

the balanced scorecard as a decision support tool, claiming that the balanced scorecard would

enable companies to incorporate nonfinancial and forward-looking information into strategic

planning to make up for deficiencies in traditional financial data. Kaplan and Norton (1996,

2001) have also acknowledged that companies evaluate the effectiveness of current employee

effort by using multiple performance measures.

Kaplan and Norton emphasize the importance of including multiple measures with different

properties in the strategic planning process. An employee who fails to balance each of the

balanced scorecard perspectives could be making poor strategic choices for his or her

organization. Each [balanced scorecard perspective] represents only one component in a

network of management activities and processes that must be performed to generate superior,

sustainable performance (Kaplan and Norton 2001, 26). For instance, Kaplan and Norton

(2001) note that companies who stressed quality in the 1980s and 1990s often won national

quality awards but ended up in financial peril.

We make the embedded assumption that firms include specific measures in the balanced

scorecard because they believe decisions made with the total scorecard information set will

provide additional benefits to the firm that are greater than the costs of providing and processing

the measures. Kogut and Zander (1992) assert, however, that individuals who identify with

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specific functional experiences can thwart coordination and cooperation with employees serving

in other business areas within a firm. In this study, therefore, we explore the following research

question: will the functional background of managers within a balanced scorecard system affect

their information-acquisition behavior and actual investment decisions?

This research is important for several reasons. First, many firms employ the balanced

scorecard (Calabro 2001; Rigby 2005) so there is significant practitioner interest in

understanding how to obtain the maximum benefit from this management tool. Second, prior

research on the effect of functional experience focuses on information acquisition. Dearborn and

Simon (1958) ask participants to underline information that identifies the mostimportant

strategic problem facing a company, while Walsh (1988) asks participants to underline relevant

information that identifies allimportant strategic problems. Beyer et al. (1997) use both

approaches to try to reconcile the results of the two earlier studies. None of these papers,

however, investigates whether the use (or misuse) of that information leads to a biased choice.

Walsh (1988, 887) writes, the managers studied were not asked to make actual decisions.

Boritz (1992) suggests that it is important to connect process and outcome findings to determine

if they lead to confirming or contradictory conclusions. By examining both information-

acquisition behavior and the decision outcome, we can examine whether the effect is due to

limited information search or to an information processing bias.

Third, balanced scorecard studies find that numerous factors (common versus unique

measures in Lipe and Salterio [2000]; organization of information in Lipe and Salterio [2002];

strategic linkages in Banker et al. [2004]) affect the use of balanced scorecard information in

performance evaluation judgments. There is little evidence to date, however, regarding how

managers use balanced scorecard information in strategic decision-making. The information and

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scorecard characteristics that create or mitigate bias in performance evaluation judgments will

not necessarily have the same effects on strategic decision-making.

Finally, prior research designs have limited the generalizability of the results. Studies have

examined the effect of only a single characteristic on individuals behavior, while theory

suggests (as we discuss below) that multiple characteristics may be involved. Other studies have

provided information sets that emphasize particular types of information (Dearborn and Simon

1958; Walsh 1988) while others have used participants with homogeneous functional

backgrounds (Rosman et al. 1994). Our participants have diverse functional backgrounds and we

consider the interactive effects of functional background and length of work experience. In the

next section, we discuss information acquisition and processing and develop our hypotheses.

Decision Makers Information Acquisition and Processing

A well-designed balanced scorecard includes measures that capture the organizations

strategic goals through functional areas such as finance, marketing, operations, information

technology, and human resource management. The management literature suggests that

functional experience can influence managers strategic decision-making. Hambrick and Mason

(1984) propose a theory of information acquisition and cognitive processing which states that the

demographic characteristics and experiences of individuals influence the way they acquire and

process information.1These characteristics include the managers age, functional experience,

length of service, education, socioeconomic roots, and financial position. Hambrick (2005)

updates the theory and indicates several unexplored implications.

Figure 1 depicts an adapted version of the Hambrick (2005) framework. First, managers must

acquire information related to a particular decision from the information environment (from

Stage 1 to Stage 2 in Figure 1). Bounded rationality (Simon 1978) limits the ability of the

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manager to access and process all available information. Hambrick and Mason (1984) and

Hambrick (2005) suggest that the personal characteristics of the manager affect the filtering

process managers use to acquire specific information. Specifically, these characteristics can limit

the managers field of vision, causing him or her to acquire information only from particular

sources or with particular properties.

[Insert Figure 1 here]

Once the manager has completed the information acquisition step, he or she must process the

information through cognitive decision algorithms (from Stage 2 to Stage 3 in Figure 1). Once

again, Hambrick and Mason (1984) and Hambrick (2005) argue that his or her personal

characteristics play a role in the managers processing of the information. These characteristics

can affect the ways in which managers perceive and interpret the information as supportive of

their preferred decisions. Miller and Toulouse (1986) and Gupta and Govindarajan (1984)

provide evidence that managers personal characteristics alter the establishment of strategy and

its implementation.

