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BEHAVIOURAL CONSEQUENCES OF CHANGES TO PERFORMANCE

BASED COMPENSATION SYSTEMS

By

Mandy M Cheng

The University of New South Wales

Susan M. Robertson

RMIT

Axel K-D Schulz*

University of Melbourne

* Corresponding Author Axel Schulz Department of Accounting and Business Information Systems The University of Melbourne Victoria 3010, Australia Ph +61 3 83447665 Fax +61 2 93492397

Email axels@unimelb.edu.au

(Draft – Please do not quote without permission of the authors)

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BEHAVIOURAL CONSEQUENCES OF CHANGES TO PERFORMANCE

BASED COMPENSATION SYSTEMS

ABSTRACT

This study investigates how changes in performance based compensation systems

(PBCS) affect managerial effort allocation and commitment to the system. In

particular we empirically examined how the introduction of a new measure and the

associated change in bonus weighting affected the individual’s effort choice.

Consistent with our prior expectations and prior literature we found changes in bonus

weights to have the desired effects on changes in effort allocation. However, also

consistent with our expectations but not considered in prior literature, we found

dysfunctional consequences of the change in terms of both effort allocation and

managerial commitment to the system. Our results show that, while necessary,

changes to the PBCS may not be costless.

Our results have direct consequences to designers of PBCSs as they need to be aware

of potential negative consequences of changes made to the system.

Keywords: Performance Based Compensation Systems, Compensation Weights,

Expectancy Theory

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I. Introduction

One of the primary roles performance-based compensation systems (PBCS) fulfil is to

persuade individuals to pursue the strategic goals and priorities of their organisation.

A fundamental underpinning of research into PBCS design is the proposition that

these systems must be appropriately aligned with organisational structure, strategic

priorities and business processes if an organisation is to successfully achieve its

strategic goals (Abernethy, 2001; Miles and Snow, 1992; Fisher, 1998). Within both

the practitioner and academic literature, articles attempting to identify attributes of

PBCSs that represent the best “fit” for different organisational environments abound

(For instance, see Abernethy and Lillis, 2001; Chapman, 1998; McAdam and Braillie,

2002).

In recent times, organisational environments have become progressively complex and

dynamic. Global competition, significant technological advancements, a greater

emphasis on strategic priorities such as time, quality, and customer service and

knowledge acquisition have created innumerable competitive opportunities, which in

turn have led to substantial change, and in particular strategic change for many

organisations.

Not surprisingly, researchers and practitioners are continually encouraging

organisations to constantly improve, adapt or modify their PBCSs (for instance, see

Bih-Ru and Fredendall, 2002; McAdam and Braillie, 2002; Cooke, 2002; Frigo, 2002

a & b; Stivers and Joyce, 2000; Bourne et al, 2000; O’Mara et al, 1998; Russell, 1997;

Feurer and Chaharbaghi, 1995; Neely et al, 1994) to ensure that these systems keep

pace with their increasingly complex and dynamic organisational environments.

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Innumerable articles have been written both in practitioner and academic journals,

which provide anything from simple guidelines (e.g. Anderson and Fagerhaug, 2002;

Bourne et al , 2000; Upton, 1998; Azzone and Noci, 1998; Neely et al, 1995; Sinclair

and Zairi, 1995; Freurer and Chaharbaghi, 1995; Vitale et al, 1994) to more elaborate

models (e.g. Medori and Steeple, 2000; Kaplan and Norton, 1992, 1996 and 2001) for

choosing appropriate performance measures in different organisational settings. The

development of the Balanced Scorecard (Norton and Kaplan, 1992, 1996) is one of

the most well known of these models.

