Indjejikian & Matejka (2012)

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THE ACCOUNTING REVIEW American Accounting AssociationVol. 87, No. 1 DOI: 10.2308/accr-101682012pp. 261290

Accounting Decentralizationand Performance Evaluationof Business Unit Managers

Raffi J. Indjejikian

University of Michigan

Michal MatejkaArizona State University

ABSTRACT: We use survey data to examine firms propensity to rely on financial

measures in evaluating local business unit managers. We find that firms rely less on

financial measures (and more on nonfinancial measures or subjective evaluations) in

determining local managers bonuses when those managers have greater influence over

the design of internal accounting systems. At the same time, we find no significant

association between the choice of performance measures and local managers authority

to make operating decisions. Instead, we find that local authority to make operating

decisions is positively associated with local managers influence over accounting

systems. Taken together, our findings suggest that the design of internal accountingsystems is an important dimension of overall organizational design. Our findings also

cast doubt on the maintained assumption in prior work that major organizational design

choices are complementary.

Keywords: performance measurement; private information; internal accounting sys-

tems; business unit controllers.

Data Availability: Data used in this study cannot be made public due to confidentiality

agreements with participating firms.

I. INTRODUCTION

Questions regarding firms performance-evaluation practices and the incentives these

practices provide have been of interest to academics as well as practitioners for several

decades. Following Holmstrom (1979), the standard agency insight holds that firms place

more weight on performance measures that are more precise and/or more sensitive indicators of

We acknowledge helpful comments of two anonymous reviewers as well as Harry Evans, Steve Kachelmeier, SusanKulp, Jason Schloetzer, and workshop participants at The Pennsylvania State University, University of North Carolina,and University of Oregon.

Editors note: Accepted by John Harry Evans III.Submitted: December 2009

Accepted: July 2011


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managerial performance. In this spirit, there is evidence that growth opportunities, business

strategy, and earnings volatility affect the extent to which firms rely on financial or

accounting-based performance measures to evaluate their managers (e.g., Ittner et al. 1997). There

is also recent evidence that performance-evaluation practices are related to the knowledge and

commensurate decision rights of local business unit (BU) managers (e.g., Baiman et al. 1995;Abernethy et al. 2004; Hwang et al. 2009). Prior literature, however, rarely highlights how

performance-evaluation practices relate to characteristics of internal accounting systems, even

though internal accounting reports often represent the underlying source of performance-evaluation

information.

We contribute to this literature by examining how firms reliance on financial measures relative

to nonfinancial measures in determining BU managers bonuses depends on the delegation of two

distinct categories of decision rights. We refer to the first category of decision rights asoperational

decentralization, by which we mean BU authority to make marketing, production, and related

operating decisions. Much of prior literature focuses on this category of decision rights and argues

that operational decentralization allows BU managers who are knowledgeable about their localbusiness environment to make better decisions than managers at corporate headquarters (e.g.,

Christie et al. 2003). We refer to the second category of decision rights as accounting

decentralizationby which we mean the extent to which local BU managers have authority to design

internal accounting systems or make accounting choices that affect the reported financial results of

their local operations. This category of decision rights has received much less research attention,

although there is some evidence that firms try to alleviate the information asymmetry between BU

managers and corporate headquarters by centralizing the design of local accounting systems (e.g.,

Simon et al. 1954; Siegel and Sorensen 1999).

Given the paucity of prior studies on accounting decentralization, we begin by presenting

field evidence about the nature of accounting decentralization. We find that some firms exercise

centralized control by standardizing their internal reporting systems and enforcing a common

set of accounting practices among their BUs. In contrast, other firms allow BU managers to set

their own accrual policies such as valuing and depreciating divisional assets as well as allow

them to engage in a variety of BU-specific cost allocation and transfer pricing practices. Our

interviews with both corporate and BU managers also consistently suggest that accounting

decentralization increases the availability of locally relevant information and improves BU

decision making.

The idea that BU managers make better decisions when they rely on decentralized accounting

systems aptly coincides with the fundamental rationale for why firms delegate operating decisions

to local managers. Indeed, if decentralized accounting systems imply that BU managers have more

locally relevant information, and more local information implies that firms delegate more operating

decisions to BU managers, then we expect operations to be more decentralized in settings in which

accounting systems are also more decentralized. Thus, our first hypothesis is that accounting

decentralization and operational decentralization are complements in organizational design.

The preceding discussion suggests that the benefits of both operational and accounting

decentralization can be attributed to local private information. Of course, private information also

entails agency costs related to suboptimal decision-making or strategic misrepresentation of

information relevant for performance evaluation. Firms anticipate such agency costs when

designing their incentive compensation and measuring performance of local managers. Hence, our

next two hypotheses address the links between accounting and operational decentralization and

firms performance measurement choices.

We begin with a simple agency model (described in Section III and Appendix A) that

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practices.1 We assume that a local manager has private information about drivers of firm value as

well as private information about the non-value components of reported performance measures.

Whereas the first type of information helps the manager make value-added decisions, the latter

type of information helps the manager embellish his/her performance measure(s). We find that the

emphasis on a performance measure decreases in the precision of a managers information signal

if the signal is relatively more informative about the non-value components of reported

performance than about the drivers of firm value.

In the context of our model, we characterize accounting decentralization as a setting in which

BU managers have access to different types of private information, i.e., information about drivers of

firm value as well as information about the non-value components of reported performance

measures (e.g., Jablonsky and Keating 1998; Indjejikian and Matejka 2006). That said, the

distinguishing feature of accounting decentralization is that managers with authority to make

accountingchoices are uniquely more informed about the non-value components ofaccounting-

basedmeasures. Thus, if BU-specific cost allocation, transfer pricing, or asset-valuation choices

improve BU managers understanding of their accounting-based reported performance more than

anything else, then we expect firms to deemphasize financial measures in favor of nonfinancialmeasures such as market share or customer satisfaction that do not directly depend on such

discretionary accounting choices. Hence, our second hypothesis is that accounting decentralization

and the relative emphasis on financial performance measures in BU managers bonus plans are

substitutes.

Next, we characterize operational decentralization as a proxy for private information about the

drivers of firm value, because the quality of local private information is a key reason why firms

delegate marketing and production decisions (e.g., Jensen and Meckling 1992). If authority to make

operating decisions implies that local managers have private information primarily about the drivers

of firm value, then we expect firms to emphasize performance measures that better reflect such

decision-oriented local information. This is also consistent with Raith (2008), who shows thatprivate information about drivers of firm value is associated with greater emphasis on output-based

measures (as opposed to input-based measures). Assuming that output measures are mostly

financial in nature, our third hypothesis is that operational decentralization and the emphasis on

financial performance measures are complements.