One characteristic in particular has been of interest to researchers: the managers functional

experience, such as finance and accounting, human resources and general management,

marketing and sales, production and operations, and information systems. Kogut and Zander

(1992) assert, moreover, that individuals who identify with specific functional experiences can

impede coordination and cooperation with employees serving in other business areas within a

firm. Studies on functional experience and strategic decision-making, however, have produced

inconsistent and inconclusive results.

Certain studies demonstrate a positive correlation between the functional experience of

participants and their information acquisition (i.e., individuals acquire more information related

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to their functional area). Dearborn and Simon (1958) show that executives frame problems in

terms of their functional areas. Ettlie (1990) documents that CEOs with manufacturing

backgrounds engage in more aggressive manufacturing technology policies. Hitt and Tyler

(1991) find that the decision-making of the upper-level managers in their survey sample is

associated with their work experience (among other demographic variables). Rosman et al.

(1994) show that functional experience makes a difference in loan decisions but that such

differences depend on the type of information (financial or strategic) being used.

Other research finds no relation or even a negative correlation between functional experience

and information acquisition (i.e., individuals acquire information in areas other than those related

to their functional experience). Walsh (1988) finds only marginal support that such experience

makes a difference in information acquisition and problem formation. He concludes that a

managers belief structure (the lens through which the manager perceives information) is much

broader than his or her discipline. Beyer et al. (1997, 728) document that functional experience

triggers the individual to over-emphasize information in areas otherthan his or her background

area. They state that [o]nly in finance and accounting were [functional] experience and

problems identified related, and then only at a marginal level of significance. Furthermore, the

relationship is not positive, as theory predicts, but negative.

While our focus is on functional experience, the discrepancies in other studies might be due

to the influence of other (omitted) managerial characteristics in combination with functional

experience. Reed and Reed (1989), for example, find no relation between a CEOs background

(finance, accounting, etc.) and the strategy he selects but do find an interaction between a CEOs

experience and the chosen method of diversification (internally or through acquisition). Gunz

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and Jalland (1996, 735) review numerous studies on managerial careers and business strategies

and conclude the following:

Thus, although it would seem intuitively obvious that there must be personal

characteristics of managers that are affected by career backgrounds in such a wayas to have an impact on strategic choice and the effectiveness of strategyimplementation and that there is some evidence for this impact, this is clearly adifficult area to study, which perhaps explains why few clear findings haveemerged. The personal characteristics that mediate have yet to be identified.

We therefore also examine whether length of work experience interacts with functional

experience to affect information acquisition and decision-making. Fredrickson (1985) reveals

that inexperienced managers naivet creates disparities between their information processes and

those of their more experienced counterparts. Chiesi et al. (1979) find experienced participants

acquire more relevant cues and Brucks (1985) finds experienced managers place greater weight

on functional information. Lurigio and Carroll (1985) suggest that experienced participants

employ knowledge in a more sophisticated fashion than those with less experience. Choo and

Trotman (1991) find experienced auditors recall more and different types of information than

inexperienced auditors.

Hypotheses

Our hypotheses address participants information-acquisition behavior (as is done in previous

research) and their actual decisions, a dependent variable that other functional background

studies do not examine. Based on Hambrick and Masons (1984) theory, we predict a significant

association between functional background and both dependent variables in our balanced

scorecard setting. Given the conflicting extant evidence, however, and in the absence of a

compelling theory that favors one direction or the other in our setting, we do not make a

directional prediction. Our hypothesis for functional experience, Hypothesis 1, is therefore:

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H1: Balanced scorecard users who have functional experience in one area will (A)

exhibit different information-acquisition behavior and (B) make different

investment decisions than balanced scorecard users who have functional

experience in other areas.

Hambrick and Mason (1984, 200) also suggest that Other Career Experiences, such as

length of work experience, affect strategic decision-making. They state that career experiences

partially shape the lenses through which they [managers] view current strategic opportunities and

problems. We propose that length of work experience will interact with functional background

to influence information acquisition and strategic decision-making. Hall (1987) finds that, as

individuals continue to work in a certain field, they become more socialized to that particular

discipline, suggesting that more-experienced managers will have developed biases for certain

information types. A viable alternative, however, is the more-experienced managers have

overcome their preferences for information related to their functional background through

practice and feedback. A balanced scorecard, which makes the functional nature of each

information item salient, could either exacerbate or mitigate the managers tendency towards

particular types of information, as well. We therefore do not believe that theory and existing

evidence justify a directional prediction regarding length of work experience. Our hypothesis

relating to the interaction of functional experience and the length of work experience, Hypothesis

2, is therefore:

H2: Balanced scorecard users who have more work experience and who have

functional experience in one area will (A) exhibit different information-

acquisition behavior and (B) make different investment decisions than balanced

scorecard users who have less work experience and/or functional experience in

other areas.