Evidence indicates that organisations are taking on board the recommendations of

both practitioners and researchers to continually modify their PBCSs. Findings from

the most recently published Performance Measurement Survey (Frigo 2001)

conducted by the Cost Management Group of the IMA reveal that 80% of respondents

reported that their businesses made changes to their performance-based compensation

systems during the last three years. 33% indicated that these changes represented a

“major overhaul” or “new performance-based compensation system”. Furthermore,

50% of respondents indicated that their organisations were currently in the process of

changing their PBCSs. Findings also indicated that 40% of respondents used or were

intending to use the Balanced Scorecard within the next year. In addition, users of the

Balanced Scorecard tended to have a much greater mix of performance measures, and

66% of respondents agreed that the Balanced Scorecard helped identify new

measures. These findings together provide clear indications that performance-based

compensation system design is a dynamic, shifting process.

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Very little research however has been undertaken on the behavioural outcomes of

changing PBCSs. There appears to be an assumption that, if changes made to a PBCS

result in that system becoming more appropriately aligned with strategic priorities and

goals, behaviour will also change appropriately. It is almost taken for granted that

individuals will be better able to understand the behaviours required to achieve their

organisation’s strategic goals, and will be motivated to do so. There will be no

residual effects of the old system, nor will there be any negative effects arising from

change itself. The purpose of this study is to examine the impact of changing a PBCS

on a particularly important type of behaviour – individual effort allocation decisions.

The study examines firstly whether individuals do redirect their effort in ways that are

consistent with the changes to the PBCS. Secondly, the study explores the potential

for dysfunctional behavioural resulting from changing PBCSs.

Changes to PBCSs can come in many forms. Within the context of this study, two

types of modifications will be emphasised – changes to the types of performance

measures included within the PBCS, and changes to the weightings placed on those

measures when they are used for compensation determination.

In particular this study investigates:

1. The link between the introduction of a new performance measure and an

individual’s effort allocation decision.

2. The link between the introduction of new weightings to performance measures

and an individual’s effort allocation decision.

3. The impact on effort allocation decision of removing weightings from existing

performance measures.

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4. The impact of PBCS change in general on overall effort allocation.

5. The impact of PBCS change in general on commitment to the PBCS.

The answers to these questions contribute to our knowledge of the behavioural

influences implicit in PBCS design. Given the need for firms to continually update

and modify their PBCSs, and the importance of this task to the successful

implementation of strategy, it is critical that first, change in PBCSs does lead to

change in behaviour, and second, unintended and unwanted behaviours are identified

and take into consideration.

II. Theoretical Framework

The role of performance-based compensation systems

Most PBCSs comprise three elements 1) performance measures, 2) a target level of

performance attached to each performance measure and 3) a weighting used to

determine the amount of compensation received for a given target level of

performance. The role of PBCSs is well established in the literature. Two related

theoretical perspectives – goal-setting theory and expectancy theory – provide a well-

documented framework in which to outline the role of performance-based

compensation systems. Goal-setting theory suggests that performance measures and

targets that represent goals that are specific, challenging but achievable provide a

powerful mechanism by which organisations can provide information to direct

managers towards the types of work behaviours and outcomes that lead to strategy

achievement (Locke and Latham 1990). Managers are expected to use the

information contained in the measures on which their performance is based as a

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means for setting their own work goals and ultimately and most importantly effort

allocation choices. Vroom, (1964) was the first to establish an explicit expectancy

theory model. Numerous models representing Vroom's theory have been developed

and used within many research disciplines (see for instance Campbell and Pritchard,

1976; Ronen and Livingstone, 1975; Harrell and Stahl, 1986), however the underlying

propositions of all the models remain the same. Expectancy theory argues that an

individual’s motivation to exert effort towards a particular activity or task will be

determined by the multiplicative relationship of three factors – expectancy,

instrumentality and valence. Expectancy refers to the degree to which an individual

perceives that their effort will lead to valued performance. Instrumentality relates to

the extent to which an individual believes that particular outcomes1 are tied to

achievement of valued performance. Finally, valence refers to the degree to which an

individual desires those outcomes2. Within the expectancy theory framework, it can

be argued that the use of performance measures and targets3 contained in the PBCS

provide individuals with an understanding of the relationship between their effort

choices and valued performance, hence expectancy and instrumentality is enhanced.