Our three hypotheses represent predictions about pairwise associations among accounting

decentralization, operational decentralization, and the emphasis on financial performance measures.

As Milgrom and Roberts (1995) point out, however, various organizational practices are often

adopted in clusters. Thus, it is important to also consider overall complementarityi.e., whether all

bivariate relations among the organizational design choices are complementary. In our context, we

do not expect overall complementarity to prevail because the second hypothesis predicts that two ofthe choices are substitutes.

Our empirical tests are based on data obtained from a survey of 242 BU managers and

controllers of 121 BUs. Consistent with our first hypothesis, we find that accounting and

operational decentralization are positively associated. We also find support for our second

hypothesis, which predicts that accounting decentralization and the emphasis on financial

performance measures are substitutesi.e., when BU managers have considerable authority to

make internal accounting choices, their bonus plans are less sensitive to financial measures of BU

performance. This finding contributes to prior literature by suggesting that performance-evaluation

practices depend on characteristics of internal accounting systems.

1Although it is well established that local private information can improve decision making as well as exacerbateagency costs (Christensen 1981; Milgrom and Roberts 1992) prior theoretical work rarely examines how private

Accounting Decentralization and Performance Evaluation of Business Unit Managers 263


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In contrast, we find little support for our third hypothesisi.e., we find no discernable

association between operational decentralization and the emphasis on financial performance

measures, which is consistent with similar findings in the literature (e.g., Bouwens and van Lent

2007; Widener et al. 2008). We attribute this finding to a lack of complementarity among the three

organizational design choices. In particular, our results imply that any positive association between

operational decentralization and the emphasis on financial performance measures is offset by the

indirect negative effect due to the positive link between accounting and operational

decentralization.2 Thus, while most prior empirical studies implicitly or explicitly assume that all

organizational design choices are complementary, our findings suggest that complementarity

among a broad set of organizational design choices is unlikely to prevail.

Section II reviews prior literature and briefly summarizes our field evidence. Section III

presents our theoretical framework and develops our hypotheses. Section IV describes our data and

empirical methods. Section V presents the empirical results and Section VI concludes.

II. PRIOR LITERATURE AND FIELD EVIDENCE

How firms make organizational design choices has been studied in a variety of disciplines,

including accounting, finance, management, and economics. Milgrom and Roberts (1990, 1995)

suggest that because firms commonly adopt various organizational practices in clusters,studying

any practice (or a pair of practices) in isolation disregards important interactions among different

organizational practices. The reason is that direct effects of an exogenous force on an organizational

design choice are accompanied by indirect effects that might in principle be as large as the direct

effects and opposite in sign (Milgrom and Roberts 1990, 514). Under assumptions of

complementarity among a variety of organizational design choices, it is possible to show that

the indirect effects reinforce the direct effects and firms optimally adopt practices in clusters. In a

similar vein, Rivkin and Siggelkow (2003, 290) argue that organizational design choices can

interact as complements or as substitutes and that failures to appreciate the systemic nature of

organizational designcommonly lead to suboptimal decisions.

In contrast to the notion of complementarity among various organizational practices, most prior

empirical studies examine only one firm choice, such as the extent of operational decentralization or

the weight on financial performance measures in incentive plans. Thus, these studies implicitly

assume that the complement or substitute relations among key organizational design choices are

secondary in the sense that any hypothesized direct effects of exogenous environmental factors

always dominate potential indirect effects. We briefly review this literature below.

Operational Decentralization

BU managers often have superior information about critical success factors in their local

markets. When such local information is too costly to transfer, firms delegate various marketing,

operating, and investment decisions to local managers (Melumad and Reichelstein 1987; Jensen

and Meckling 1992; Milgrom and Roberts 1992). A number of empirical studies provide support

for the theory that operational decentralization is associated with local private information. For

example, Bouwens and van Lent (2007) and Abernethy et al. (2004) find that operational

2 Milgrom and Roberts (1990) show that lack of complementarity implies that all pairwise associations amongorganizational design choices are attenuated by indirect effects. Given that our hypotheses predict signs but notthe relative magnitude of bivariate relations among the three organizational design choices, we cannot predictexante which of our tests are low-powered and which tests suffer relatively little from the presence of indirecteffects. Nevertheless, our results in support of the second hypothesis suggest that the indirect effect due to thepositive relation between accounting and operational decentralization is not sufficient to offset the negative



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decentralization is positively associated with information asymmetry between BU managers and

their superiors regarding local activities and technical expertise. Other studies find that operational

decentralization is greater in settings characterized by high growth or high emphasis on innovation,

in industries generating relatively more specialized knowledge, or for large or complex firms

(Baiman et al. 1995; Nagar 2002; Christie et al. 2003; Graham et al. 2009).

Recent literature also suggests that operational decentralization and incentive strength are

complementary organizational design choices. In particular, there is evidence that greater

operational decentralization is typically associated with stronger incentives (Nagar 2002; Foss

and Laursen 2005; OConnor et al. 2006; Widener et al. 2008; Ortega 2009).

Choice of Performance Measures

Following Holmstrom (1979), a number of empirical studies have shown that firms place more

weight on performance measures that are more informative (less noisy or more sensitive) indicators

of managerial performance. For instance, Ittner et al. (1997) find that the use of nonfinancial

performance measures increases with noise in financial performance measures and the extent towhich firms follow an innovation- or quality-oriented strategy. Several other studies suggest that

firms emphasis on financial performance measures can be explained by volatility of earnings,

growth, within-firm interdependencies, and past performance (e.g., Bushman et al. 1996; Keating

1998; Moers 2006; Matejka et al. 2009). Evans et al. (2010) examine physician compensation

contracts and find that nonfinancial measures are used more frequently when the measures are more

informative.

There are also a few studies that examine how performance measurement choices (e.g.,

financial versus nonfinancial, input versus output, BU-level versus higher-level measures) depend

on various proxies for local knowledge and private information of managers and employees.

Hwang et al. (2009)find that local specific knowledge, as measured by complexity of production

technology, is associated with a shift away from input-based rewards in favor of output-based

rewards. Abernethy et al. (2004)find some evidence that operational decentralization is associated

with the use of BU summary measures, such as income or ROI, as opposed to disaggregated

financial measures or nonfinancial measures. OConnor et al. (2006) show that operational

decentralization is associated with objective performance measures in the context of Chinas state-

owned enterprises. In contrast, Widener et al. (2008) and Bouwens and van Lent (2007)find no

significant association between operational decentralization and the emphasis on financial

performance measures, while Moers (2006)finds that the sign of the association can be positive

or negative depending on properties of financial performance measures.

