determinants of the use of vb measures

8/3/2019 Determinants of the Use of VB Measures

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Determinants of the reliance on value-based performance

measures for managerial performance evaluation

Henri C. Dekker*

VU University Amsterdam, Department of Accounting, and School of Economics and

Commerce, Dept. of Accounting and Business Information Systems, University of Melbourne

Tom L.C.M. Groot

VU University Amsterdam, Department of Accounting

Martijn Schoute

VU University Amsterdam, Department of Accounting`

Eelke Wiersma

VU University Amsterdam, Department of Accounting

May 2011

*Corresponding author

Email: [email protected]

Acknowledgements: We are grateful for useful comments of Frank Hartmann, Steve Salterio,

Naomi Sderstrom, and participants at the 2008 EIASM Conference on New Directions in

Management Accounting, the 2009 Global Management Accounting Research Symposium,

and the 2009 Annual Meeting of the American Accounting Association.


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Determinants of the use of value-based performance measures for

managerial performance evaluation

Abstract

Value-based (VB) performance measures are argued to be more congruent than earnings

measures, since they hold managers accountable for the cost of capital used to generate

returns. Prior studies have found, however, that the use of VB measures for managerial

performance evaluation is less extensive than might be expected based on their presumed

benefits. We posit that important reasons for this relate to differences in firms importance to

manage capital costs and in managers opportunity to do so. Building on strategic and

economic arguments, we examine conditions that influence the importance of VB measures in

the performance evaluation of middle-level managers. In particular, we examine how this is

influenced by on the one hand the strategic importance for the firm to use assets intensively,

and on the other hand managers influence over VB performance, following from delegated

authority for decision making and interdependencies with other firm units. An analysis of

survey responses from 123 manufacturing firms provides significant support for the influence

of these factors. In addition, we find that reduced delegation of decision rights in response to a

need for intensive asset use partially offsets the increase in importance of VB measures for

managerial performance evaluation following from greater importance to use assets

intensively.


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1. Introduction

Financial performance measures are a central element of performance measurement (PM) and

evaluation systems in many firms (Merchant and Van der Stede, 2007). An important

consideration in the choice of financial performance measures is whether or not managers

should be held accountable for the cost of capital used to generate returns. A well-known

problem that arises when firms use earnings measures for evaluation is that managers are

provided with an incentive to invest in projects that improve their units earnings, even if this

comes at the cost of inefficient asset deployment. As a consequence, the use of earnings

measures may result in incongruence between manager and firm goals (Bromwich and

Walker, 1998; Rogerson, 1997). To resolve this incongruence, the literature on value-based

management (VBM) provides various approaches to incentivize managers to include the cost

of capital in their decision making. For instance, firms may use capital budgeting with hurdle

rates based on the cost of capital, set performance targets based on the cost of capital and

compare earnings or return measures (e.g., ROI) against this, and use non-financial

performance measures that are reflective of value drivers to incentivize value creation. A key

approach forwarded in this literature to reduce goal divergence, which we focus on in this

paper, is the use of value-based (VB) measures that explicitly include an estimate of the cost

of invested capital, such as Residual Income (RI), Economic Value Added (EVA) and Cash

Flow Return on Investment (CFROI).1

Empirical research suggests that managerial behavior

can be affected substantially by using financial performance measures that include a capital

charge. Wallace (1997), for instance, reported that firms of which the CEO was held

accountable for a VB measure (RI or EVA) invested less, divested more, used existing assets

1The claim that VB measures induce more congruent decision making is not unchallenged. Bromwich and

Walker (1998, 392), for instance, discuss that responsible units need to be relatively independent, that managers

personal costs are not considered, and that they might try to optimize current performance at the expense of

future performance. Thus these VB measures are better interpreted as partially resolving some of the congruence

issues that may arise with traditional earnings or return measures.


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more intensively, and returned more unused capital to shareholders in the form of dividends

and share repurchases.

Although some proponents of VBM expose a desirability to evaluate managers at all

organizational layers on VB measures (e.g., Haspeslag et al., 2001), empirical evidence to

date is more indicative of the opposite: only a minority of firms uses financial measures that

include a capital charge (Bouwens and Van Lent, 2007; Lovata and Costigan, 2002; Wallace,

1997). This may be even more the case at lower firm levels where additional complications

for use arise, such as constrained decision making rights and controllability. This suggests

that use of VB measures for measuring and evaluating managerial performance is contingent

upon firm context, and raises the question under which conditions firms are more likely to

hold managers accountable for the cost of capital.

In our exploration of this question, we build on strategic and economic arguments to

predict that the importance of VB measures for managerial performance evaluation depends

on the strategic importance for the firm to use assets intensively (i.e., to induce strategy-

consistent behavior), and on the influence that managers have on VB performance, and in

particular the cost of capital. By doing so, we aim to contribute evidence on the determinants

of the use of VB measures, and in particular focus on their use for the evaluation of middle-

level managers. Although managers at this level do not always have the formal decision rights

to make (dis)investments in the asset base (Bouwens and Van Lent, 2007), they can still have

significant opportunities to manage the existing asset base, such as through managing their

units working capital (e.g., inventory turnover and accounts receivable/payable), and the

efficiency with which existing assets are used (e.g., production and capacity planning). In

addition, as Bouwens and Spekl (2007) argue, by being held accountable for VB

performance, they may also be incentivized to informally impact investments in new assets

and through that influence the size of the asset base by initiating (dis)investment decisions (cf.


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Aghion and Tirole, 1997; Fama and Jensen, 1983), which proposals however are typically

formally ratified at higher managerial levels.

We define VB measures as financial performance measures that include a capital charge

for the use of (debt and equity) capital.

In developing expectations about what explains

variation in the importance of VB measures for performance evaluation of middle-level

managers, we posit that this is affected by two key conditions: (1) the strategic importance for

the firm to intensively manage the cost of capital, and (2) the managers influence over these

capital costs. With respect to the first condition, the literature on strategic performance

measurement posits that to guide and induce congruent decision making, performance

measurement choices should be aligned with a firms strategy and value drivers (Ittner and

Larcker, 2001). In applying this argument, we focus on a primary value driver identified in

the VBM literature; the strategic importance of intensive asset use (Wallace, 1997), which in

prior studies has typically been proxied by publicly available measures such as firms capital

intensity (Garvey and Milbourn, 2000) and asset turnover (Hogan and Lewis, 2005). Greater

strategic importance of intensive asset use for the firm, which we refer to as asset use

intensity, should increase the importance of VB measures to induce strategy-consistent

decision making. This value driver should be of particular relevance for the manufacturing

firms that we study, which often face high capital intensity.

The second condition concerns the influence the manager has on the performance

measure, which for VB measures in particular concerns the extent to which the manager can

affect the cost of capital through managing the asset base. Building on prior research, we view

their influence on VB performance as a result of (1) a managers decision authority, and (2)

factors beyond the managers control, in particular dependence on other firm units (Abernethy

et al., 2004; Bouwens and Van Lent, 2007). VB measures should better reflect managerial

performance (i.e., are less noisy), and thus increase in importance for evaluation, when


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managers have more decision making authority, and when their units dependence on other

firm units is lower. Since middle-level managers often lack the rights to invest in new assets,

their primary way to affect VB performance is by increasing the intensity of use of existing

assets at their disposal and by reducing working capital. They may, however, also indirectly

affect the size of the asset base by initiating new (dis)investments in assets which though

typically are ratified at higher managerial levels (Bouwens and Spekl, 2007).

