Accounting Innovation

1. ABACUS, Vol. 41, No. 3, 2005
217
Blackwell Publishing, Ltd.
Oxford, UK
ABA
Abacus
0001-3072
© 2005 Accounting Foundation, Unviersity of Sydney
41
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ORIGINAL ARTICLE
DETERMINANTS OF ACCOUNTING INNOVATION
ABACUS
MARGARET A. ABERNETHY AND JAN BOUWENS
Determinants of Accounting Innovation
Implementation
Accounting innovations are often not successfully implemented or
diffused throughout the organization. This study seeks to explain this
phenomenon. One of the major impediments to the successful imple-
mentation of accounting innovation is that management accounting
systems are generally used to serve the decision control needs of top
management while at the same time purportedly supporting the decision
management needs of lower level managers. To the extent that the
accounting system is used for decision control, innovation creates the
potential for wealth effects to occur. This prompts managers, whose
wealth will be negatively affected, to resist accounting innovation. We
present conditions where it is likely for negative wealth effects to occur.
Under these conditions the system will fail to achieve its intended
objectives. Our theoretical model examines how decentralization choices
influence resistance to accounting innovation. We argue that delegation
of decision rights can limit the potential for resistance in two ways—(a)
by creating the environment which allows managers to ensure that their
subunits are able to adapt to the new signals provided by accounting
innovations and (b) by enabling subunit managers to become involved
in the design of these systems. Our model also enables us to assess the
consequences on organizational outcomes when subunit managers resist
accounting innovations. Based on data collected from production
managers, our results demonstrate the importance of decentralization
choices on the effective implementation of accounting innovations.
Key words: Adaptability; Change; Decentralization; Innovation; Manage-
ment accounting systems.
Top managements implement new management accounting systems (MAS) as
they believe these systems will improve the performance of the firm. They are
often wrong. There is increasing evidence that management accounting innova-
tions do not improve either decision making or firm performance (Kaplan, 1986;
Bruns, 1987; Innes and Mitchell, 1991; Cooper et al., 1992; Scapens and Roberts,
Margaret A. Abernethy (dean.ecomfac@unimelb.edu.au) is the Dean of the Faculty Economics and
Commerce, and Professor in the Department of Accounting and Business Information Systems, The
University of Melbourne. Jan Bouwens is Professor of Accounting at Tilburg University and research
fellow at CentER and The University of Melbourne.
The authors wish to acknowledge the constructive comments provided by Sue Haka, Jennifer Grafton,
Laurence van Lent, Anne Lillis and participants at The University of Melbourne Accounting Research
Seminar Series. We thank Jennifer Grafton and Phua Yee for their research assistance. Financial assist-
ance for this project was provided by the Australian Research Council Grant and by NIVRA-Nyenrode.

2. ABACUS
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1993; Scapens and Burns, 2000; Abernethy and Lillis, 2001; Cavalluzzo and Ittner,
2004). There have been attempts to document the reasons for these failures, but
much of this research focuses on the organizational level problems associated
with the implementation of MAS innovations. For example, empirical research
examines how a firm’s strategy, training policies and internal management
structures influence the successful implementation and diffusion of MAS (Shields,
1995; Gosselin, 1997; McGowan and Klammer, 1997; Krumwiede, 1998).
There is, however, little research directed towards understanding the attitudes
of the users to MAS innovation and the factors influencing those attitudes. Jermias
(2001) used cognitive dissonance theory to investigate managerial resistance to
accounting systems. Cavallazzo and Ittner (2004) were also interested in the
factors that influence acceptance of performance measurement systems. We build
on this research and examine how organizational design choices influence produc-
tion managers’ attitude to MAS innovations. We assess how decentralization
choices influence production managers’ acceptance of accounting innovation. We
expect the delegation of decision rights to be the primary determinant of produc-
tion managers’ acceptance of these innovations. When lower level managers are
delegated decision rights, MAS can be used to measure if managers are using
decision rights optimally (Vancil, 1979, p. 88). These managers, therefore, will be
concerned when new systems are implemented as there is a potential for these
systems to directly influence how their performance is evaluated and rewarded.
Two conditions mitigate the potential adverse consequences of decentralization
on MAS innovations. One is when decentralization enables production managers
to have greater input into the design of the MAS. A second is when decentraliza-
tion empowers the subunit manager to implement the changes that will enable the
subunit to adapt to the new signals provided by the accounting innovation. We
expect that these two conditions will be important mediating variables in the
relation between decentralization and accounting innovation acceptance. Our
predictions are tested using an intervening path model. We also address the ‘so
what?’ question by examining the relation between accounting innovation accept-
ance and two outcome variables. We assess the impact on subunit performance as
well as a more ‘immediate’ outcome variable, namely, level of satisfaction with
the information provided by the new system.
THEORETICAL MODEL
Management accounting systems (MAS) are implemented to serve two functions:
facilitate managerial decision making (i.e., support subunit managers’ decisions)
and control subunit manager’s behaviour by superiors (i.e., support the superior’s
performance evaluation decisions). We are interested in the conditions necessary
to encourage acceptance of MAS innovations by subunit managers (in this study,
production managers). For the purpose of this study, MAS innovations are
conceptualized as either new systems (e.g., an activity based costing system,
balanced scorecard) or the redesign of an existing system (e.g., integrated and
comprehensive performance measurement system, production control system).