The attachment of rewards to targeted performance, through the use of compensation

weightings, enhances instrumentality and valence. Thus the use of PBCSs is able to

influence an individual’s motivation to exert effort on a particular activity and as a

result their effort allocation decisions.

1 These outcomes have also been categorized as either first or second level outcomes (Galbraith and Cummings 1969, House, 1974). First level outcomes tend to be represented by performance itself – e.g. goal accomplishment. Second level outcomes are those that are expected to arise from first level outcomes – e.g. pay or promotion. 2 The literature also identifies two sources of valence – extrinsic and intrinsic valence. Sources of extrinsic valence are those outcomes that are formally mediated by the organisation, such as pay and promotion. Sources of intrinsic valence are those outcomes that come from the individual – such as self-esteem, accomplishment or self-fulfillment. 3 In keeping with goal setting theory it is assumed that these performance measures and targets would be specific, challenging but achievable.

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Changing the Performance-Based Compensation System

When organisations choose to change their PBCSs, often in response to changes in

strategic priorities or goals, designers of PBCSs use the three elements of the system

to redirect and re-align individual effort to those activities that are most likely to result

in strategy achievement. To use a simple example, if product quality has become the

new strategic priority within an organisation, introducing a new measure, such as a

defect rate, sends signals to individuals that exerting effort on activities reflected by

that measure will lead to performance that is valued. Within the context of

expectancy theory, individuals will be provided with information that allows them to

better understand the effort allocation required to achieve desired performance.

Hence expectancy is enhanced, and individuals will be motivated to exert effort

towards those activities. Conversely removing a measure from the PBCS would send

signals that exerting effort on activities reflected by that measure, instead of those

measures still within the system, will not lead to performance that is as valued. As a

result expectancy would decrease. Decreasing or increasing the targeted level of

performance will also alter expectancy, as required performance becomes more or less

difficult to achieve, and thus more or less likely to be achieved.

Altering weightings attached to targeted performance allows designers of PBCSs to

change the valence or “attractiveness” of outcomes arising from a particular type and

level of effort expenditure (Mento, Cartledge and Locke, 1980; Mowen, Middlemist,

and Luther, 1981). For instance, if a particular performance measure target was

initially given a 60% weighting in the determination of compensation4 and this was

then changed to 20%, effort exerted on activities required to reach that target would

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have less valence, and thus an individual would have less motivation to exert that

level of effort. For example, in an experimental study involving a loan-processing

task, Edminster and Locke (1987) found significant correlations between goal weights

set by subjects and their subsequent performance on three of their five performance

measures. While Edminster and Locke (1987) did not directly measure subjects’ effort

allocation, their result suggests that weightings in PBCSs is an important

consideration when subjects are striving towards achieving their performance target.

The framework outlined above provides arguments to suggest that when organisations

need to modify their PBCSs to reflect changes in strategic priorities and goals,

designers of these systems will reflect those changes through the removal or inclusion

of performance measures, adjustment of the targeted level of performance or

alterations in the weightings attached to measures. Most importantly, in light of the

aims of this study, individuals will alter the effort choices consistent with the changes

made to the system. These arguments form the basis for the development of a set of

hypotheses that test the relationship between a number of common changes in the

elements of PBCSs and an individual’s effort allocation decision. These changes

include 1) the inclusion of a new measure without a compensation weighting attached,

2) the inclusion of a new measure with a compensation weighing attached and 3) the

removal of a compensation weighting from an existing measure. These hypotheses

are presented in the following section.

4 This would mean that 60% of total compensation will be determined by reference to performance on this particular measure.

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III. Hypothesis Development

This study investigates the impact of a change in the PBCS on individual behaviour.