While the delegation of decision rights is seen as an antecedent of performance

measurement choices, this also represents a structural design choice that can be affected by

asset use intensity and unit interdependence. In particular when these factors are high, senior

management may prefer to retain control over key decisions that affect the size and use of the

asset base and avoid managerial decisions that lead to local instead of global optimization.

Thus, management may on the one hand stimulate strategy-consistent behavior by evaluating

managers on VB measures, and on the other hand limit incentive loss by partially centralizing

decision making (Aghion and Tirole, 1997; Colombo and Delmastro, 2004), which in turn

should have a mitigating effect on the use of VB measures. In our analyses, we use a

mediation model to decompose these effects of asset use intensity and unit interdependence

by identifying their direct and indirect effects (i.e. through delegation) on the importance of

VB measures for the evaluation of middle-level managers.

We test our expectations using a sample of survey responses collected from senior

managers of 123 manufacturing firms who were asked to reflect specifically on the

manager(s) of the firms primary operating units. Evidence on the extent to which VB

measures are used at organizational levels below the executive level is scarce (Rajan, 2000).

Although prior research has documented contingencies that affect the adoption of VB

measures in CEO incentive plans (e.g., Garvey and Milbourne, 2000; Hogan and Lewis, 2005;

Lovata and Costigan, 2002), at lower levels it is well possible that firms only partially adopt


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VB measures instead of using them firm-wide (Rajan, 2000). While in principle firms can use

VBM at all organizational levels, use at lower levels creates additional complications, such as

dependencies between firm units (i.e., reduced controllability of performance outcomes) and

the proper allocation of decision rights that enables managers to affect VB performance

(Bouwens and Van Lent, 2007). An examination of the use of VB measures at organizational

levels below top management thus can provide insight into the interplay between these

variables and tradeoffs among choices (e.g., delegation and performance measurement) to be

made. For instance, we distinguish between delegation of strategic and operational decision

rights, and find that for the managerial level that we study, in particular the delegation of

operational decision rights impacts the use of VB measures. When managers are granted

sufficient operational decision rights, they are in a position to directly influence the intensity

with which assets are used, and accordingly, to influence the capital charge. This setting thus

allows an effective test of whether the importance of VB measures for managerial

performance evaluation varies with a strategic value driver (i.e., asset use intensity) that

should determine how important managing the cost of capital is for the firm, and with factors

that determine managers influence on these costs.

In sum, we aim to provide two contributions to the literature. First, we extend research on

performance measure choices, and VB measures in particular. We do this by examining how

the use of VB measures for managerial performance evaluation is affected by both the

importance to hold managers accountable for capital costs to provide incentives to manage

assets intensively, and managers influence over these costs, following from delegated

authority for decision making and interdependencies with other firm units. This analysis not

only includes an examination of how asset use intensity induces use of VB measures, but also

of how it puts constraints on the delegation of decision rights, which in turn may reduce the

extent to which managers can be held accountable for VB performance. Second, in the


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examination of VBM practices, prior research has primarily focused on the top management

level. Our study instead focuses on a lower level of analysis, and examines to what extent and

under which conditions middle-level managers are evaluated on VB measures. While we, as

expected, find that managers at this level are held less accountable for VB performance than

could be expected at higher organizational levels, we also observe much variation across

firms in the importance of VB measures for managerial performance evaluation. The results

of the partial least squares (PLS) models that we estimate provide support for our expectations

that this variation is associated with asset use intensity, delegation of (operational) decision

rights and unit interdependencies.

In the next section, we review the literature on the use of VB measures for managerial

performance evaluation and develop hypotheses. In section 3, we describe the sample

selection and variable measures, and in section 4, detail the results of the empirical analyses.

In sections 5 and 6, we discuss these results and conclude.

2. Literature review and hypothesis development

2.1. Introduction

Financial performance measures form a core part of most firms PM and evaluation systems,

both at executive and lower management levels. Financial measures fulfill an important

control function through performance planning, target setting, performance measurement and

evaluation, and are typically associated with the provision of explicit and/or implicit

incentives, such as performance-based compensation and promotion prospects. A key

consideration in the choice of financial performance measures for managerial performance

evaluation and incentive provision is whether or not to include a charge for the cost of capital.

Including a capital charge in financial measures is argued to enhance goal congruence

(Rogerson, 1997), and in particular should incentivize managers to more intensively manage


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the firms existing asset base, and to only engage in new investment opportunities when the

expected returns exceed the cost of capital.

Several empirical studies have explored conditions under which VB performance

measures are used in incentive plans, in particular at the CEO level. These studies report that

VB measures are adopted more often by firms with a large asset base (Garvey and Milbourn,

2000), a lower Market-to-Book ratio (Hogan and Lewis, 2005), and a lower R&D intensity

(Lovata and Costigan, 2002). In addition, Lovata and Costigan (2002) find that VBM

adoption is higher for defender than for prospector firms. These studies examine firm-wide

adoption of VBM and together suggest that large, capital-intensive firms in a stable

environment benefit most from the use of VB measures for evaluation.

In his discussion of Garvey and Milbourn (2000), Rajan (2000) argued that whereas prior

studies have focused primarily on the adoption of VB measures in CEO bonus contracts,

firms often use a combination of measures for performance evaluation. Survey evidence from

Sibson & Co reported by Ittner and Larcker (1998) supports this and shows that only 26.3%

of the firms that included a VB measure in their incentive plan, used this as the only measure.

Similarly, Malmi and Ikheimo (2003) and Claes (2006) found that the case firms they

studied used VB measures in combination with other financial and non-financial measures for

evaluation purposes. Rajan (2000) additionally commented that while prior studies only

examine firm-wide adoption of VBM systems, it is very well possible that firms partly adopt

VB measures (i.e., use it only in some divisions, but not in others, and use them differently

across organizational levels). Consistent with these observations, we do not explore firm-wide

adoption of VB measures, but more specifically examine their importance for the evaluation

of middle-level managers responsible for firms primary operating units.

In order to reduce the capital charge to realize adequate VB performance, managers may

use different strategies. Wallace (1997), for instance, examined the impact of VBM on the


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level of new (dis)investments in the asset base, the level of working capital (inventory

turnover, accounts receivable, and accounts payable), and the level of dividends and share

repurchasing. Prior literature shows that middle-level managers do not always have the

decision rights to make (large) (dis)investments to directly manage the asset base (e.g.,

Bouwens and Van Lent, 2007), nor influence dividends and share repurchasing decisions.