Accounting Decentralization

Prior Literature

Early studies on accounting decentralization show that some firms grant BU managers broad

discretion to design their own internal accounting systems according to their local needs, while

other firms centralize the design of internal accounting systems (Simon et al. 1954; Jablonsky and

Keating 1998; Siegel and Sorensen 1999). More recently, Indjejikian and Matejka (2006)find that

BU managers who have considerable authority to design local internal accounting systems enjoy

informational rents in form of greater slack in budgetary targets, and at the same time are more

satisfied with how such accounting systems support their decision making. This evidence suggests

that authority to design local internal accounting systems is associated with local private

information and its attendant costs and benefits.

Building on prior literature, we characterize internal accounting systems as centralized in



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extend well beyond GAAP in the form of detailed formal or informal guidance in areas such as

asset valuation, cost allocation, or transfer pricing. In contrast, we characterize internal accounting

systems asdecentralizedin settings where BU managers have to comply with external GAAP but

otherwise have wide discretion to design their accounting systems according to their local needs.

To better illustrate the costs and benefits of accounting decentralization, we present a briefsummary of relevant field evidence collected as a part of this study (see Appendix B for more

detail). Briefly, the field evidence yields two key observations. First, firms balance the benefits of

centralized and standardized accounting systems that facilitate corporate control against the costs of

burdening local BUs with additional reporting requirements that (1) are of minimal local relevance

and (2) constrain the amount of information available for local decision making. Second, the extent

to which internal accounting systems are decentralized appears to be directly or indirectly related to

operational decentralization.

Field Evidence

Our interviews suggest that accounting centralization enables standardization of internal

reporting and facilitates corporate control. In the words of a BU controller, If you talk about

inventory and [the headquarters] say inventory is too high and here they say: No, it is not too high

. . .If there are different definitions of inventory, then it is going to be difficult.At the same time,

accounting centralization also reduces the usefulness of internal reports for local decision making.

For example, a corporate controller acknowledged the downside of a centralized system as follows:

[O]ur accounting system prescribes FIFO for valuation of inventory. That is troubling, certainly

for [BUs in bulk chemical business], because the raw material prices fluctuate so badly. If you work

with FIFO, you do not get the actual margins. Thus, [BUs in bulk chemical business] prefer to work

with LIFO, but that is not our system.A business group controller in another company made a

similar remark: We have a rather complicated system at [the company] . . .based upon full costing

. . .It is a rather good system if you are a production company, when you have a lot of people. It is

not such a good system if you are a trading company. Direct costing would be much more helpful,

easier to steer your company.

Our interviews also highlight the presence of a trade-off between generating information for

standardization and corporate control and generating information for local decision making. One

BU controller described the trade-off as follows: This is a big discussion within [the company] at

this momentstandardization versus individual needs. As usual, it is a pendulum . . .

Standardization is hard to find in [the company] at this moment. Everybody has their own SAP

systems, implemented in their own way; everybody does it in a different way. The pendulum

swings the other way now. The truth must be somewhere in between. But at the moment the

pressure is on standardization, standardization ... worldwide standardization in accounting so that

we speak a common languageif we benchmark costs per ton or something else per ton across our

plantsso that we speak about the same costs.

Our field evidence further suggests that the extent to which internal accounting systems are

decentralized is positively associated with the extent to which operating decisions are decentralized.

For instance, in circumstances where higher-level management is closely involved in local BU

operations, the demand for standardized reporting appears to be greater. A BU controller with prior

experience in auto manufacturing, where the local BUs were centrally run sales offices in different

countries, commented as follows: I used to work for [a BU] where . . . the marketing and

controlling departments did not have a chance to work for the local [BU]. We were completely

dependent on a large amount of very detailed questions that came from the headquarters and we had



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local BUs were largely independent operating companies): I think that I am more useful for the

operating company than for the holding company. The holding company must receive several key

figures from me that are reliable and that are good. However, most of the information relates to

managing the company.

Finally, our field evidence suggests the presence of an indirect link between accounting andoperational decentralization (see Appendix B). In particular, we find that high-growth prospects and

lack of synergies among BUscommon determinants of operational decentralization (e.g., Nagar

2002; Bouwens and van Lent 2007)are also associated with accounting decentralization. We also

find that individual characteristics and attributes of controllers and managers at the BU and/or

business group level can affect the extent to which accounting systems are decentralized.

III. THEORY AND HYPOTHESES

Considering accounting decentralization and operational decentralization as two distinct

categories of decision rights raises an important question: Should BU managers with more

operational autonomy have more or less authority to make internal accounting choices? That is,

should managers entrusted with marketing and production decisions have more or less control over

the accounting systems and performance reports on which they are evaluated? One response to this

question emphasizes the importance of co-locating information and decision rights (Jensen and

Meckling 1992; Aghion and Tirole 1997). Given that local private information is the rationale for

operational decentralization in the first place (Baiman et al. 1995; Christie et al. 2003), this

perspective implies that BU managers with greater autonomy to make operating decisions should

also have more autonomy to design local accounting systems and generate reports that improve

their operating decisions. An alternative perspective emphasizes the agency costs or control costs

commonly associated with decentralized operations (Christensen 1981; Baiman and Evans 1983;

Baiman and Sivaramakrishnan 1991). If such agency costs are high, then BU managers with greater

autonomy to make operating decisions should have less autonomy to design local accounting

systems to facilitate corporate control and alleviate the information asymmetry between corporate

and BU management.

In light of these conflicting views about the relation between accounting and operational

decentralization, we rely on our field evidence to discern which of the two alternative theoretical

perspectives is more descriptive in our context. As discussed earlier, in circumstances where

higher-level management is closely involved in local BU operations, we find that demand for

centralized and standardized internal accounting systems is greater. This suggests that the first

perspectivei.e., the importance of co-locating information and decision rightsis primary, which

motivates our first hypothesis concerning the relation between accounting and operational

decentralization.

H1: Accounting decentralization and operational decentralization are complements.

Our next two hypotheses link accounting and operational decentralization to firms choice of

performance measures. Because the unobservable theoretical construct that underlies both

accounting and operational decentralization is the presence of local private information, we draw

on agency-theoretic arguments that focus on private information as the major determinant of the

choice of performance measures in incentive contracts. In particular, Appendix A presents a formal

agency model that demonstrates how different types of local private information (e.g., those

underlying accounting and operational decentralization) affect the choice of performance measures

differently.