3. DETERMINANTS OF ACCOUNTING INNOVATION
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We argue that decentralization choices are an important determinant of
managers’ acceptance of accounting innovations and develop two intervening path
models to explain why this is so. The first path examines the relation between
decentralization, subunit adaptability and acceptance, while the second path
examines the relation between decentralization, user influence and acceptance.
And finally we examine the relation between accounting innovation acceptance
and two outcome variables.
Decentralization, Subunit Adaptability and Accounting Innovation Acceptance
Decentralization provides subunit managers with the authority to take actions that
will influence the adaptability of the subunit. For example, if lower level managers
are granted decision rights over conditions associated with the employment and
management of employees they will be able to create a culture within the subunit
that is conducive to change. If, however, those decision rights are not delegated
then it will be more difficult to bring about such a culture. It is the inability to
bring about change within the subunit that leads to accounting innovation resist-
ance. Changes in the accounting system are likely to reveal new information to both
the superior and the subordinate concerning asset utilization. Superiors often use
the information from the MAS to assess and reward subordinate performance
(Zimmerman, 2003). Subunit managers, on the other hand, are expected to
‘recognize the value of the new information, assimilate it and apply it’ in the
management of their subunit (Cohen and Levinthal, 1990, p. 128). If they are
unable to use the information to improve their performance there are likely to be
commensurate effects on their compensation and/or promotion prospects. The
ability to use new information, or what Cohen and Levinthal (1990) refer to as
absorptive capacity, is influenced by the extent to which the subunit manager is
both able and willing to use the information to make the necessary adaptations
in the subunit. Subunit managers need to be able to redeploy assets (both labour
and capital) in response to this new information. This type of adaptability includes
both the ability of the subunit to obtain investment funds to purchase desired
technology and the willingness of individuals working within the subunit to adjust
their behaviour to changing circumstances (Scott and Bruce, 1994; Tsai, 2001).
Subunit managers’ ability to implement change in response to new information is
limited by the extent to which these two conditions are satisfied (Bolton and
Dewatripont, 1994).
In sum, our model predicts that decentralization will have a positive effect on
subunit adaptability. This adaptability allows the subunit manager to respond to the
signals provided by the MAS efficiently and effectively. This in turn will make him
more receptive to MAS innovation. We thus expect the relation between decen-
tralization and MAS acceptance to operate via the degree of subunit adaptability.
Decentralization, User Influence and Accounting Innovation Acceptance
Decentralization will also influence the extent to which subunit managers are
involved in the design of accounting innovations. When decision making is
centralized, lower level managers are unlikely to have sufficient authority to