This may be a reason not to hold managers accountable for VB measures, as it is often

assumed that managers have sufficient formal authority to make project selection and

(dis)investment choices (Bromwich and Walker, 1998). Accordingly, most empirical studies

on VBM take as level of analysis the CEO or top management of the firm (some exceptions

are Claes (2006) and Malmi and Ikheimo, (2003)). On the other hand, middle-level managers

may still have many other ways to influence their units VB performance through operational

decisions that impact working capital and the intensity of use of existing assets (Bouwens and

Van Lent, 2007). For instance, by being able to decide which customers to serve and under

which conditions, they may influence accounts receivable (Bouwens and Spekl, 2007). By

being able to select suppliers they may influence materials quality and speed of delivery,

which in turn affects production speed and inventory levels. And by being able to select

production technologies and schedule production orders, managers can also determine

production speed and capacity utilization. Consistent with the argument that VB measures are

useful to incentivize managers at lower firm levels to improve asset use, the operations

research literature also emphasizes strategies for these managers to enhance the efficiency of

use of existing assets. Altendorfer and Jodlbauer (2011), for example, analytically derive how

EVA can be enhanced by lowering inventory, increasing machine utilization, and lowering

delivery times. Furthermore, even when middle-level managers do not themselves have the

formal decision rights to make large investments in assets, they may still be able to influence

such decisions by pursuing their superiors to invest (Aghion and Tirole, 1997; Bouwens and


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Spekl, 2007). Fama and Jensen (1983) decompose decision rights into initiation rights,

ratification rights, implementing rights, and monitoring rights. While the right to ratify

investment decisions as well as the monitoring of the performance of implementation are

typically retained at a higher managerial level, middle-level managers often are provided the

rights to initiate investment decisions and to implement the decision as they possess better

knowledge of market requirements.

2.2. Hypothesis development

In our theory development, we build on strategic and economic reasoning to develop the

argument that the use of VB measures for managerial evaluation depends on the strategic

importance for the firm to manage assets intensively and on managers influence on the

performance measure (in particular on the asset base and associated capital charge). Recent

studies on PM design have given particular interest to the latter aspect, managers degree of

influence, for instance by examining the influence of delegated decision rights to middle-level

managers and interdependencies between firm units (Abernethy et al., 2004; Bouwens and

Van Lent, 2007; Keating, 1997). The strategic performance measurement literature, however,

suggests that a primary design criterion is that PM choices should align closely with the

firms strategy and value drivers, in order to guide and induce congruent decision making

(Ittner and Larcker, 2001). Following this reasoning, we first argue that firms are more likely

to include a capital charge in financial performance measures when a key value driver, the

efficient deployment of the firms asset base, increases in importance for the firm to gain a

competitive advantage.

Second, the managers influence on the asset base and capital charge can be viewed as a

function of his authority to make decisions that affect the measure, and his controllability over

the measure. As key determinants of this influence, we examine managers delegated decision

rights and the scope of influence over the firms value chain activities, which determines the


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extent of interdependencies with other firm units. Since the delegation of decision rights is

also an important organizational design choice (e.g., Abernethy and Lillis, 2001; Abernethy et

al., 2004; Bouwens and Van Lent, 2007; Nagar, 2002), consistent with prior studies we model

this choice as mediating the effects of asset use intensity and unit interdependencies on the

importance of VB measures for managerial performance evaluation. Figure 1 summarizes the

research model.

[Insert Figure 1 about here]

The strategic importance to manage capital costs

The strategic performance measurement literature suggests that PM design should be closely

linked to a firms strategy and value drivers (Ittner and Larcker, 2001). When performance

measures do not represent important value drivers, managers are more likely to ignore their

impact on decision making, which increases incongruence between managerial and firm

objectives. With respect to the choice whether or not to hold managers accountable for the

cost of capital, a key value driver identified in the VBM literature is the importance to the

firm of efficient asset use to obtain and maintain a competitive advantage, which we label as

asset use intensity.2

In particular, the capital charge over the investment base should induce

more congruent managerial decision making regarding investments in new assets and efficient

use of existing assets (Wallace, 1997). Accordingly, it is suggested in this literature that the

benefits of using VB measures for incentive provision increase when the importance of

intensively managing the asset base is more important to the firm. Consistent with this

argument, Garvey and Milbourn (2000) found that the adoption of VB measures (EVA) in

CEO contracts is positively associated with the size of firms asset base. Malmi and Ikheimo

(2003) found that of the six firms they studied, for four the primary reason to adopt VB

2Prior studies that use archival data to examine firms use of VB measures have proxied for this importance

using measures such as capital intensity (Garvey and Milbourn, 2000) and asset turnover (Hogan and Lewis,

2005). The role of these proxies, however, is to reflect how important intensive asset management is for the firm.


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measures was to increase the efficiency of asset use. A consequence of the introduction of VB

measures in these firms was a reduced hunger for capital investments and an increased

concern about the cost of working capital. Claes (2006) identified similar motives in the three

case firms that he studied. In an earlier study, Wallace (1997) documented that adopters of

VB measures used existing assets more intensively, invested less in new assets, divested more

and increased payouts to shareholders, as compared to non-adopters. Given the primary role

of firm assets, and in particular the stimulation of efficient asset use in the decision to hold

managers accountable for a capital charge, we hypothesize that the inclusion of such a charge

in financial performance measures (i.e., use of VB measures) is positively affected by the

strategic importance for the firm to use assets intensively.

Hypothesis 1: The importance of VB measures for managerial performance evaluation is

positively influenced by the strategic importance for the firm to use assets intensively.

The opportunity to influence VB performance

While asset use intensity describes the importance for the firm to incentivize managers to use

assets intensively, if managers are to be held accountable for VB measures, they should also

have the opportunity to influence VB performance, and in particular the capital charge, by

being able to manage the asset base. A primary way for the firm to provide this influence is

through the delegation of decision rights. The theoretical literature and prior empirical studies

that primarily rely on economic (agency-based) reasoning, have documented that the

delegation of decision rights is an important determinant of PM choices (e.g., Abernethy et

al., 2004; Bouwens and Van Lent, 2007; Milgrom and Roberts, 1992). However, the literature

also recognizes that the process of delegating decision rights is complex and multifaceted

(Aghion and Tirole, 1997), and that not only the extent of delegation, but also the type of

decision rights can affect managers opportunity to manage the asset base and capital charge.


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These types of decision rights can involve strategic decisions that typically include rights to

make (dis)investments in new (existing) assets, and operational decisions that typically relate

to the use of existing assets (Malmi and Ikheimo, 2003). Strategic decision rights can, for

instance, involve decisions to develop new products or enter new markets that require

investments in new assets. Accordingly, a key function of VB measures in relation to strategic

decision rights is to incentivize managers to select projects/investments only if these are

expected to generate returns that meet the firms or units cost of capital. As Bouwens and

Spekl (2003) note, for middle-level managers often these rights are limited to the initiation

of investments (e.g., in new production technology), while ratification takes place at higher

managerial levels (Fama and Jensen, 1983). Operational decision rights include decisions

such as the selection of suppliers, hiring of workers, scheduling of production and

optimization of capacity use. Typically, these decision rights concern the use of existing

assets, and the primary role of VB measures is to incentivize more intensive use of these

assets and a reduction of working capital (e.g., Claes 2006; Malmi and Ikheimo, 2003). More

delegation of both types of decision rights thus in different ways should increase middle-level

managers influence on their units cost of capital.

Another reason why the distinction between strategic and operational decision rights is

important is that superiors typically overrule subordinates decisions more frequently when

decisions increase in importance, even if these are formally delegated (Aghion and Tirole,

1997). Jointly with the expectation that strategic decision rights are typically retained at

higher managerial levels (e.g., Bouwens and Van Lent, 2007), this implies that operational

decision rights likely will be more strongly associated with use of VB measures for the

managerial level that we study. Nevertheless, we expect the delegation of both types of

decision rights to increase managers opportunity to influence capital costs and, as a


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consequence, to be associated with increased importance of VB measures for performance

evaluation.


positively influenced by the delegation of (a) strategic decision rights and (b) operational

decision rights.