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production function, but also about the performance report on which s/he is evaluated.3 The notion

that agents have private information and discretion regarding how their performance is reported has

been addressed in accounting research in various ways (e.g., Demski et al. 1984; Verrecchia 1986;

Natarajan 2004; Indjejikian and Matejka 2009). In this respect, our model is most similar to the

predecision private information models of Baker (1992), Bushman et al. (2000), and Baker andJorgensen (2003). Briefly, these models suggest that the optimal contract is a function of the extent

to which an agents private predecision signal informs an agent about his/her upcoming decisions

(i.e., decision-oriented) versus his/her upcoming performance evaluation (i.e., evaluation-oriented).

If private information is more decision-oriented, then firms offer stronger incentives. Conversely, if

private information is more evaluation-oriented, then firms offer weaker incentives.

Our model extends the above intuition to settings with two or more performance measures and

multi-dimensional private information. Specifically, we characterize the incentive weight on a

performance measure (relative to another measure) as a function of the amount (precision) and type

(decision-oriented versus evaluation-oriented) of local private information. We show that the

relative emphasis on a performance measure increases in the precision of a private signal with highdecision orientationi.e., a signal that is relatively more informative about firm value than about

reported performance. Conversely, the relative emphasis on a performance measure decreases when

the manager obtains additional private information that is evaluation-orientedi.e., primarily

informative about how to embellish the performance measure rather than how to increase firm

value.

To empirically operationalize our model, we describe accounting decentralization and

operational decentralization in relation to the two types of local private information (i.e.,

evaluation-oriented and decision-oriented) contemplated by our model (see Motivation of

Hypotheses in Appendix A). In particular, we characterize an increase in accounting

decentralization as providing BU managers with more evaluation-oriented information relative todecision-oriented information. For example, greater discretion to make asset valuation or cost

allocation choices improves BU managers understanding of the drivers of their upcomingreported

financial performance more than such discretion improves their understanding of how to run their

operations or their understanding of other measures used to evaluate their performance.4 Hence, we

predict the following:

H2: Accounting decentralization and the relative emphasis on financial performance measures

(as opposed to nonfinancial measures) in BU managers bonus plans are substitutes.

Next, we characterize an increase in operational decentralization as a setting where BU

managers have more decision-oriented information. This is consistent with much of the priorliterature, which suggests that BU managers who have greater authority to make operating decisions

also have better knowledge of the business environment (e.g., Jensen and Meckling 1992). Our

model shows that firms will optimally increase the emphasis on financial performance measures in

order to motivate BU managers to use such private information. This insight is similar to Raith

(2008), who finds that firms optimally emphasize output-based measures that are sensitive to

managers decision-oriented private information at the expense of input-based measures that are

insensitive to such information.

3 Following Courty and Marschke (2003), we can also extend our model to feature an agent explicitly manipulating

his/her performance report.4

Although BU managers can also anticipate their upcoming performance on nonfinancial dimensions (e.g., theirBUs customer satisfaction scores) how well they understand their nonfinancial performance is unlikely to be



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H3: Operational decentralization and the relative emphasis on financial performance measures

(as opposed to nonfinancial measures) in BU managers bonus plans are complements.

Taken together, our three hypotheses imply an incentive design conflict. Although accounting

decentralization is positively associated with operational decentralization (H1), the former calls for

less emphasis on financial performance measures (H2), while the latter calls for more emphasis(H3). This incentive conflict arises because the three organizational design choices are not expected

to be complementary in the sense implied by Milgrom and Roberts (1990).5 It follows that, in the

absence of complementarity, all pairwise associations implied by our hypotheses will be attenuated

by indirect effects, and empirical tests may lack statistical power to detect significant relations.

IV. METHODS

Data

Our data are drawn from a database consisting of survey responses of managers and controllers

of 178 BUs of seven multinational firms with headquarters in The Netherlands. Indjejikian andMatejka (2006)describe these firms as well as the survey administration procedures in more detail.

After excluding 48 BUs with only one respondent and 9 BUs due to missing data, we obtain a

sample of 121 BUs, ranging from 9 to 24 BUs per firm, where survey responses are available from

both the manager and the controller.

In each of the participating firms, we interviewed five to ten controllers at different

organizational levels (48 interviews in total) and studied internal documents such as accounting

manuals, organizational charts, etc. Importantly, discussions with corporate executives revealed that

BU managers in these firms generally do not receive stock options or other equity-based

compensation, so that their incentive compensation consists mostly of annual bonuses. We rely on

this feature when we construct our proxy for the incentive weight on the financial performancemeasures described below.

Variable Measurement

Choice of Performance Measures

We measure the emphasis on financial performance measures (FIN) using an instrument

similar to those in prior literature (Gupta and Govindarajan 1986; Abernethy et al. 2004; Bouwens

and van Lent 2007). As described in Appendix C, the instrument asks BU managers to state the

percentage of their bonus that depends on (1) BU financial performance, (2) financial performance

of several BUs or the whole firm, (3) nonfinancial performance measures, and (4) subjective

evaluations.FINis the weight on BU financial performance measures divided by the sum of the

weights on BU financial and nonfinancial performance measures plus the weight on subjective

evaluations. In a similar way we calculateNONFIN, as the weight on nonfinancial performance

measures, andSUBJECTIVE, as the percentage of bonus that is determined subjectively (withoutex

ante targets). By definition, FIN NONFIN SUBJECTIVE 100. We use NONFIN andSUBJECTIVEwhen discussing robustness of our results.6

5 For complementarity to prevail, all three pairwise associations have to be positive (or two pairwise associationshave to be negative and one positive, which after rescaling is equivalent to three positive pairwise associations).For a detailed discussion of the necessary conditions for complementarity among multiple organizational design

choices, see Arora and Gambardella (1990).6

We exclude measures relating to performance of several BUs or the whole firmfrom the calculation ofFINas itis not clear to what extent BU managers are knowledgeable about the drivers of performance in other BUs We



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To assess the external validity ofFIN, we correlate it with the extent to which respondents

agree with the following statement, using a seven-point Likert scale where higher scores indicate

disagreement: When evaluating performance of our business unit, higher-level managers rely

heavily on accounting information.As expected,FINcorrelates positively with agreement of both

BU managers (p0.06) and BU controllers (p0.04).

Accounting Decentralization

Indjejikian and Matejka (2006 ) measure the extent to which BU controllers operate

autonomously from corporate headquarters using a formative model with six dimensions, three

of which pertain directly to the extent to which BUs have authority to design local accounting

systems.7 For purposes of this study, we calculate accounting decentralization (ACCDEC) as an

equally weighted average of these three dimensions after standardization.