Specifically we are interested to see whether the introduction and shifting of a

performance measure weight redirects individual effort and whether there are

dysfunctional consequences resulting from this change. Although these changes are

not strictly independent, we examine their effects separately.

Inclusion of a new measure

One of the most obvious changes that could be made to a PBCS would be the

inclusion of a new measure. Consistent with the discussion of the previous section,

the inclusion of a new performance measure and target5 would signal to individuals

that exerting effort on the activities underlying that measure would be likely to lead to

valued performance; hence expectancy that effort would lead to desired performance

would be strong. It would be expected that valence would also be positive, as

individuals would find the achievement of desired performance itself (a level 1

outcome) attractive. These two effects together would lead individuals to allocate

effort to activities underlying the new measure. Stated more formally,

H1: Individual effort allocated to activities underlying a new measure of

performance will be greater than zero.

5 The situation of a measure being introduced without a target of performance attached is not considered in this study as very few performance measures would be of this nature.

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Inclusion of a new measure with compensation weightings attached

If compensation weightings were also attached to the new performance measure, it

would be expected that effort allocation to underlying activities would be greater than

if no weighting was attached to that measure. In this situation effort could lead to

both first and second level outcomes (performance achievement and compensation

respectively). Hence instrumentality and total valence would increase, and

individuals would choose to allocate relatively more effort to activities underlying the

new measure. These arguments lead to the following hypothesis.

H2: Individual effort allocated to activities underlying a new measure of

performance will be greater for measures linked to compensation weightings

than for measures that are not linked to compensation weightings

Removal of compensation weightings

It is also likely that a particular type of performance may continue to be measured, but

the link to compensation determination is removed. The discussion contained within

the preceding section suggests that if designers of PBCSs remove the compensation

weightings attached to existing performance measures, effort allocated to the activities

underlying that measure should decline. Note that effort allocation will not

necessarily decline to zero, as there could be some remaining valence still associated

with achievement of performance on those activities that have been valued in the past.

The preceding discussion leads to the following hypothesis.

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H3: Individual effort allocated to activities underlying a particular measure of

performance will be lower when compensation weightings are removed then

when they remain attached to the measure.

The impact of change itself

While the preceding discussion addresses issues of individual changes to PBCSs, it is

possible that change in general may have behavioural implications for the effort

allocation decisions of individuals. When performance measurement systems are

consistent and stable over time, individuals are able to form a more complete

understanding of outcomes associated with their particular effort allocation choices

and consequently the type and amount of effort required to achieve desired

performance and subsequent outcomes is better understood. Conversely, when

change occurs in an individual’s PBCS links between effort choices and outcomes

become less easily understood and more uncertain. Hence a stable PBCS is likely to

be associated with higher expectancy that effort will lead to valued performance than

would a PBCS that has new elements. The implication of this is that when PBCSs

change, an individual’s motivation to exert effort would fall and thus the overall effort

they allocate to activities underlying performance measures would also decline.

Stated formally,

H4: Total individual effort allocated to activities represented by the PBCS will be

lower when elements in that system change.

It is further hypothesised that, when PBCSs exhibit change, the increased level of

uncertainty and subsequent fall in expectancy is likely to lead to reduced commitment

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to the PBCS. Hollenbeck and Klein (1987) define goal commitment as the

determination to try for a goal continually over time. Commitment therefore reflects a

willingness to exert effort over the long-term. Hollenbeck and Klein argue that strong

goal commitment requires a high level of expectancy that effort will lead to goal

attainment. When expectancy is low, goals seem less attainable and thus the

willingness to continue to try for the goal will be low. Consistent with Hollenbeck and

Klein’s arguments, it is anticipated that when expectancy that effort will lead to

valued performance falls, commitment to the performance measures underlying the

PBCS will also decline. These arguments are represented by the following

hypothesis.

H5: Total individual commitment to the PBCS will be lower when elements in that

system change.