A second element that affects managers influence on the cost of capital includes factors that

are beyond the control of the manager. Following prior research, we particularly examine

interdependencies with other firm units that may affect the focal units performance

(Abernethy et al., 2004; Bouwens and Van Lent, 2007). In our research setting of middle-

level managers responsible for firms primary operating units, we consider these

interdependencies to be a function of the scope of the firms value chain activities performed

by the focal unit. Given the number of value chain activities taking place within the firm, a

broader scope of those activities conducted by the unit should limit the dependence on other

firm units and provide a greater direct influence on the intensity of asset use. If, for instance, a

unit conducts not only production activities but also R&D, the manager has the opportunity to

make tradeoffs between product design and manufacturing efficiency, and through that

improve production speed and capacity use. In contrast, when the scope of value chain

activities performed by the unit is narrow, dependence on other units likely increases and the

managers opportunity to influence the efficiency of asset use decreases.3

Accordingly, the

performance and decisions of other units may reduce the focal units opportunity to use assets

efficiently and influence its performance. For example, when the purchasing function is not

part of the focal unit, and purchasing decides to buy larger batches materials of lower quality,

this may increase inventory levels and reduce production efficiency and capacity use due to

3While there may always be some externalities, for a given set of assets, a broader scope of influence in general

should provide greater influence on efficient asset use as compared to a narrow scope.


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interruptions. This situation provides the unit manager with limited opportunities to manage

tradeoffs between decisions that affect working capital and intensity of asset use, and thus

adds noise to the managers VB performance.

An additional concern that arises when the scope of activities within the unit is narrow is

that VB measures can create negative externalities of unit decisions on other units by

incentivizing managers to optimize local performance, even if this comes at the expense of

firm performance (Abernethy et al., 2004). For instance, responsibility for VB performance

may induce a production manager to schedule activities in order to minimize inventory, but

by doing so, create inefficiencies onwards in the value chain (so-called parochial behavior;

cf. Bouwens and Spekl, 2007). Accordingly, a broader scope of firm activities performed by

the unit should reduce the dependence on other units (reducing noise in the performance

measure), mitigate negative externalities, and provide better opportunities to affect asset use.

We hypothesize that:


negatively influenced by interdependencies with other firm units.

The mediating role of delegation of decision rights

As shown in Figure 1, and following prior studies (e.g., Abernethy et al., 2004; Bouwens and

Van Lent, 2007; Nagar, 2002), we also expect delegation of decision rights to be endogenous

and to be affected by the strategic importance for the firm to use assets intensively and by unit

interdependencies. Reasons to delegate decisions rights abound. Colombo and Delmastro

(2004) classify these reasons in an information processing and an incentive loss

perspective. The information processing perspective follows the argument that subordinate

managers need more flexibility to implement urgent decisions and, since they are closer to the

market, are often better informed. The incentive loss perspective suggests that by delegating


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decision rights, top management looses the ability to directly monitor decisions. Prior studies

have mainly focused on the information processing perspective. Abernethy and Lillis (2004),

for example, argue and find that when service orientation is an important strategic choice for a

hospital, hospital managers need more decision rights to be able to react quickly when

needed. This delegation of decision rights in turn is associated with more emphasis on

performance measures. Chung et al. (2009) replicate this result in a university setting.

In contrast, when intensive use of assets is an important value driver and therefore

managers capital hunger needs to be restricted, it can be expected that the incentive loss

perspective gains greater importance, reducing top managements willingness to delegate.

Aghion and Tirole (1997) show analytically that important decisions will be monitored more

closely by superiors, since there is more at stake. Colombo and Delmastro (2004) find

empirical support that the extent of delegation to middle-level managers is less when there is

more at stake in the decisions to be made. In particular, they find that decisions about capital-

intensive production technologies (in contrast to workforce decisions) are delegated less

often. Accordingly, based on the incentive loss perspective, we expect that when intensive use

of firm assets gains greater strategic importance, this will induce senior management to retain

decision control by centralizing more decisions.4

This implies that when the strategic

importance of intensive asset use is high, it is less likely that decision rights will be delegated.

As a consequence, while asset use intensity is expected to have a positive direct effect on the

importance of VB measures for evaluation to induce strategy-consistent behavior (cf. H1), it

also is expected to have an offsetting negative indirect effect by reducing the delegation of

decision rights to avoid incentive loss. This tension in the research model reflects the

alternative ways in which we expect senior management to on the one hand incentivize

middle-level managers to direct effort towards VB performance, and on the other hand retain

4In the empirical analysis we also include a control variable (i.e., organizational structure) that proxies for

information differences between units, which reduces the possible impact of the information processing

perspective.


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some of the decision rights that impact the size and use of the asset base. The total effect thus

should be an offsetting or reduced effect of asset use intensity on the importance of VB

measures (i.e., a reduced importance of the measure in the PM system).

Similarly, when the scope of the firms value chain activities performed within a unit is

narrow, and thus interdependence with other units is high, senior management likely prefers

to centralize more decisions to ascertain coordinated decision making, and avoid local

optimization and negative externalities (Abernethy et al., 2004). Indeed, Bouwens and Van

Lent (2007) find that units with greater interdependencies receive fewer decision rights than

units that face limited interdependencies. Thus, we expect that organizational units

performing a broader scope of activities will receive more decision rights than those with a

more narrow scope.

Since units with a broader scope have less negative externalities with other firm units, we

also expect that this greater delegation is associated with more emphasis on VB measures for

evaluation. Accordingly, more unit interdependencies should both directly (cf. H3) and

indirectly (through reduced decision rights) result in a lower importance of VB measures for

evaluation. To complete our path model, we thus hypothesize that asset use intensity and unit

interdependencies both reduce the influence of delegation of decisions rights to avoid control

loss:5

H4: Delegation of strategic decision rights is negatively influenced by (a) the strategic

importance for the firm to use assets intensively and (b) interdependencies with other firm

units.

5Although specifying and modeling reciprocal effects between delegation and PM choices may be desirable to

obtain a closer test of their causal interrelation (e.g., Nagar, 2002), as in other studies (e.g., Colombo and

Delmastro, 2004), the data requirements to do so properly are very demanding. Specifically, such an analysis

would require three adequate instrumental variables that each would have to be significantly and equally related

to one dependent variable and be orthogonal to the other two (Wong and Law, 1999). Instead, in our

specification we follow recent papers (e.g., Abernethy et al., 2004; Abernethy and Lillis, 2004; Bouwens and

Van Lent, 2007; Chung et al., 2009) that have modeled and found evidence of significant effects of

decentralization on PM choices, but not the other way around.


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H5: Delegation of operational decision rights is negatively influenced by (a) the strategic

importance for the firm to use assets intensively and (b) interdependencies with other firm

units.

3. Sample and variable measurement

3.1. Sample

The data used to test the hypotheses were obtained using a large-scale multi-purpose

questionnaire administered in 2004-2005 among members of the Controllers Institute (CI; a

Dutch institute for Chartered Controllers and Chartered Accountants). To select an

appropriate sampling frame from the CI member list, we used three criteria: (1) the firm the

respondent worked for had at least 100 employees, (2) it was located in the Netherlands, and

(3) it was a for-profit firm. In addition, we only selected respondents who, based on their job

title occupy higher positions in their organizations (e.g., CEO, VP, CFO, corporate controller,

BU manager, BU controller), to ascertain they could provide knowledgeable responses to the

questionnaire items. This provided a sampling frame of 2,584 potential respondents who were

sent a questionnaire with a prepaid return-envelope and an enclosed letter that explained the

purpose of the study and the involvement of the CI to stimulate participation in the study.