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influence the design of the system. It is the influence of the user on the design of the
system that will predict subunit managers’ resistance to accounting innovation.
There are two reasons for this expectation. First, it is well recognized in the
management information system literature that systems should be tailored to
the needs of the user (see Kirs et al., 2001). While there is very little research in the
accounting literature examining the relation between user influence and attitude
to accounting innovation,1
we expect that involvement in the design and imple-
mentation of the MASs will increase the extent to which the system is tailored
to the needs of subunit managers. This will have a payoff, as a well designed MAS
will reduce ex ante uncertainty and improve decision making (Galbraith, 1973;
Bouwens and Abernethy, 2000). Second, influence on MAS design also has the
potential to decrease the measurement error of the system when it is used for
performance assessment. That is, it increases the possibility that more relevant
performance norms will be set which in turn increases the probability that these
norms will actually be met (Hofstede, 1967). Not only does this reduce managers’
uncertainty relating to performance outcomes, it also increases their commitment to
the system (Brownell, 1982; Hartwick and Barki, 1994; McGowan and Klammer,
1997).
In summary, we expect that decentralization will have a positive relation with
the subunit managers’ involvement in the design and implementation of the
accounting innovations and that this involvement will positively affect their
acceptance of MAS innovation.
Consequences of Accounting Innovation Acceptance
Researchers are now giving more attention to the consequences on organiza-
tional outcomes in MAS models of behaviour. We assess if there are positive
consequences when managers accept changes to their MAS. First, we examine
the effect of MAS acceptance on the satisfaction of the user with the information
provided. This can be considered to be a more direct or immediate effect of
acceptance. Second, we examine if acceptance of the system influences overall
subunit performance. Based on prior research (Hunton, 1996; Hunton and Rice,
1997), we expect that if MAS innovations gain acceptance by the user, firm
performance will improve. Hunton (1996), using an experimental method, found
a positive association between participation in the design of information systems
and (a) MIS use, (b) satisfaction with the MIS and (c) output (performance) of
subjects.
We also test for the possibility that the relation between MAS acceptance and
performance is an indirect one, operating via satisfaction. Of particular relevance
to this study is the work of Hunton and Price (1997) where performance effects
occurred via the users’ satisfaction with the management information system.
Similarly, Igbaria and Tan (1997) find that user satisfaction with information
technology has a strong direct effect on performance. We expect a similar effect
1
There has been considerable literature examining how participation in budgeting influences its use
for measuring managerial performance.

5. DETERMINANTS OF ACCOUNTING INNOVATION
221
when managers accept changes in the MAS. The greater managerial acceptance of
MAS innovations, the greater will be their satisfaction with the system. System
satisfaction is likely to lead to performance improvements through the benefits
gained from the information provided to users. To examine if this is the case, we
test whether there is a direct relation between acceptance of MAS innovation
and performance or whether the effect on performance occurs via satisfaction
with the system. The expectation is that MAS acceptance will have a positive
effect on satisfaction and that satisfaction will affect performance.
Control Variables
To avoid omitted variable bias (see Ramanathan, 1988, pp. 180–1), we include
three control variables identified in prior literature as potentially problematic
for our model, namely, subunit size, the length of time the system has been
implemented into the subunit and industry. This limits the probability of drawing
inappropriate conclusions concerning the effect of the main test variables on the
dependent variable.
It is important to control for size, as compensation is often related to the size of
the subunit (Milgrom and Roberts, 1992). As a consequence, the potential effect
of MAS innovation on the wealth of large-unit managers will be much greater
than on small-unit managers. The management and the accounting literatures
also argue that analysis of this type should control for size (Govindarajan, 1988;
Simerly and Li, 2000). Managerial attitude to change is also likely to depend on
how recently the changes have been implemented. We expect that acceptance of
MAS innovations will increase the longer the system has been in place. Jermias
(2001) supports this expectation. He demonstrates that individuals are more
accepting of system innovations when they do not disturb their normal routines.
The longer a system is in place, the less disruptive it is to an individual’s routine.
And finally we control for industry. Some industries involve more complex
operations than others (e.g., Christie et al., 2003). It is possible that more complex
industries require greater change in MAS to meet their information requirements.
This would create a culture where change is the norm, leading to a greater accept-
ance over time of MAS innovations. We control for this possible effect here.
Summary of Theoretical Model
The research question is directed towards understanding the antecedents that lead to
effective implementation of MAS innovations. As we are interested in production
managers’ attitudes to systems once they have been implemented, we examine how
decentralization choices influence the users’ acceptance of accounting innovations.
In particular, we argue that decentralization influences acceptance of accounting
innovations via two intervening variables, subunit adaptability and user influence
on system design. The hypotheses to be tested are as follows: The model is illus-
trated in Figure 1.
H1: The relation between decentralization and acceptance of MAS innovations
is mediated by subunit adaptability.

6. ABACUS
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H2: The relation between decentralization and acceptance of MAS innovations is
mediated by subunit managers’ influence in the design and implementation
of MAS innovations.
H3: There is a positive relation between acceptance of MAS innovations and
both user satisfaction and subunit performance.
H4: The relation between acceptance of MAS innovations and subunit perform-
ance operates via user satisfaction.
METHOD
The Sample
Data were collected from a random sample of production managers of manufac-
turing business units2
located throughout Australia. The sample population was
identified using the Kompass Australia 1998 database and includes a diverse
range of industries. In order to optimize the response rate we followed Dillman’s
(1983) total design method (TDM). Of the 242 firms in the sample, eleven
questionnaires were undeliverable. This left 231 potential respondents. After four
mailings, a total of eighty-three firms responded (36 per cent). Given that this was
a random sample directed at middle level managers, we consider this response
rate to be reasonable. There is considerable debate in the literature over the
2
The term ‘business units’, in this study, refers to both single business unit firms and business units
of large multi-business firms.
Figure 1
RESEARCH MODEL