III. Research method

Overview of design

To test the proposed hypotheses we conducted an experiment using a 2 x (2) between

repeated design. The independent variable was the stability of the performance

measurement system (Stable Performance Weights – SPW or Changing Performance

Weights – CPW). The repeated measure represented the configuration of the

performance measurement system in the first period (where both treatment groups

received the same measures) and the second period (where treatment groups received

different weights). Subjects were randomly allocated to the treatment groups.

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Subjects

A total of 46 subjects took part in the experiment. The subjects were completing a

part-time Master of Accounting degree at a major university. There were 24 males

and 22 females and their average age was 25 years (range 21 – 31). The subjects had

on average 2.6 years of work-experience.

As subjects were volunteers, we offered them a $25 incentive for taking part in the

experiment. The incentive was not tied to any aspect of task of task performance.

Experimental task

The experimental task consisted of three parts, experimental period one, period two

and manipulation test. Subjects assumed the role of a business unit manager of a large

international company, and were randomly assigned to one of the two experimental

groups (SPW or CPW). In part one all subjects were told that, prior to 2002, their

PBCS comprised one financial performance measure (measure F) and three non-

financial measures (NFA, NFB and NFC), which were linked equally to their annual

bonus.

In part two, a change in the PBCS initiated by the head office, however, saw the

introduction of a further non-financial measure (NFD) in 2003. In 2003 the

performance weights depended on the treatment groups, with SPW receiving the same

weights as in 2002 and CPW receiving equal annual bonus weighting for NFB, NFC,

NFD and F. The 2003 period also saw the introduction of the managers “pet” project,

which was not part of the formal PBCS.

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Subjects were then asked to allocate up to 1,000 units of their effort between the four

performance measures in part one and between the five performance measures in

part two. The experiment was set up so that 1,000 units of effort was sufficient to

meet all measured aspects of the task and thus permit subsequent analysis of effort

allocation on each measure independently of all other measures.

To establish a clear linkage between effort and performance, subjects were told that,

based on their pervious year’s performance, there was a high level of certainty that

every 1 unit of effort allocated to these areas would result in 0.5 unit of output, with

the exception of the new measurement which had a higher level of uncertainty as no

previous year performance was available. Further, as previous literature has

suggested that different levels of difficulty associated with multiple performance

targets may affect individuals’ effort allocation among multiple performance targets

(e.g. Yearta, Maitlis and Briner 1995; Gilliland and Landis 1992), the same level of

performance target (100 output units) was set for each of the performance areas. In

addition to their effort allocation decisions, subjects were also asked to justify their

decision by writing a short comment to explain why they chose to allocate the amount

of effort units for each performance area.

The “pet” project was created to provide another avenue for managers to allocate

effort if they chose not to allocate it to one of the five measured aspects. As such it

provides a cost to the effort allocated by each subjects in the absence of a physical

necessity to exert effort.

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Administrative procedures

Subjects received part one of the task upon commencement of the experiment. Once

subjects signalled to the researcher that they had completed the first effort allocation

in part one, they were given part two of the experimental task, which involved the

second effort allocation decision for subsequent year. Once subjects have completed

part two, the experimental instrument was collected and a post-test questionnaire (part

three), which contained a set of manipulation check and demographic type questions,

were administered, after which the subjects were thanked and incentive payment

distributed. The entire experimental session lasted on average 30 minutes.

Dependent variables

The overall dependent variable was the total amount of effort allocated to measured

aspects of the PBCS. In addition, we measured effort allocation on each of the

performance measures separately. Due to the repeated nature of the experiment, each

dependent variable was measured twice, once in part one again in part two of the

experiment. Furthermore, we also measured subjects’ commitment to the PBCS by

asking subjects to indicate, on a scale of 1 to 7, the degree to which they were

committed to achieve each of the five performance areas. The theoretical range of

commitment was therefore 5 (minimum commitment) to 35 (maximum commitment).