To test the external validity ofACCDEC, we correlate it with a measure of budgetary slack

employed by Indjejikian and Matejka (2006 ). Our theory suggests that BU-specific accounting

systems increase BU managers information advantage regarding what drives their financialperformance measures. BU managers can exploit this advantage when negotiating budgetary

targets. Thus, we predict and find a positive association betweenACCDECand budgetary slack (r0.22, p 0.03). We also note that this correlation is not affected by the common method biasbecause ACCDEC is solely based on responses of BU controllers, while the budgetary slack

measure is based on responses of BU managers.

Operational Decentralization

Our measure of operational decentralization (OPERDEC) is similar to an instrument

commonly used in the accounting and management literature (Inkson et al. 1970; Ghoshal and

Nohria 1989; Abernethy et al. 2004). Using seven-point Likert scales, BU managers describe fourdimensions of delegation of decision rightsmarketing (four items), financial (five items),

operational (five items), and purchasing (two items) decisions. OPERDEC is an average of

managers reverse-coded responses to these 16 items. We find evidence of inter-rater reliability

based on a confirmatory factor analysis model using nine of the 16 items answered by both the BU

manager and the BU controller (Anderson 1985, 1987).8

7

Aformative modelassumes an underlying construct is formed or induced by indicators that describe its inherentconstitutive facets (Bollen and Lennox 1991; Diamantopoulos and Winklhofer 2001). In contrast, a reflectivemodelassumes an underlying construct is reflected or manifested by a series of indicators. A key difference isthat, in formative models, indicators need not covary, rendering traditional reliability evaluation tools based oninternal consistency (e.g., Cronbachs alpha) meaningless, illogical, and inappropriate(Bisbe et al. 2007, 803).Throughout this paper we use both formative and reflective models for our constructs. ACCDEC is amultidimensional construct with three formative dimensions, each of which is manifested by several reflectiveindicators.

8 As in the case ofACCDEC,OPERDECis a multidimensional construct with formative dimensions. Given thattraditional tests of reliability are not appropriate (see footnote 7), we test for inter-rater reliability as follows. First,we group nine of the 16 items that are answered by both informants into the four dimensions of decentralization(marketing, financial, operational, and purchasing decisions), because it reduces the number of estimatedparameters and deviations from normality (Hoyle 1995, 70). Specifically, each dimension is calculated as anequally weighted average of corresponding items after normalization, assuming that there is an underlyingcontinuous variable having a standard normal distribution (Joreskog and Sorbom 1988). Second, we test a modelwith six latent variables: the four dimensions of decentralization and two factors capturing informant specificvariance We constrain item loadings to be equal for both informants Fit of the model is satisfactory: v2 18 4 df



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Control Variables

We follow prior literature discussed in Section II when identifying relevant control variables.

Most of these control variables below have been validated and used in prior studies. We provide

limited additional information about the reliability and validity of these variables.

Environmental uncertainty (ENVIRON). We use a proxy for noise in BU financialperformance measures akin to a measure of environmental uncertainty employed by Gul and Chia

(1994). In particular, we ask both BU managers and BU controllers to indicate the predictability of

BUs business environment with regard to competitors actions, market demands, production

technology, product attributes/design, purchasing of supplies, and government regulation.

ENVIRONis an equally weighted average of responses on all 12 items.

The inter-rater reliability ofENVIRONis weak. Correlations between BU managers and BU

controllers responses on the same items range between 0.10 and 0.16 but are not significant at

conventional levels (p 0.14, p 0.14, p 0.21, p 0.12, p 0.30, p 0.30 following the order ofitems in Appendix C). Although this suggests that individual items are noisy, averaging all 12 items

of two different respondents likely alleviates the measurement error problem.BU growth (GROWTH). We adopt an instrument similar to the one suggested byGupta and

Govindarajan (1984). We ask respondents about the percentage of total sales for which the strategy

is to increase sales and market share, be willing to accept low returns on investment in the short-to-

medium term, if necessary.To reduce deviations from normality, we calculate the square root of

this percentage.

Interdependencies (INTERDEP).We calculateINTERDEPas the square root of an equally

weighted average of seven items reflecting business sharing with other BUs in the same firm in the

following areas: customers, sales force, advertising, plant facilities, advertising, R&D, internal

transfers, and purchasing (Davis et al. 1992).

Past performance (PASTPRF).We measure BU performance relative to budget in the last

year preceding the survey. Respondents indicate BU performance on a seven-point scale ranging

from far below the budgetto far above the budget.

Size (SIZE).We calculateSIZEas the natural logarithm of the number of employees in a BU

and include it in our regressions to control for other confounding effects.

V. RESULTS

Descriptive Evidence

Table 1 provides descriptive evidence for our sample. We note that annual bonuses are animportant incentive component for our sample BU managers; bonuses comprise 10 to 65 percent of

BU managers total compensation with an average of 31 percent.9 The relative incentive weight on

financial performance measures is 67 percent on average and varies considerably, ranging from 0 to

100 percent for our sample BUs. Most other variables exhibit substantial variation as well. For

example, the median BU has 350 employees, with a minimum of 34 and a maximum of 39,000.

Growth products account for 26 percent of BU sales on average, ranging from 0 to 100 percent. The

scores on our measure of BU interdependencies range from the theoretical minimum of 1 almost to

the theoretical maximum of 7 (based on our scale). Finally, both the mean and median BU

performance is about 4.0, which is labeled on our scale as performance about the same as the

budget.



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The last column of Table 1 measures the importance of firm-level effects in our BU data. The

interclass correlation coefficient, which reflects correlation between two randomly drawn BUs

within the same firm, is small (less than 0.2) or not significantly greater than zero for most of our

variables. The only exception is FINwith a relatively high interclass correlation of 0.45. We

acknowledge that the large firm-level variation in the emphasis on financial performance measures

may limit the power of some of our tests.10

Table 2 shows bivariate correlations and Table 3 presents a multivariate examination of the

determinants of the key organizational design choices. Specifically, in Table 3 we regress

accounting decentralization, operational decentralization, and the emphasis on financial

performance measures on the control variables, as follows:

DESIGNijb0jb1ENVIRONijb2GROWTHijb3INTERDEPijb4PASTPRFijb5SIZEijeij; 1

whereDESIGNstands forACCDEC,OPERDEC, orFIN, respectively; the subscriptidenotes BU-

TABLE 1

Descriptive Statistics

n Mean Std. Dev. Min. Median Max. q

BONUS 113 30.78 11.14 10.00 30.00 65.00 0.14

FIN 121 67.32 28.03 0.00 66.67 100.00 0.45

ACCDEC 121 4.96 1.22 1.00 5.00 7.00 0.19

OPERDEC 121 4.78 0.76 2.44 4.81 6.25 0.15

ENVIRON 121 3.27 0.61 1.00 3.25 5.00

GROWTH 121 25.94 24.25 0.00 20.00 100.00

INTERDEP 121 2.88 1.03 1.00 2.86 6.14

PASTPRF 118 4.19 1.95 1.00 4.00 7.00

SIZE 121 1,059 3,641 34.00 350 39,000 0.19

Reported statistics for the data are before transformations. ForACCDECthis means that descriptive statistics pertain to

the average of the seven underlying items (see Appendix C) rather than to the (less informative) average of standardizedscores actually used in Tables 24. Three missing values forGROWTHare replaced using the mean of BUs in the samebusiness group. There are eight missing values inBONUS(not replaced).qis the interclass correlation coefficient that estimates the proportion of variance accounted for by firm-level effects. It isequal to the correlation between variable scores of two randomly drawn BUs within the same firm ( Snijders and Bosker

1999). q is not reported when the null hypothesis of no firm-level effect cannot be rejected.