7. DETERMINANTS OF ACCOUNTING INNOVATION
223
adequacy of response rate for different populations and problems associated with
low response rates. While our response rate was higher than that normally
observed in MAS research (e.g., Moores and Yuen, 2001; Baines and Langfield-
Smith, 2003) and similar to other studies at this level (Baruch, 1999), the important
question is whether our data suffered from response bias. We assess if this is the
case by, first, using the demographic statistics provided by the industry database
to assess if respondents differed from the non-respondents on size, state and
industry grouping. The food and beverage industry was slightly over-represented
in our sample (17.7 in the population, 29.9 in the sample). However, there is no
reason to believe that this will affect the results of our study as the measures are
not industry-specific. Second, we compared the means on the variables of interest
between the early respondents and the late respondents, on the assumption that
late respondents shared similar characteristics to non-respondents. The results of
these tests provide little evidence of response bias in the data.
Where possible, the measurement instruments were developed from previous
research. In some cases it was necessary to adapt existing instruments to suit the
constructs of interest; in others purpose-developed measures were used. To develop
the new measures we first searched the existing literature. Then managers of a
number of manufacturing firms were interviewed to determine the relevant items
for inclusion in the instrument. To overcome potential measurement problems we
also undertook a number of steps to assess the validity of our measures. Where
possible additional data were collected and used to assess the construct validity
of our test variables. Particular attention was paid to the discriminant validity and
reliability of each construct. Each of the measurement instruments was factor
analysed to assess if the multiple items represented a unidimensional scale and
adapted the instrument where necessary. We also factor analysed all of the items for
the test variables. This procedure provides a rigorous assessment of the discriminant
validity of the individual constructs. We then assessed the composite reliability of
each of the constructs with a composite reliability index proposed by Fornell and
Larcker (1981). This index is analogous to Cronbach’s alpha and reflects the inter-
nal consistency of the indicators measuring a given construct. We also computed
variance extracted estimates (again following Fornell and Larcker, 1981). This sta-
tistic measures the amount of variance that is captured by an underlying factor in
relation to the amount of variance due to measurement error. Estimates of 0.50 or
larger are desirable. Based on this test, our constructs have a reasonable level of
discriminant validity. For any two constructs, the square root of the variance esti-
mate should be greater than the simple correlation between these constructs. This
requirement is met for each of the multi-item instruments included in the study.
The factor analysis for all items is included in Appendix A. Table 1 provides
the correlations between the constructs and the details of the psychometric
properties associated with each construct. The correlations among the exoge-
nous variables are not sufficiently high to warrant concerns with multicollinearity
(Griffiths et al., 1993).
The measurement of each construct is discussed in turn. The items included in
each measure are provided in Appendix B.

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Acceptance of Accounting Innovation
There are few studies in the accounting literature that operationalize managers’
acceptance (or rejection) of MAS innovations. Some empirical studies examine
MAS change by studying the stages of ABC implementation (e.g. Anderson, 1995;
Shields, 1995; Krumwiede, 1998). Others identify the number of changes that have
occurred in the accounting system (Libby and Waterhouse, 1996). In this study we
are interested in how production managers respond to changes that have occurred
in the MAS. As this is a different construct from that studied in prior accounting
literature, we searched the general management literature to inform the measure-
ment of this variable. Our search resulted in only one instrument that is conceptu-
ally consistent with our construct. We adapt the eight-item measure developed by
Lau (1990) to capture subunit manager’s attitude towards accounting change. Lau
reports an alpha coefficient of 0.88. The instrument is also validated in Lau and
Woodman (1995).
Factor analysis of the data produces two constructs. One factor includes three
items and represents subordinates’ attitudes to changes that have recently occurred
in the MAS. The second factor captures whether the subordinate is inclined to
initiate future changes. As the first factor clearly captures the construct of interest
in this study, the three items are used in our analysis. Discriminant analysis supports
the validity of the instrument (square root of variance extracted is 0.83). A high
score on this construct reflects a high level of acceptance of the MAS innovation.
Table 1
PEARSON CORRELATIONS BETWEEN LATENT VARIABLES
Composite
reliability
1 2 3 4 5 6 7 8
1. Decentralization of decision rights 0.85 0.68
2. Adaptability 0.73 0.37 0.67
3. Influence MAS design 0.89 0.37 0.18 0.78
4. MAS acceptance 0.87 0.31 0.37 0.32 0.83
5. Performance 0.89 −0.15 −0.23 −0.12 0.10 0.90
6. User satisfaction 0.96 0.10 0.22 0.16 0.26 0.30 0.91
7. Years passed since last change — 0.05 −0.16 0.08 0.13 0.10 0.07 —
8. Size department — 0.04 −0.01 −0.04 0.06 0.02 0.08 −0.07 —
9. Industry dummy — −0.18 −0.05 −0.08 0.21 0.05 0.15 0.23 0.11
Based on 83 observations. Diagonal entries are the square root of the average variance extracted. For
adequate discriminant validity, diagonal entries should be greater than corresponding off-diagonal
entries. Composite reliability is a measure for internal consistency developed by Fornell and Larcker
(1981) similar to Cronbach’s alpha. Correlations (absolute value) above 0.16 are significant at 5%
level.