Independent variables

The overall independent variable was stability versus change in the PBCS. Recall that

both treatment groups received the same performance weights in part one. For part

two subjects in the treatment group receiving the same performance weights (SPW)

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were told that the new performance measure (NFD) was not linked to their annual

bonus. In contrast, for part two subjects in the treatment group receiving the change

in performance weights (CPW) were told that while the new performance measure

(NFD) would now constitute 25% of their annual bonus, the existing performance

measure for Area A (NFA) would no longer be linked to their annual bonus.

Manipulation check

We used a total of three manipulation checks in this study. The first two checks were

conducted in relation to the subjects’ perception of reward weights used for the two

manipulated measures (NFA and NFD). The third check was to assess subjects’

perceptions of the personal opportunity (i.e. the “pet project”). A total of 6 subjects

failed either or both of the first two checks, while none of the subjects failed the third

check. The distribution of the 6 subjects failing the first two checks was almost

consistent across both treatment groups, with 4 subjects from SPW and 2 subjects

from the CPW treatment groups. While we report our tests in this study on the

complete data set, we also conducted all tests with these 6 subjects omitted from the

analysis. None of the results reported in this study changed as a result of omitting

these 6 subjects.

Finally, analyses were conducted on the demographic data collected in the post-test

questionnaire. No significant differences were found in the distribution of age,

gender, study program or work experience across the two treatment groups.

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IV. Results

Descriptive statistics

The descriptive statistics reported in Table 1 (Panel A) show a similar distribution of

effort across the four performances measures for both treatment groups during

period 1. In contrast Panel B (period 2) shows that effort allocated for NFA is lower

for the CPW groups than for the SPW groups, while the reverse is the case for NFB.

None of the performance measures received an effort allocation of close to zero. And

finally, overall effort allocated across all measures in the SPW groups (841.0426) is

higher than for the CPW groups (775.0767) for period 2.

Insert Table 1 – Panel A & B about here

Hypotheses testing

In hypothesis 1 we predicted that individual effort allocated to activities underlying a

new measure of performance will be greater than zero. To test this hypothesis we

analysed NFD (period 2) of the SPW treatment groups. Recall that for period 2, the

measurement of NFD was not linked to the reward system. Results show that the

SPW group allocated significantly more effort units (120.625) than zero (t=7.933,

p=0.000 – refer Table 2). As such we strongly supported this hypothesis.

Insert Table 2 about here

6 175.625 + 196.250 + 164.792 + 120.625 + 183.750 = 841.042 – From Table 1 Panel B 7 66.667 + 169.091 + 162.727 + 185.227 + 191.364 = 775.076 – From Table 1 Panel B

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For hypothesis 2 we predicted that individual effort allocated to activities underlying a

new measure of performance will be greater for measures linked to compensation

weightings than for measures that are not linked to compensation weightings. We

analysed NFD (period 2) to test this hypothesis. As discussed previously this measure

was newly introduced in period 2 to both treatment groups. The SPW received the

measure without a link to their reward system, while CPW not only received the

measure but also were told that the measure was tied to 25% of their bonus. The

difference in effort allocated by SPW compared to CPW represents the change

attributed to the introduction of the reward system link. The former allocated

significantly less effort units to the measure than the latter (SPW=120.625 vs.

CPW=185.227, t=3.241 p=0.002 – refer to Table 1 Panel B and Table 3). Hence

hypothesis 2 is supported.

Insert Table 3 about here

In hypothesis 3 we stipulated that individual effort allocated to activities underlying a

particular measure of performance will be lower when compensation weightings are

removed then when they remain attached to the measure. Table 1 (Panel A & B)

contains the descriptive statistics related to the amount of effort exerted by subjects in

terms of NFA. Recall that in period 2, the stable performance weight (SPW)

treatment group continued to receive a 25% weighting while the change performance

weight (CPW) treatment group saw the weighting drop to 0%. While both treatment

groups assigned approximately the same amount of effort in the first period (251.50

for SPW and 232.05 for CPW), in the second period the CPW treatment group

significantly reduced the amount of effort allocated to the task dimension measured

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by NFA relative to the SPW treatment group (66.67 for CPW and 175.62 for SPW,

F=17.607, p=0.000 – refer Table 4). We thus support hypothesis 3.