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METHOD

Participants

We conduct an experiment in which participants choose which of eight new store locations to

open. Our participants are 78 first- and second-year MBA students.2Table 1, Panel A, contains

information about our participants and the selection process we use to reduce our sample to the

55 participants with functional experience in only one balanced scorecard area. First, we

eliminate 12 participants who do not have functional experience that aligns with a balanced

scorecard perspective. We then eliminate 11 participants whose functional background aligns

with more than one balanced scorecard perspective. We conduct our analyses on the remaining

55 participants with pure functional experience. Our participants possess pure functional

experience, unlike those of Walsh (1988) and Beyer et al. (1997). We believe that using only

participants with pure backgrounds will enhance our ability to detect background-driven biases

in information acquisition and investment choice behavior.

Panel B of Table 1 contains demographic information about our participants. The average

age of our participants is 28 and the mean work experience is 4.5 years. Twenty-one of the 55

participants (38%) are female. Our participants have an average of 0.73 years of experience with

the balanced scorecard but slightly more experience (1.27 years) working in a system that uses

nonfinancial performance measures. Half of the participants have experience with the balanced

scorecard through the classroom, their place of employment, or both.

[Insert Table 1 here]

Experimental Procedure

Participants analyze an investment decision case in a computer laboratory equipped with

MouseLab, a software program that keeps track of the order in which the user accesses specific

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information items. MouseLab is a computer-based process tracing software program that

presents information in an alternative by attribute matrix. Each piece of information is in a

separate box and hidden by an opaque screen. Participants reveal the information in a box by

moving the mouse-controlled cursor to the desired box (clicking is not necessary). MouseLab

records which boxes the participants open, the order in which participants open boxes, and the

length of time participants spend in each box. A box closes again once the participant moves the

cursor to another part of the screen. Further explanation of the MouseLab software can be found

in Payne et al. (1993).

Researchers studying consumer behavior and marketing have been the primary users of

MouseLab, using it to study brand choice in numerous product categories (Dhar and Nowlis

1999, 2004; Dhar et al. 1999) and price/quality trade-offs in emotion-laden consumer goods

decisions (Luce et al. 1999). Researchers in human resources have used MouseLab to study

hiring decisions using an alternative by attribute matrix consisting of candidates and five

candidate characteristics (Iglesias-Parro et al. 2002). Accounting researchers have employed

similar computer-based process tracing programs (such as Search Monitor (Brucks 1988) in

going concern decisions (Rosman et al. 1994; Seol 2006) and analytical review judgments

(Bhattacharjee et al. 1999). Swain and Haka (2000) used ISLab (Cook 1993) to determine

information load effects in capital budgeting tasks.

Participants first complete a practice task to familiarize themselves with the MouseLab

software and the task instructions.3Participants then receive the experimental case, which

includes background information for a division of a womens clothing chain that specializes in

teen apparel, the performance measures used in the divisions balanced scorecard (including

general background information on the balanced scorecard), and target scores for each balanced

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Each capital investment choice has a rating for each balanced scorecard perspective: low

(value = 6), moderate (values = 7 or 8) or high (value = 9). The attribute values sum to 30 for

each alternative, so there is no dominant choice (dominant alternatives reduce careful and

thorough searches (Swain and Haka 2000)). The value disclosed in Figure 2, for example, is a

moderate score of 8 for the financial perspective for the Store C investment option.6We

randomize the order of the rows and columns in the participants information boards to prevent

order effects.While there is not an explicit cost to the participants for acquiring an information

item (i.e., clicking on a cell in the alternative-attribute matrix), there is an implicit cost in the

time required to decide which item to acquire, click on the item, read it, and process the item.

When they are ready, participants make their investment selection by clicking on the desired

investment option (see the bottom of Figure 2).

Dependent Variables

We measure information acquisition in two ways. We calculate our first measure,IAFA

(Information Acquisition by Functional Area), as the number of cues acquired for each balanced

scorecard perspective (ignoring revisits) divided by the 32 cells available.IAFAcan range from

zero (when a participant acquires no information in a particular balanced scorecard perspective)

to 25% (eight cells in a balanced scorecard functional area divided by 32 total cells). We

calculate our second measure,IAFA-R(Information Acquisition by Functional Area with

Revisits), similarly, but account for revisits to specific cues (Russo [1978] proposes that

reacquisitions indicate the decision makers assessment of the relative importance of cues).

IAFA-Rrepresents the number of times a participant accesses a particular cue (even if acquired

previously) divided by the total number of times the participant accesses all cues.IAFA-Rcan

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backgrounds in human resources, general management, and information technology into the

learning and growth perspective.