After one month, non-respondents were sent a reminder and a new questionnaire. A second

reminder was sent by email, with a link to an online version of the questionnaire. In response

to this email non-respondents could indicate their reason for not responding to the

questionnaire, or fill it in online. In total, 454 questionnaires were returned, which represents

a 17.6% response rate. Respondents generally were high placed managers at the firm level

(e.g., CEO, CFO, corporate controller), or at the level of business unit/independent location

(BU manager, division/BU controller, location manager), and were well qualified to answer


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all survey questions. On average, respondents were 41 years of age, had 3.4 years of

experience in their current position, and a total experience in that function of 6.8 years.

For this study, we use only the responses from the 130 manufacturing firms in the sample

since the nature and impact of value drivers (in particular the one that this study focuses on;

the importance of intensive asset use) are typically industry specific, and the items that we

used for the measurement of asset use intensity have a specific meaning for manufacturing

firms.6

Kleiman (1999) reported that VBM is used relatively more often in manufacturing

firms, while Riceman et al. (2002) found that managers in service functions, such as customer

support, benefit less from being evaluated on VB measures as compared to managers in

capital-intensive operational functions. Accordingly, we use a homogenous sample from one

industry to provide an appropriate test of our hypotheses, but also add control variables for the

sub-industry that firms operate in.

In the instructions about the questions on performance evaluation, we asked respondents

to reflect specifically on the manager(s) of the firms primary operating units. Thus, we omit

PM practices in other (e.g., customer service and staff) units. Of the 130 respondents, 7 failed

to answer a question about the organizational structure of the unit, resulting in a dataset

available for analysis of 123 survey responses.7

An early-late respondents analysis between

the 30 first responding manufacturing firms and the 30 latest responding manufacturing firms

on all model variables shows no significant differences (at p


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response bias is unlikely to be a problem. The 123 firms included in the final sample have a

median number of employees close to 2,000.8

3.2. Variable measurement

Dependent variables

Appendix A provides details of all questionnaire items used for analysis. For the hypothesis

tests, we use two dependent variables. The first is the importance placed on value-based

measures (VBa) in the evaluation of the manager(s) of the firms primary operating units,

which is an item measured on a 1-7 Likert scale (1-not important, 7-very important). To frame

respondents towards financial performance measures that include a charge for the cost of

capital, in the question we provided two specific examples of VB measures: EVA and

CFROI. These are well-known measures in our sample of managers with a financial

background.9

While VBa provides information on the absolute importance of VB measures in

managerial performance evaluation, it is less informative about its relative importance to

other (financial) measures. Prior empirical studies have found that when firms adopt VB

measures, these are not used fully at the expense of earnings measures, but instead they often

combine multiple financial measures (Ittner and Larcker, 1998; Malmi and Ikheimo, 2003).

Thus, while VBa indicates the overall extent to which managers are held accountable for the

cost of capital, differences can also exist in the extent to which managers are held accountable

for financial bottom-line performance. Since the inclusion of a capital charge is the distinctive

8The respondents of these firms are generally high placed and can be classified as board member (44, e.g., CEO,

CFO, VP), head (18) or member (12) of a support department to the board (e.g., corporate controller), BU

manager (8), head (29) or member (7) of a support department to BU management (e.g., division controller,business controller), or head (5) of a BU department. These different types of respondents (board members, staff

managers or operational managers) provided similar answers on the questions for our dependent and independent

variables. A question on whether respondents faced any difficulties completing the questionnaire also supports

their ability to do so adequately, as only 13% (which were spread over all respondent types) indicated they did.

Excluding these does not affect our results and inferences.9

In line with our rationale to focus only on manufacturing firms and with Kleiman (1999), VBa is significantly

higher in this industry than in most other sampled industries, including construction, professional services, and

transportation, warehousing and communication. There are no significant differences, however, with firms from

trade and financial services, which may relate to the importance of working capital (inventory) and capital

management in these industries.


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element of VB measures, their importance relative to other financial measures (e.g., profit)

should increase if including a capital charge increases in importance for the firm.10

Accordingly, we also use a relative measure as dependent variable (VBr), computed as the

importance of VB measures relative to the importance of profit (Pa) for evaluation:

VBa /(VBa + Pa).

Since profit is included in VB measures, increasing values on VBr indicate that the capital

charge is relatively more important in the managers performance evaluation.

One way how firms stimulate VB performance is through the provision of financial

incentives (Claes, 2006; Malmi and Ikheimo, 2003). In the questionnaire we included an

item about whether or not the firm provides performance-based compensation to the

manager(s) of its primary operating units. A t-test supports that use of incentive compensation

is associated with a greater importance of VB measures (p


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(4) expansion of production capacity.11

While VBa correlates significantly with all four

performance measures (rranges between 0.22 and 0.37), Pa correlates only significantly with

inventory level (r=0.22). These results confirm that when firms rely on value-based measures,

this is often in conjunction with non-financial measures that reflect critical value drivers (i.e.,

intensive asset use), and support criterion-related validity for VBa by indicating that this

measure is distinctively different from Pa.

Independent variables

Table 1 reports the factor analysis results (based on principal axis factoring and Oblimin

rotation) for the constructs that are measured using multiple items.

[Insert Table 1 about here]

Asset use intensity is measured by three items specific to manufacturing firms reflecting the

strategic importance for the firm to use assets intensively (ASSINT). This construct reflects

how important the following items are to achieve a competitive advantage: (1) low inventory

levels, (2) optimal utilization of production capacity, and (3) high production speed.12

Factor

analysis shows that these items load on one factor that explains 59% of the items variance.

The Cronbach alpha of 0.64 indicates sufficient reliability.13

Delegation of decision rights is measured with a scale that captures the lowest

management level in the organization that has the right to make a variety of strategic and

operational decisions. The items are measured on a 1-5 scale anchored as 1) headquarters, 2)

business unit manager, 3) location manager, 4) department manager, and 5) team leader.

11Indeed, these four non-financial measures also correlate positively and significantly with asset use intensity.

12While strategic choices aimed at competitive advantage may not always be aimed at enhancing shareholder

value as implied in VBM, in general these perspectives can be expected to converge, as enhanced shareholder

value may follow from a competitive advantage.13

A similar factor analysis for all responses on these items, including non-manufacturing firms, results in a

variance explained of only 26.7% and item loadings and Cronbach alpha are significantly lower. This supports

the assertion that asset use intensity has different meanings across industries and our choice to limit the analysis

to manufacturing firms. In addition, the reported factor solution is robust against a sequence of alternative

estimations in which we successively leave out the observations for each of the sub-industries, supporting the

homogeneity of the construct across different sub-industries.


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Accordingly, higher values on these items indicate more delegation of decision rights.14

As

expected, factor analysis on the 11 items extracts two clear dimensions that reflect strategic

decision rights (STRDR) and operational decision rights (OPDR). Strategic decision rights

include rights to develop and introduce new products, and to explore new markets. These

decisions typically are associated with investments in new assets. Operational decision rights

include rights such as selecting suppliers, determining production capacity, setting production

priorities, hiring production personnel, and setting prices. While the discretion to invest in

new assets may be limited with these rights, they can strongly impact the intensity of use of

existing assets and working capital and through that VB performance. For instance, setting

adequate production priorities can enhance capacity use, selecting adequate suppliers can

affect quality, delivery performance and inventory levels, and hiring qualified production

personnel can improve operations and asset use. The factor solution explains 53% of the

variance in all items. The Cronbach alpha of 0.73 for STRDR and 0.83 for OPDR indicate

adequate reliability.