Variable Definitions:

BONUSBU managers bonus as a percentage of total compensation;FINrelative incentive weight on financial performance measures;ACCDECaccounting decentralization (BU authority to design local accounting systems);OPERDECoperational decentralization;

ENVIRONperceived environmental uncertainty;GROWTH

BU growth opportunities (sales of growth productsas a percentage of total sales);

INTERDEPinterdependencies;PASTPRFprior years performance relative to budget; andSIZEnumber of employees.

10The large interclass correlation forFINarises in part because one of our firms sets bonus weights on financialperformance measures at 100 percent for all its BUs in our sample Excluding this firm and all its BUs from our



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level observations, and the subscriptj1,. . .,7 denotes firms. To account for clustering of BUswithin firms, we include firm-specific intercepts and allow for firm-specific error terms by means of

the weighted least squares (WLS) estimation technique.11 We also estimate standard errors robust

to data clustering.12 Below, we selectively highlight some of the results from Tables 2 and 3.

Table 2 shows that BU managers in larger BUs receive greater bonuses on average, and these

bonuses tend to be based relatively more on financial performance measures. Both Table 2 and 3

further suggest that BUs operating in uncertain environments tend to have more centralized

operations, consistent with our interview-based evidence suggesting that corporate executives are

more involved with BU operations when the potential for negative surprises is greater. High

environmental uncertainty is also associated with limited BU authority to design accounting

systems. To the extent that uncertain operating environments encourage the recording of

discretionary accruals, such as provisions for future losses, bad debt write-offs, etc., firms may find

it necessary to limit BU discretion over such accounting choices. Finally, Table 3 shows that,

controlling for firm fixed effects, the emphasis on financial performance measures is relatively high

in BUs that performed poorly in the past.

TABLE 2

Pearson Correlations

BONUS FIN ACCDEC OPERDEC ENVIRON GROWTH INTERDEP PASTPRF

FIN 0.32**

ACCDEC 0.04 0.29**OPERDEC 0.15 0.14 0.33**

ENVIRON 0.05 0.13 0.23* 0.26**GROWTH 0.06 0.05 0.13 0.05 0.04

INTERDEP 0.07 0.13 0.12 0.05 0.06 0.15PASTPRF 0.01 0.07 0.02 0.01 0.11 0.22* 0.06SIZE 0.26** 0.21* 0.04 0.00 0.01 0.08 0.10 0.05

*, ** Denotes significance at the 0.05 and 0.01 levels (two-tailed), respectively.

Variable Definitions:BONUSBU managers bonus as a percentage of total compensation;FINrelative incentive weight on financial performance measures;ACCDECaccounting decentralization;OPERDECoperational decentralization;

ENVIRONperceived environmental uncertainty;GROWTHBU growth opportunities (sales of growth productsas a percentage of total sales);

INTERDEPinterdependencies;PASTPRFprior years performance relative to budget; andSIZElog of the number of employees.

11 WLS, also referred to as feasible generalized least squares (Greene 2000), is an iterative procedure that obtainsconsistent estimates of firm-specific error variances and uses them in a subsequent step as weights to calculatecoefficient estimates. Alternatively, we also consider corner solution models that take into account thatFINcannot exceed the value of 100. In particular, we estimate a Tobit model and also a less restrictive Cragg double-

hurdle model and obtain qualitatively similar results (Cragg 1971; Wooldridge 2002).12

Clustered standard error estimates have to be interpreted with caution given the small number of clusters in oursample Our inferences in all our tests remain qualitatively unchanged when we use standard errors without



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Test of Hypotheses

We follow prior literature and test our hypotheses about complementarities in organizational

design by estimating conditional correlations among the three organizational design variables

(Arora and Gambardella 1990; Arora 1996; Athey and Stern 1998). Table 4 implements conditional

correlation tests using the error terms from regressions estimated in Table 3, which holds the effect

of control variables constant. To account for clustering of BUs within firms, the correlations and

standard errors in Table 4 are estimated by means of nonparametric block bootstrapping (Cameron

and Trivedi 2005).

Consistent with H1, Table 4 reports a significant positive association (p 0.04) betweenOPERDECandACCDEC. We stress thatOPERDECis based on BU managers responses, while

ACCDECis based on BU controllers responseshence, this positive correlation is not an artifact

of the common method bias. Rather, it suggests that the decision-making benefits of operational

decentralization are best harnessed when BUs also have the authority to design their accounting

systems.

Consistent with H2, Table 4 reports a significantly negative association (p0.04) betweenaccounting decentralization and the relative incentive weight on financial performance measures.

The theory motivating H2 attributes this finding to the effect of increasing BU managers private

TABLE 3

Weighted Least Squares Model of the Relative Incentive Weight

on Financial Performance Measures

Variables ACCDEC OPERDEC FIN

ENVIRON 0.76*** 0.27*** 1.73*(0.001) (0.008) (0.058)

GROWTH 0.06 0.02 0.51(0.498) (0.352) (0.168)

INTERDEP 0.88** 0.13 0.54(0.016) (0.658) (0.857)

PASTPRF 0.04 0.03 1.28**(0.704) (0.315) (0.012)

SIZE 0.16 0.01 0.18(0.264) (0.949) (0.812)

Adjusted R2 0.14 0.14 0.38

Adjusted R2 (excl. fixed effects) 0.04 0.04 0.03

n 120 120 118

*, **, *** Denotes significance at the 0.10, 0.05, and 0.01 levels, respectively. Two-tailed p-values are reported inparentheses (based on standard errors robust to clustering of data).

Variable Definitions:ACCDECaccounting decentralization;OPERDECoperational decentralization;

FINrelative incentive weight on financial performance measures;ENVIRONperceived environmental uncertainty;

GROWTHBU growth opportunities (sales of growth products

as a percentage of total sales);INTERDEPinterdependencies;PASTPRFprior years performance relative to budget; andSIZElog of the number of employees.