Insert Table 4 about here

For hypothesis 4 we predicted that total individual effort allocated to activities

represented by the PBCS will be lower when elements in that system change. To test

this hypothesis we use the total amount of effort allocated to measured aspects of the

task. Recall that managers had the opportunity in period 2 to allocate effort not only

to measured aspects of the task but also unmeasured aspects, which were described as

their “pet project”. Results reported in Table 5 show that managers receiving stable

performance measures (in terms of their link to the reward system) are significantly

more likely to exert effort than managers receiving performance measures which are

changing in respect to their link with the reward system (SPW = 841.04 vs. CPW =

780.24, t=2.103, p=0.041). We thus support hypothesis 4

Insert Table 5 about here

And finally for hypothesis 5 we proposed that total individual commitment to the

PBCS would be lower when elements in that system change. To test hypothesis 5 we

examined the total amount of commitment reported by managers on all measures

(refer Table 6). The results show a significantly greater amount of commitment

reported by managers in the SPW treatment group (25.087) compared to the CPW

group (20.048, t=2.554, p=0.014). We thus support hypothesis 5.

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Insert Table 6 about here

IV. Discussions and conclusions

Discussion of results

Results found in this study continue to support the mainstream accounting literature

both in terms of “what gets measured gets done” as well as in terms of “what gets

rewarded gets done”. Our findings show that managers are more likely to withhold

effort where incentive system weights are changed, which has not been traditionally

considered by designers of PBCSs. As any change in the performance measurement

system is likely to be accompanied by changes to the compensation system, it might

be necessary to consider potential costs associated with the potential consequence of

managers withholding effort.

In particular and unlike prior literature on PBCSs, we have considered the effect of

measurement and rewards separately. Our findings suggest that the introduction of a

new measure, even in the absence of explicit linkage to the compensation system,

have the effect of increasing managers’ effort allocation towards the associated

performance area. Conversely, the removal of a performance measure from the

compensation scheme does not necessarily result in managers reducing their relevant

effort allocation to zero. Rather, our results show that there may be some “residual

effect” whereby managers continue to allocate effort despite the lack of explicit

linkage between effort exertion and compensation. This residual effect is particularly

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of concern to designers of PBCSs who are considering using compensation weighting

as a means of communicating strategic priorities to managers.

Furthermore, our results also show that managerial commitment to their PBCSs is

affected by changes in the reward system weights. Consistent with our expectations,

changes in the reward system weighting were accompanied by a general decline in

commitment reported by managers as they face changes in their PBCSs. This implies

that designers of PBCSs must carefully balance their desire to continuously improve

their reward system, and the potentially undesirable impact on managers’

commitment.

Research contributions

This study makes a fundamental contribution to our understanding concerning the

impact of introducing change to PBCSs on individual’s effort allocation decisions, by

providing empirical support for the proposition that changes in the PBCS is not

costless in motivational terms. As such, it adds anther dimension to aspects control

system designers have to consider in their quests for a better control system.

From the practitioner’s perspective, designers of PBCSs must consider the impact of

continuous changes on managers’ commitment and total effort exerted. For example,

our results suggest that modifications of PCBS must be accompanies by other

initiatives that can maintain or improve managers’ commitment to the new system.

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Furthermore, minor adjustment to PCBS in the name of continuos improvement may

not always be appropriate, as the cost may outweigh the benefit for such changes.