Unit interdependence is reflected by the scope of value chain activities under the units

control, and is measured as the sum of the number of different primary operating value chain

activities performed by the focal unit. Respondents were asked to indicate whether or not the

following activities take place within the unit: production, service delivery, logistics, research

and development, marketing, purchasing and/or sales, and provision of after-sales service.

The count measure of scope of the value chain (SCOPE) thus varies from one (if the unit

performs only one operational activity) to seven (if all are performed). Units that include a

14Two items are excluded from the analysis since they show very little variation and are generally not delegated

to management levels below the headquarter level. Only 15% of the firms in the sample delegates decision rights

for making large investment decisions to the business unit level (and 0% below this level), and only 8%

delegates decision rights to attract loans below the headquarter level. These observations are consistent with

Bouwens and Van Lent (2007). In addition, we excluded an item about the rights to determine bonuses for direct

production personnel as many sample firms did not have a bonus system for this level.


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broader scope of activities should be less dependent on other firm units and be better able to

influence asset use and working capital.

Control variables

We control for four variables that may influence delegation choices and the importance of VB

measures for evaluation: organizational structure, respondent level, firm size, and sub-

industry membership. First, our measurement of decision rights may not fully capture the

different types of decisions delegated and managers real authority . Accordingly, we

control for the firms organizational structure, which relates to the information characteristics

of its organizational units and the decisions taken by managers. We distinguish between

product-market, product and functional structures, which differ in their ability to employ local

knowledge, and allow firms to (partly) delegate real authority.15

Firms are structured as more

independent decision making units when the benefits of local decision making are higher than

the cost of upward communication and for coordination between units (Jensen and Meckling,

1995). Since organizational structure affects managers opportunity to make decisions based

on local knowledge, this proxies for information asymmetries and for managers real

authority (Aghion and Tirole, 1997). In addition, organizational structure is reflective of unit

interdependencies, with product-market structures providing the lowest degree of

interdependence. As argued before, since VB measures may induce parochial behavior, they

are ill-suited to incorporate the effects of decisions made in other units (Bouwens and Spekl,

2007). Thus, managers in firms with a product-market structure should have more influence

on VB performance than managers operating in other structures. We use two variables that

15A product-market structure builds on both local product and market knowledge in deciding on which products

to produce and sell, and can react relatively swiftly to changes in customer preferences and competitive

circumstances. Product-oriented structures (where functions are clustered around one product or product group)

and functional structures (where responsibilities are divided over functional groups) build to a lesser extent on

local knowledge and are relatively less able to respond quickly to environmental changes.


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indicate whether (1) or not (0) the firm has a functional structure (FUNC), or a product

structure (PROD), with product-market structure as reference group.

Second, we control for respondent level as a proxy for the level of the managers the

respondents reflect on. In particular, the managers of operating units reflected on by

headquarter respondents may be at a relatively higher level than those reflected on by

BU/location level respondents, which could affect the extent to which they are held

accountable for VB performance.16

The indicator variable (LEVEL) is coded 1 for

headquarter-level respondents, and 0 for BU/location-level respondents.

Third, we control for firm size, which likely influences firms use of VBM. Larger firms,

for instance, have been argued to have higher agency costs because they have higher risks of

cross-subsidizing unprofitable units and consuming perks (Garvey and Milbourn, 2000;

Lovata and Costigan, 2002). In addition, the cost of introducing a VBM system may be more

easily covered by larger firms (Garvey and Milbourn, 2000).17

We measure firm size (SIZE)

as the log of the number of employees working in the firm.

Finally, prior studies suggest that the choice between alternative accounting measures is

often industry specific (e.g., Ely, 1991). Rajan (2000) further suggests that firms may simply

adopt VB measures because industry leaders have done so. Therefore, using indicator

variables, we control for the firms membership of one of the following six sub-industries: (1)

food, beverage and tobacco products (n=27), (2) pulp, paper and paper products; publishers

and printers (n=13), (3) chemicals and chemical products (n=23), (4) electrical and optical

equipment and instruments (n=11), (5) basic metals and fabricated metal products (n=15), and

(6) machinery and equipment (n=13) (the reference group is other (n=21)). Although this

16While effects relating to differences in decision rights should be captured by STRDR and OPDR, VB measures

have also been argued to be more relevant for managers at higher levels as they may better understand how to

influence VB measures and value drivers (Ittner and Larcker, 1998; Malmi and Ikheimo, 2003).17

We control for firm size instead of unit size since the underlying arguments concerning agency costs and

performance measurement system costs relate to firm size instead of unit size.


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study purposefully focuses only on manufacturing firms, these indicators control for potential

differences at the sub-industry level.

4. Results

4.1. Descriptive statistics and variable correlations

Table 2 reports descriptive statistics for all variables.

[Insert Table 2 about here]

With a mean of 3.67, the importance of VB measures for evaluation is below the mid-point of

the scale (4); however, the variation is substantial (SD=2.07). Thus, although (as expected)

the average extent to which managers of primary operating units are evaluated on VB

measures is not high, in many cases they appear to be important measures for evaluation. Of

the 123 respondents, 25 provided a score of 1, which means that the VB measures are not at

all important. On the other hand, 31 firms provide a score of 6 or 7 indicating substantial

importance of the measures for evaluation. On average, VB measures are less important than

profit (mean VBr=0.36, where 0.50 would indicate equal importance).

The data further confirm that operational decision rights are delegated more frequently

(=2.57) than strategic decision rights (=1.96). Although the sample data are collected

within one industry, there is substantial variation in the importance of intensive asset use to

achieve a competitive advantage (=4.53, SD=1.14).18

Table 3 reports the correlation

coefficients between all model variables (based on mean scores for multi-item constructs).

[Insert table 3 about here]

Table 3 shows that VBa

and VBr

are strongly correlated (r=0.84, p


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advantage (r=0.27,p


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all items from the measurement models.20

Table 4 and Figure 2 reports the results for both

models. Since the results are very similar for VBa and VBr, we discuss these simultaneously.

[Insert Table 4 and Figure 2 about here]

Consistent with hypothesis 1, asset use intensity has a positive and significant influence on

VBa and VBr. Delegation of operational decision rights also has a positive and significant

influence on VBa and VBr, which supports hypothesis 2b. Inconsistent with hypothesis 2a (and

in contrast to the positive correlation in Table 3), however, delegation of strategic decision

rights has no impact on VBa and VBr. One reason for this may be that these decisions are

monitored more directly and overruled when not in line with firm strategy (Aghion and

Tirole, 1997). A closer inspection, however, shows that delegation of strategic decision rights

is significantly associated with organizational structure (i.e., delegation is highest in firms

organized by product-market combinations), which factor is also significantly associated with

VBa and VBr (which are highest in product-market combinations). Thus, a more nuanced

interpretation is that delegation of strategic decision rights is associated with organizational

structures that provide units relatively greater independence, which facilitates the use of VB

measures for evaluation (Bromwich and Walker, 1998).