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local accounting systems makes it easier for BU managers to generate favorable BU performance,

as measured by their accounting system. Consequently, greater accounting decentralization reduces

the contracting value of financial performance measures relative to nonfinancial or qualitativeperformance measures that do not directly depend on accounting choices.

Finally, Table 4 suggests that operational decentralization (OPERDEC) is not significantly

related to the relative incentive weight on financial performance measures (p 0.97). This resultprovides little support for suggestions in the prior literature that the aggregate nature of financial

performance measures makes them relatively more useful for contracting in decentralized

environments (Prendergast 2002; Moers 2006; Raith 2008). However, given that operational

decentralization goes hand-in-hand with accounting decentralization, our tests may simply lack

power to detect both the negative effect ofACCDEC on FINas well as the positive effect of

OPERDEConFIN.

We also test H1H3 using the usual regression approach, but with more structure regardinghow firms make organizational design choices. In particular, we assume that operational

decentralization is the most fundamental choice among the decision variables considered in this

study and is determined only by exogenous characteristics of a BUs environment. That is, we

assume that operational decentralization is predetermined at the time a firm considers the extent of

accounting decentralization. Furthermore, we assume that both operational and accounting

decentralization are predetermined at the time a firm considers the importance of various

performance measures. Using these assumptions, Table 5 estimates WLS models of accounting

decentralization and the emphasis on financial performance measures.

Overall, the results in Table 5 closely parallel those in Tables 3 and 4. As before, we find a

significantly positive association between ACCDEC and OPERDEC (p 0.06), as well as anegative association betweenACCDECand FIN( p0.02). We also fail to find support for H3,which predicts a positive association betweenFINandOPERDEC. In addition, the last column of

TABLE 4

Conditional Correlations among Organizational Design Choices

Predicted Sign ACCDEC Predicted Sign OPERDEC FIN

ACCDEC 1.00(0.000)

120

OPERDEC H1: 0.18** 1.00(0.042) (0.000)

120 120

FIN H2: 0.19** H3: 0.00 1.00(0.043) (0.969) (0.000)

118 118 118

** Denotes significance at the 0.05 level.

Tabulated is the Pearson correlation of error terms from regressions in Table 3. Correlations and corresponding two-tailed p-values (in parentheses) are estimated using a nonparametric block bootstrap reflecting clustering of BUs withinfirms.

Variable Definitions:ACCDECaccounting decentralization;OPERDECoperational decentralization; and

FINrelative incentive weight on financial performance measures.



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BU managers describe their business environment as more uncertain (p , 0.01). To the extent that

uncertain environments imply greater volatility of financial performance measures and lower

informativeness relative to nonfinancial performance measures, this result is consistent with

standard agency predictions (e.g., Keating 1998). Moreover, we find that the relative weight on

financial performance measures is high when BU performance relative to prior years budget is poor

(p0.02). This finding is consistent with prior literature suggesting that poor performance makesfinancial performance measures relatively more congruent with the goal of firm survival and

financial profitability (Ittner et al. 1997; Ittner and Larcker 2002; Matejka et al. 2009). Finally, we

find that BU growth, another potential proxy for informativeness of financial performance, is not

related to the relative incentive weight on financial performance measures.

In summary, the evidence in Tables 4 and 5 is inconsistent with overall complementarity

among the three organizational design choices. We find that accounting and operational

decentralization are complements, while accounting decentralization and the emphasis on financial

performance measures are substitutes. These two findings combined imply that the positive

TABLE 5

Weighted Least Squares Model of Accounting Decentralization and the Relative Incentive

Weight on Financial Performance Measures

Variables Predicted Sign ACCDEC Predicted Sign FIN

ACCDEC H2: 1.74**(0.021)

OPERDEC H1: 0.52* H3: 0.17(0.055) (0.911)

ENVIRON 0.59** 3.25***(0.036) (0.000)

GROWTH 0.08 0.29(0.408) (0.582)

INTERDEP 0.75** 0.60(0.038) (0.861)

PASTPRF 0.02 1.12**(0.868) (0.017)

SIZE 0.15 0.04(0.235) (0.968)

Adjusted R2 0.16 0.39

Adjusted R2 (excl. fixed effects) 0.11 0.07

n 120 118

*, **, *** Denotes significance at the 0.10, 0.05, and 0.01 levels, respectively.Two-tailed p-values are reported in parentheses (based on standard errors robust to clustering of data).

Variable Definitions:ACCDECaccounting decentralization;FINrelative incentive weight on financial performance measures;OPERDECoperational decentralization;





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effect throughACCDEC (Milgrom and Roberts 1990). Thus, lack of complementarity at least partly

accounts for the weak bivariate association between operational decentralization and the emphasis

on financial performance measures. In conclusion, although accounting and operational

decentralization are complements, they likely have different implications for the emphasis on

financial performance measures.

Robustness Checks and Additional Evidence

To assess the robustness of our main results, we consider several alternative ways to measure

firms choices of performance measures. In particular, we reestimate the empirical model in (1)

using as the dependent variable: (i) the relative incentive weight on nonfinancial performance

measures (NONFIN), (ii) the percentage of bonus determined subjectively (SUBJECTIVE), and

(iii) a modified version ofFINincorporating higher-level performance measures.13 As discussed

below, our results largely corroborate earlier findings in Table 5.

Consistent with the motivation behind H2, the results in Table 6 suggest that accounting

decentralization is positively associated both with the relative incentive weight on nonfinancialperformance measures (p , 0.01) and with the percentage of bonus determined subjectively (p ,

0.01). Regarding H3, we find that operational decentralization is not associated with the relative

importance of nonfinancial targets (p0.94), but is negatively related to subjectivity (p0.07).One explanation is that subjectivity is more effective than explicit nonfinancial targets when

motivating effort in centralized environments.

In untabulated analyses, we also reestimate (1) after adding the percentage of bonus depending

on higher-level financial performance measures to the denominator ofFIN. This specification

assumes that group- or firm-level performance measures are more like nonfinancial performance

measures, in the sense that BU authority to design local accounting systems provides BU managers

little knowledge about the drivers of higher-level financial measures. Consistent with Table 5, wefind a significant negative association with accounting decentralization (p 0.01) using thisalternative specification.

Next, we present additional evidence regarding the relation between overall incentive strength

(BONUS) and the other three organizational design variables. As before, we first estimate

conditional correlations as in Table 4 (untabulated). We find strong conditional correlation between

incentive strength and operational decentralization (p ,0.01) and somewhat weaker associations

between incentive strength and accounting decentralization (p0.11) and incentive strength andthe emphasis on financial performance measures (p0.11).