From the perspective of researchers our study extends the expectancy theory to

incorporate multiple performance measures. Our result confirms that, given multiple

measures, the role of valence and instrumentality affects both the amount of effort

individuals allocate to individual performance areas (the direction of effort allocation)

as well as the total amount of effort managers choose to exert on behalf of the

organisation.

Limitations and future research directions

Several limitations to this study should also be noted. As this study is based on a

laboratory experiment, the task represents a simplified PBCS. As such, the task did

not include all the information potentially considered by managers in their effort

allocation. This study also only considered an externally imposed PBCS. Future

research could investigate how negotiation around both the types of measures and the

targets set for those measures influence managerial acceptance and effort allocation.

Further, as our study demonstrates that a “residual effect” may exist whereby

managers continue to allocate effort towards performance areas that are no longer tied

to their compensation scheme, future research could also examine more closely the

factors that affect managers’ decision to exert or withhold effort on performance areas

they have been excluded from their PBCS. Finally, future research could also

investigate the interaction between goal setting theory and expectancy theory, for

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example, by considering the interaction effect of goal weights, goal difficulty, effort

allocation and performance.

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Table 1 Panel A – Effort allocation (units) for Period 1 SPW Group

NFA(1) NFB(1) NFC(1) F(1) N of cases 24 24 24 24 Minimum 150.000 200.000 150.000 100.000 Maximum 400.000 300.000 300.000 400.000

Mean 251.500 252.917 223.583 236.667 Standard Dev 59.433 42.064 41.936 75.393

CPW Group

NFA(1) NFB(1) NFC(1) F(1) N of cases 22 22 22 22 Minimum 100.000 100.000 100.000 100.000 Maximum 310.000 300.000 300.000 500.000

Mean 232.045 227.500 233.182 263.409 Standard Dev 67.905 49.946 51.375 96.774

Table 1 Panel B – Effort allocation (units) for Period 2 SPW Group

NFA(2) NFB(2) NFC(2) NFD(2) F(2)N of cases 24 24 24 24 24Minimum 100.000 100.000 100.000 0.000 0.000Maximum 275.000 300.000 250.000 250.000 300.000

Mean 175.625 196.250 164.792 120.625 183.750Standard Dev 51.146 46.514 47.947 74.488 63.507

CPW Group

NFA(2) NFB(2) NFC(2) NFD(2) F(2)N of cases 21 22 22 22 22Minimum 0.000 100.000 100.000 100.000 100.000Maximum 200.000 240.000 215.000 300.000 500.000

Mean 66.667 169.091 162.727 185.227 191.364Standard Dev 68.160 45.556 44.070 58.969 78.212

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Table 2 – Effort allocation for NFD – Period 2 (SPW) Effort allocation (units) Standard deviation 74.488t-statistic (Probability) 7.933 (p=0.000)Confidence interval (95%) 89.171-152.079Degree of freedom 23

Table 3 – Effort allocation for NFD – Period 2 (SPW vs CPW)

Mean Standard deviation t-statistics

SPW 24 120.625 74.488 3.241 0.002

CPW 22 185.227 58.969

Table 4 – Effort allocation for NFA – ANOVA results

Between Subjects

SS df MS F p Group 93577.002 1 93577.002 18.016 0.000Error 223340.598 43 5193.967

Within Subjects

SS df MS F p

NFA 323632.891 1 323632.891 129.479 0.000NFA * GP 44008.224 1 44008.224 17.607 0.000

Error 107478.932 43 2499.510

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Table 5 – Total effort assigned to all performance measures (units)

Group N Mean SD t-statistic p SPW 24 841.042 94.593 2.103 0.041CPW 21 780.238 99.228

Table 6 – Total Commitment

Group N Mean* SD t-statistic p SPW 23 25.087 7.292 2.554 0.014 CPW 21 20.048 5.590

*Theoretical range = 5-25 (sum of five 7-point questions on subjects’ commitment to

each performance measure), the higher the number, the stronger the commitment.