In line with hypothesis 3, the scope of value chain activities under managerial control has

a positive influence on both VBa and VBr. This supports that VB measures, and in particular

the capital charge included, gain importance when there are fewer interdependencies with

other firm units, and the manager has greater influence on the capital charge. Thus, managers

with a broader scope of influence over the firms value chain activities are not only held more

accountable for aggregate financial performance measures as found in prior studies (e.g.,

20In addition, in the appendix we report the composite reliability and average variance extracted (AVE) of each

construct. Composite reliability ranges between 0.81 and 0.87, indicating that internal consistency reliability is

satisfactory. The AVEs range between 0.46 and 0.66, which is higher than all squared correlations between

constructs, supporting that the constructs have good discriminating validity (Fornell and Larcker, 1981). The

AVE for operational decision rights, however, is slightly lower than the rule of thumb that it should be higher

than 0.50; cf. Fornell and Larcker (1981). Finally, we also factor analyzed all items ofASSINT, OPDR andSTRDR simultaneously, which reinforces that these are three separate constructs and thus further supports

discriminant validity. This test also indicates common method bias is unlikely to be a problem.


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Abernethy et al., 2004; Keating, 1997), but also more specifically for the capital charge

included in these measures.21

The delegation of decision rights is influenced negatively by asset use intensity. This

effect is only significant, however, for operational decision rights (H5a), and not for strategic

decision rights (H4a). Together with the support for H1, these results suggest that when asset

use intensity becomes more important, on the one hand VB measures gain importance, but on

the other hand top management also retains more direct control over decisions that impact

asset use. The total effect, consisting of the positive direct effect of asset use intensity on

importance of VB measures (0.21) and the negative indirect effect via operational decision

rights (-0.19*0.20=-0.04), is positive (0.17). Thus, in total, greater importance of intensive

asset use is associated with greater importance of VB measures for managerial evaluation.

To analyze which types of operational decision rights are more often centralized when

asset use intensity increases, we additionally analyze each decision right individually. Using

the same model specification, we find that asset use intensity is most strongly related to the

centralization of production capacity choices (p


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indicate that while organizational structure has no influence on the delegation of operational

decision rights, as noted earlier, firms with a product-market structure delegate significantly

more strategic decision rights. This finding partially converges with the results of Colombo

and Delmastro (2004), who found that firms with a more complex organizational structure

delegate more decision rights (although they did not differentiate between types of decision

rights). Since organizational structure proxies for local knowledge and information

asymmetry, this finding also can be connected to prior accounting studies that find a positive

association between information asymmetry and delegation (e.g., Abernethy et al., 2004;

Bouwens and Van Lent, 2007; Nagar, 2002). As noted above, VB measures are also more

important in firms with a product-market structure than in those with a product structure,

although the difference with firms with a functional structure is not significant. Consistent

with Table 3, respondent level is negatively associated with the delegation of both types of

decision rights, but has no influence on VBa and VBr. Firm size has a positive and significant

influence on both VBa and VBr, consistent with arguments of higher agency costs and the

better covering of the costs of VBM. While the controls for sub-industry membership

(untabulated) show some significant effects, their inclusion does not change the influence of

the hypothesized variables.22

Thus, the results are unlikely to relate to structural sub-industry

differences (e.g., in asset use intensity).

5. Discussion

Several of our results merit further discussion. First, prior studies have focused on explaining

the use of or weight on performance measures primarily by variables reflecting the operating

context of the firm, such as interdependencies, information asymmetries and delegation of

decision rights (Abernethy et al., 2004; Bouwens and Van Lent, 2007; Keating, 1997; Nagar,

22Positive significant coefficients on VBa and VBr are found for chemical & pharmaceutical firms (t-value=1.69,

p


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2002). Our results with respect to the delegation of operational decision rights and scope of

value chain activities under the units control support the influence of these variables on the

importance of VB measures for managerial evaluation. However, we find that this importance

is not only a function of managers opportunity to affect VB performance, but also of their

firms incentives to do so, as reflected by the importance of a specific value driver. This

supports arguments in the strategic PM literature that PM choices should reflect the outcomes

of value driver analysis to induce behavior congruent with firm strategy (Ittner and Larcker,

2001). Since our data come from a relatively homogenous sample of manufacturing firms and

the analyses control for sub-industry membership, our finding indicates that also within one

industry variation in the importance of a value driver explains variation in PM choices.

Second, we find a complex set of relationships between the decision rights managers

receive and the importance of VB measures. Of the two types of decision rights that we

examine, only the delegation of operational decision rights is directly associated with the

importance of VB measures. These decision rights enable managers to influence the intensity

of asset use (e.g., by scheduling production, filling capacity, selecting high quality suppliers)

and working capital (e.g., by adjusting inventory levels). While the delegation of strategic

decision rights has no significant direct effect, we find that middle-level managers in firms

organized by product-market combinations (who on average have more delegated rights to

develop and introduce new products and explore new markets) are also held more accountable

for value-based performance. The data further indicate that decision rights to make new

significant investments are rarely taken at levels below top management (as also observed by

Bouwens and Van Lent (2007)). This implies that when managers are evaluated on VB

measures, it is not per se imperative to provide them with investment or divestment rights,

since through the delegation of other decision rights they appear to be sufficiently able to

influence the cost of capital in other ways.


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Third, we find support that in the context of high asset use intensity delegation of

decision rights and VB measures are used as alternative ways to increase control. While the

delegation of operational decision rights is positively associated with importance of VB

measures, the negative effect of asset use intensity on delegation of these rights suggests that

senior management relies on both incentive provision through VB measures and

centralization of particular decisions to mitigate incentive loss. The overall effect is a reduced

but still positive influence of asset use intensity on importance of VB measures for managerial

evaluation. Thus, these results suggest that, conditional on asset use intensity, a balance needs

to be found between the delegation of decision rights and the extent to which managers are

held accountable for VB performance for the decision rights that they do receive.

Fourth, a risk of VB measures identified in the literature is that they can create parochial

behavior by incentivizing managers to improve their performance without considering

externalities of their decisions on other units (Bouwens and Spekl, 2007). Such behavior

should be less problematic when units include a broader scope of the firms value chain

activities, which limits interdependencies with other units. We indeed find that when the

scope of activities in the unit is broader, VB measures gain importance for evaluation. We

also find that these measures are more important in firms organized as product-market

structure, which similarly are characterized by greater unit independence.

Finally, our data indicate that middle-level managers are often evaluated on both profit

and VB measures. We find that the model variables help explaining both the absolute

importance of VB measures, and the importance relative to profit. The observation that VB

measures and profit measures are used in combination raises the question why firms use

multiple financial measures simultaneously, in particular since VB measures already include

profit. One explanation is that PM system changes are slowly implemented and that firms are

reluctant to dispose old practices (Malmi and Ikheimo, 2003). Alternatively, firms may also


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deliberately add a measure with a capital charge to earnings measures to increase managers

awareness of the cost of capital used. This is in line with survey evidence of Haspeslagh et al.

(2001), who found that the most important reason to adopt VB measures in their sample was

that managers simply are not sufficiently aware of the costs involved with capital use.