Second, in Table 7, we also estimate WLS models of incentive strength as measured by

BONUS, similar to those in Table 5. Column (1) shows that neither accounting nor operationaldecentralization are significant at conventional levels when included as regressors jointly. In

contrast, Columns (2) and (3) show that operational decentralization (p 0.05), as well asaccounting decentralization (p0.05), are significant when regressed separately. We further findthatBONUS is associated with size and interdependencies. While prior research suggests that

13 For a large number of our sample observations, NONFIN and SUBJECTIVE equal zero, which calls forestimation models, allowing for corner solution outcomes. The results in Table 6 are estimated using the Craggdouble-hurdle model, which simultaneously estimates the probability of non-zero incentive weights and theexpected incentive weights conditional on including nonfinancial performance measures (allowing for subjectiveevaluations) in bonus plans (Cragg 1971; Smith and Brame 2003). Given that our theoretical framework pertainsto the magnitude of incentive weights rather than to institutional forces allowing or precluding the use ofnonfinancial measures (subjectivity) in bonus plans, Table 6 presents only parameters describing the(conditional) expected incentive weights The results should be interpreted with caution given the difficulty of



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interdependencies affect the choice of performance measures rather than incentive strength (e.g.,

Bouwens and van Lent 2007), it is plausible that interdependencies proxy for the high marginalproduct of BU managers effort in our sample and thus are associated with greater incentive

strength.

VI. SUMMARY AND CONCLUSIONS

We study the use of financial and nonfinancial measures in business unit (BU) managers

bonus plans and how the design of internal accounting systems affects the choice of performance

measures. We find that managers in some BUs have considerable discretion over internal

accounting choices, while internal accounting choices in other BUs are largely centralized. We

examine how this variation in accounting decentralization relates to operational decentralization and

the emphasis on financial performance measures in BU managers bonus plans.

Our findings contribute to a stream of prior research examining how private information affects

TABLE 6

Models of the Relative Incentive Weight on Nonfinancial Performance Measures and the

Percentage of Annual Bonus Determined Subjectively

Variables NONFIN SUBJECTIVE

ACCDEC 4.01*** 3.10***

(0.008) (0.000)

OPERDEC 0.32 6.52*(0.937) (0.073)

ENVIRON 2.07 4.80**(0.616) (0.012)

GROWTH 0.91 1.01

(0.558) (0.598)

INTERDEP 2.38 13.58(0.365) (0.622)

PASTPRF 0.74 0.78(0.647) (0.746)

SIZE 3.88 2.40(0.385) (0.830)

r 22.37 17.66

n 118 118

*, **, *** Denotes significance at the 0.10, 0.05, and 0.01 levels, respectively.Two-tailed p-values are reported in parentheses (based on standard errors robust to clustering of data). Estimated usingthe Cragg double-hurdle model. For brevity, first hurdle results and firm-specific intercepts are not reported.

Variable Definitions:NONFINrelative incentive weight on nonfinancial performance measures;SUBJECTIVEpercentage of bonus that is determined subjectively (withoutex antetargets);

ACCDECaccounting decentralization;OPERDECoperational decentralization;





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associated with the emphasis on financial performance measuresi.e., when BU managers have

considerable authority to make internal accounting choices, their bonus plans are less sensitive to

financial measures of BU performance and more sensitive to nonfinancial measures or subjective

evaluations. Second, we find that accounting decentralization is positively associated with

operational decentralization. Finally, we find no significant association between operational

decentralization and the emphasis on financial performance measures, although there is some

evidence that managers of decentralized BUs are less likely to be evaluated subjectively.

Taken together, our findings are inconsistent with complementarity among accounting

decentralization, operational decentralization, and the emphasis on financial performance measures.

For instance, our findings imply that the hypothesized positive association between operational

decentralization and the emphasis on financial performance measures is attenuated by an indirect

negative effect due to the positive link between accounting and operational decentralization. To our

knowledge this is the first study to provide empirical evidence suggesting that complementarity

among several organizational design choices does not hold. In contrast, most prior empirical studies

TABLE 7

Weighted Least Squares Model of Overall Incentive Strength

Variables (1)

BONUS

(2) (3)

ACCDEC 0.77 1.08**

(0.196) (0.046)

OPERDEC 3.06 3.56**

(0.122) (0.047)

ENVIRON 1.58 0.92 1.03

(0.299) (0.501) (0.432)

GROWTH 0.33 0.39 0.33(0.238) (0.117) (0.231)

INTERDEP 5.76** 4.08* 5.65**

(0.045) (0.096) (0.043)

PASTPRF 0.19 0.34 0.26(0.739) (0.512) (0.662)

SIZE 1.15 1.34* 1.20

(0.122) (0.060) (0.125)

Adjusted R2 0.17 0.12 0.15

Adjusted R2 (excl. fixed effects) 0.04 0.02 0.04

n 112 112 112

*, ** Denotes significance at the 0.10, and 0.05 levels, respectively.Two-tailed p-values are reported in parentheses (based on standard errors robust to clustering of data).

Variable Definitions:

BONUSBU managers bonus as a percentage of total compensation;ACCDECaccounting decentralization;OPERDECoperational decentralization;





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choices and disregard potentially important indirect effects arising due to complementary or

substitute relations among organizational design choices. At a minimum, our results call for the

exercise of greater caution for researchers who seek to understand firms myriad organizational

design practices.

Finally, we acknowledge a number of caveats to our study. First, we use a nonrandom sampleof BUs owned by seven multinational firms headquartered in The Netherlands. Second, our main

organizational design variables are complex constructs that can only be measured with error. Third,

due to space limitations on our survey questionnaire, we have only a limited set of control variables.

Thus, to the extent that our set of control variables is not exhaustive or measured with error, our

results may be subject to correlated omitted variable problems. Despite these limitations, we believe

our data and analyses are uniquely suited to inform future research on incentives, decentralization,

and other key organizational design choices.

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APPENDIX A

THEORETICAL MODEL

In this appendix, we show how Hypotheses 2 and 3 can be motivated by agency models similar

to the predecision private information models of Baker (1992), Bushman et al. (2000), and Baker

and Jorgensen (2003). We begin with a somewhat general model that allows for multipleperformance measures and multidimensional private information. We then solve for special cases

that motivate our hypotheses.

General Model

We consider a business unit (BU) whose contribution to the firm (or principal) is represented

byV ve, whereeis a BU-specific task andvis uncertain BU productivity withv;Nl; r2v. Thefirm delegates the task e to a BU manager or agent, who is (weakly) risk-averse with negative

exponential utility with risk-aversion parameterr0, and who can perform the task at a cost equal

to

1

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