[Insert Table 2 here]

In our discussion below and in our analyses, we further aggregate participants with functional

backgrounds in the internal business processes and learning and growth perspectives. There are

only five participants with functional backgrounds in the internal business process category, and

we cannot conduct empirical tests on such a small number of observations. This choice increases

the power of our tests but reduces out ability to draw specific inferences about the influence of

these two functional backgrounds on information acquisition and choice behavior. Arguably,

there is significant overlap among the functional backgrounds associated with these balanced

scorecard perspectives. For instance, information technology (categorized under learning and

growth in Table 2) can also be viewed as part of an efficient operational system (an internal

business processes area). Because of this overlap, we believe our results are still informative

about the effect of functional background on our dependent variables.

Our participants provide the number of years of work experience they possess on the

demographic questionnaire. To test the effects of length of work experience, we follow Lurigio

and Carroll (1985) and partition our sample by the median years of work experience (four). We

consider our participants experience to be low when they have worked less than four years

and high when they have worked four or more years.

RESULTS

We report the results of several debriefing questions in Table 3. Participants responded to

each question by marking a Likert scale that ranged from 5 (Strongly Disagree) to 5 (Strongly

Agree), with the mid-point labeled 0 (Indifference). The t-tests indicate whether the mean

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response to each question is significantly different from zero (indifference). Participants found

the case easy to understand (mean = 2.10,p-value < 0.0001), realistic (mean = 1.74,p-value FH1A:

Functional Experience IAFA 0.8923 0.72 0.6749IAFA-R 0.8376 1.54 0.1716

H2A:

Functional Experience

and Length of Work

Experience IAFA 0.6777 0.96 0.5190IAFA-R 0.6874 1.26 0.2357

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Figure 2

Choice Display for the Capital Investment Decision

Customer Internal Bus Learn/Grow Financial

Store B

Store G

Store D

Store C 8

Store E

Store H

Store A

Store F

Which store would you choose?

Choose one Store B Store G Store D Store C Store E Store H Store A Store F

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Figure 3

Store Choice by Functional Experience and Length of Work Experience

Panel A: Financial Functional Experience

Store Choice for Subjects With Financial Functional Experience

0

10

20

30

40

50

60

70

80

90

100

Low Financial Score Moderate or High Financial Score

Store Choice

PercentofSubjects

WE 4

WE < 4

Panel B: Customer Functional Experience

Store Choice for Subjects With Customer Functional Experience

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Low Customer Score Moderate or High Customer

Score

Store Choice

PercentageofSubjects

WE 4

WE < 4

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Figure 3 (Continued)

Panel C: Internal Business Process/Learning and Growth Functional Experience

Store Choice for Subjects With IBP/LG Functional Experience

0

0.2

0.4

0.6

0.8

1

1.2

Low Score for Either IBP or LG Neither IBP nor LG has LowScore

Store Choice

PercentageofSubjects

WE 4

WE < 4

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Endnotes

1Hambrick and Mason (1984) called their theory Upper Echelons Theory because of their interest in top-

management teams. As Hambrick (2005) points out, however, the theory is really about information

acquisition and processing and thus applies to individuals at all organizational levels.2Dearborn and Simon (1958) use executives, Walsh (1988) uses mid-career managers, and Beyer et al.

(1997) use MBA students. We believe MBA students are appropriate for the following reasons. First,

Walsh (1988, 890) notes that his results may not apply to simple-minded processors or managers with

more modest accomplishments. We try to determine if this is the case by using MBA students who have

limited work experience. Second, Lipe and Salterio (2000) note that they used MBA students to minimize

the effect of participants relying on their own companys (rather than the experimental case firms)

strategy and related balanced scorecard measures when completing the case. Third, Ashton and Kramer

(1980) and Mowen and Mowen (1986) do not find differences between information processing of students

and non-students. All of our participants are full-time students. We find some demographic differences

between our first- and second-year participants but no effects of program year on our dependent variables.

3The practice task involves 16 information items and 4 decision alternatives. The experimental task

involves 32 information items and eight decision alternatives.

4We are grateful to Marlys Lipe and Steve Salterio for granting us permission to adapt their case materials

from Lipe and Salterio (2000). Our case materials are available upon request.

5We determined the appropriate amount of time during pilot testing.

6To make the cognitive burden on our participants manageable, the investment attribute matrix provides an

aggregate effect for each investment on each balanced scorecard perspective. This type of aggregation

occurs in balanced scorecard firms: CorVu, for example, produces and sells balanced scorecard software

that aggregates the separate performance measures for each perspective into overall performance scores.

7

Since MouseLab tracks not only what boxes participants open but how much time participants spend with

each box open, we conducted similar tests using the percentage of total time spent acquiring information

that each participant spent in each balanced scorecard category as the dependent variable. There were no

significant effects of functional experience and its interaction with length of work experience on time spent