6. Conclusion and limitations

In this study, we build on strategic and economic arguments to predict conditions that

influence the importance of value-based measures for the performance evaluation of middle-

level managers. Our basic tenet is that these managers are held more responsible for value-

based measures when it strategically becomes more important for their firm to intensively

manage the cost of capital and when their influence over these capital costs increases. We find

that the importance of value-based measures for managerial performance evaluation increases

when intensive asset use is an important value driver for the firm, when managers receive

more operational decision rights, and when they have control over a broader set of activities

of the firms value chain, limiting the interdependencies with other firm units. Although we

also find that firms delegate less operational decision rights when it strategically is more

important to use assets intensively, the overall influence of asset use intensity on use of value-

based measures remains positive.

Our results should be interpreted within several limitations. First, the level of analysis is

at the manager(s) responsible for the primary operating activities of an organizational unit.

Although this is an advantage because within the firm only some managers might be

evaluated on value-based measures and others not, and it thus allows us to examine the

influence of factors that affect the relevance of VB measures for evaluation, it also implies we

have less information on whether such measures are used firm-wide and on their importance

at other levels. While our control variable for respondent level indeed supports differences

exist in use of value-based measures across firm levels, an interesting direction for future


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situations characterized by less appropriate use (misfit). Finally, a common limitation when

using cross-sectional data is that it does not allow establishing directions of causality between

the variables. Notwithstanding these limitations, our findings add to the insights into

determinants of the use of value-based measures for managerial evaluation.

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Figure 1: Representation of the research model

Unit interdependence

Asset use intensity Delegation of strategic

& operational decision

rights

Importance of VB

measuresControl variables


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Figure 2, Panel A: PLS model estimations for the effects on the absolute importance of VB

measures1

Figure 2, Panel B: PLS model estimations for the effects on the relative importance of VB

measures

1The effects of the control variables FUNC, PROD, LEVEL and SIZEare not reported in these graphs.


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Table 1: Results of factor analyses on the multi-item scales (N=123)

Constructs/items Factor loadings1

Panel A: Asset use intensityOptimal utilization of production capacityHigh production speed

Low inventory levels

0.710.66

0.50

Eigenvalue (incr. variance explained)Cronbach

1.77 (59%)0.64

Panel B: Delegation of decision rightsOperational decision rights

Selecting suppliersDetermining production capacity

Hiring production personnel

Setting production prioritiesSetting product/service prices

Determining personnel salary

Choosing production methods/systemsOrganizing marketing campaigns

Strategic decision rightsIntroducing new products/services

Developing new products/servicesExploring new markets

0.780.74

0.69

0.640.58

0.52

0.480.45

0.95

0.750.43

Eigenvalue (incr. variance explained)

Cronbach

4.13 (38%)

0.83

1.67 (15%)

0.731Factor loadings are based on principal axis factoring with Oblimin rotation.


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Table 2: Descriptive statistics of the model variables (N=123)

Mean St. dev. Theoretical

range

Min Max

VBa

VBr

3.67

0.36

2.07

0.15

1-7

0.13-0.88

1

0.13

7

0.88

ASSINT 4.53 1.14 1-7 1 7

OPDR 2.57 0.63 1-5 1.37 4.00

STRDR 1.96 0.68 1-5 1.00 4.00

SCOPE 2.46 1.86 1-7 1 7

FUNC 0.57 - 0-1 0 1

PROD 0.13 - 0-1 0 1

LEVEL 0.53 - 0-1 0 1

SIZE 3.48 1.02 1- 1.70 5.36

Variable definitions: VBa = absolute importance of value-based measures for evaluation; VBr =

importance of value-based measures relative to profit; ASSINT = importance of intensive asset use

(mean score on 3 items); OPDR = delegation of operational decision rights (mean score on 8 items);

STRDR = delegation of strategic decision rights (mean score on 3 items); SCOPE = sum of indicator

variables reflecting value chain activities performed by the unit; FUNC = indicator variable whether or

not the firm has a functional structure; PROD = indicator variable whether or not the firm has a

product-oriented structure; LEVEL = respondent level (BU/location vs. headquarters); SIZE = log of

the number of employees working in the firm.


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Table 3: Pearson correlation coefficients (N=123)

VBa VBr ASSINT OPDR STRDR SCOPE FUNC PROD LEVEL

VBa 1.00

VBr 0.84*** 1.00

ASSINT 0.27*** 0.30*** 1.00

OPDR 0.15* 0.15* -0.15* 1.00

STRDR 0.16* 0.09 0.13 0.35*** 1.00

SCOPE 0.27*** 0.22*** 0.01 0.02 -0.02 1.00

FUNC -0.07 -0.03 0.05 0.01 -0.07 0.02 1.00

PROD -0.17* -0.14 -0.14 -0.04 -0.17* -0.15 -0.44*** 1.00

LEVEL 0.06 0.02 -0.09 -0.18** -0.22** 0.19** -0.20** 0.17* 1.00

SIZE 0.28*** 0.25*** 0.07 0.03 0.09 0.07 -0.28*** 0.06 -0.04

***,**,* indicate significance atp


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Table 4: Partial least squares model estimations (N=123)

Model 1 Model 2

Variables OPDR1

STRDR1

VBa VBr

ASSINT -0.19(-1.79)*

0.03(0.46)

0.21(2.42)**

0.24(2.82)***

OPDR

STRDR

0.20

(1.99)**

0.05

(0.68)

0.20

(2.07)**

-0.02

(-0.32)

SCOPE 0.10

(1.18)

-0.00

(-0.01)

0.19

(2.23)**

0.14

(2.07)**

FUNC

PROD

-0.02

(-0.31)

-0.06

(-0.73)

-0.22

(-2.12)**

-0.27

(-3.02)***

-0.05

(-0.73)

-0.15

(-1.81)*

0.01

(0.12)

-0.13

(-1.76)*LEVEL -0.25

(-2.50)**

-0.19

(-2.03)**

0.12

(1.51)

0.07

(1.08)

SIZE 0.02

(0.29)

0.05

(0.75)

0.21

(2.38)**

0.22

(2.62)***

R2

0.15 0.13 0.32 0.32

Model 1 estimates the effects on the absolute importance of value-based measures and Model 2

estimates the effects on the relative importance of value-based measures. The estimates of the

standardized coefficients on OPDR and STRDR in Model 2 are similar as in Model 1 (since PLS uses

bootstrapping to compute t-values, these differ marginally between both models).

Cell Coefficients are the standardized coefficients, and t-values (between brackets). ***, **,* indicatesignificance atp


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Appendix A: Questionnaire items

This appendix details the measurement of the different questionnaire items used in the study.

Importance of value-based measures for managerial performance evaluationHow important is each of the following performance indicators for the evaluation of the managers(s) ofthe primary operating units in your organization(al unit)? (1-not important, 7-very important)

- Value-based measures, such as EVA and CFROI- Profit

Asset use intensityHow important are the following elements for the firm to achieve a competitive advantage over itscompetitors? (1-not important, 7-very important)

- Low inventory levels- Optimal utilization of production capacity (people and machines)- High production speed

Organizational structureWhich of the following organizational structures best fits your organization(al unit)?

- My organization(al unit) has a functional organizational structure; the responsibilities are dividedbased on functions, such as marketing, production, assembly, sales and after sales service

- My organization(al unit) has a product structure: functional departments that work on one product (orproduct group) are united in one unit

- My organization(al unit) has a product-market structure: functional departments are united perproduct-market combination

Delegation of decision rightsWhat is the lowest management level that has the authority to take the following decisions in your

organization? (1-team

determinants of the use of vb measures

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