9. DETERMINANTS OF ACCOUNTING INNOVATION
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Decentralization of Decision Rights
Miller and Droge introduced an eleven-item instrument into the literature in 1986.
Given the items included, we were concerned that the instrument was missing out
on some pivotal roles of production managers. We therefore presented the items
of the Miller and Droge (1986) instrument to production managers in the field and
asked them if the items represent the activities that had been delegated to them,
and if not, to list those items that were lacking. They completed the instrument
with nine additional items. We use these twenty items and ask production managers
to indicate their influence on these decisions. The decisions we included refer to
human resource decisions (e.g., selection of workers, dismissal, etc.) and production
operating decisions (e.g., methods of work, choice of machinery, deployment of staff,
sources for raw materials, etc.). Factor analysis produced four factors. The original
Miller and Droge instrument was represented in each of the four factors. Two factors
related primarily to human resource decisions. As we were not interested in human
resource decisions per se, we removed these items and performed another factor
analysis from which a two-factor structure emerged. We selected the factor with the
highest eigen value (4.1); that is, the one that explained the major portion of the
variance (31 per cent). It mapped most closely to our construct of interest, and rep-
resents what Aghion and Tirole (1997) refer to as the real power managers have over
activities. The six items in the factor also include the range of decisions delegated
to production managers—salary determination, sourcing raw materials, trading off
internal/external customers, making non-budget investments, cost increasing decision.
The instrument is reproduced in Appendix B. Validity of the instrument is tested
using the square root of the variance and the result (0.67) well exceeds the correla-
tion of decentralization with any other variable of our model, confirming the validity
of the instrument (Fornell and Larcker, 1981). We use the original Miller and Droge
(1986) instrument to assess the predictive validity of our instrument. Correlation
between the Miller and Droge instrument and our instrument is 0.70, supporting
the empirical validity of our instrument (Bohrnstedt, 1983). This evidence provides
sufficient confidence to use the six-item decentralization instrument in our model.
Subunit Adaptability
There is no empirical study, of which the authors are aware, that attempts to
measure the adaptability of a subunit. We follow an approach similar to the one
used in developing the decentralization instrument. From the field we identify
eight items that reflect the characteristics of adaptability at the production unit
level. Factor analysis indicates that the eight-item scale is not unidimensional.
One factor represents production departments’ actual adaptability (i.e., the extent
to which they alter production methods, respond to investment proposals, imple-
ment new methods and deploy workers to other departments). The second factor
reflects the potential for the subunit to be adaptable (e.g., sufficient flexible
working methods). We are interested in the first factor because ‘actual adaptability’
is more relevant than ‘the ability to be adaptable’. The square root of variance
extracted amounts to 0.67 and exceeds the correlation of adaptability with every
other variable of our model. These four items are thus included in our model.

10. ABACUS
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Influence on MAS Design
To measure the influence of subordinates on MAS design we use an adapted
version of the Barki and Hartwick (1994) instrument. The six-item instrument
captures the extent of lower level managers’ influence on accounting system
change during initiation, implementation and maintenance of the system. It
captures their involvement in both the design of the system and the provision of
data for the system. We modified the Barki and Hartwick instrument by removing
one item (item 1) to prevent any overlap with our MAS change instrument.3
The square root variance extracted is 0.78. This is well beyond conventional levels
of confidence supporting instrument validity.
Performance
We use a two-item instrument that asks managers whether or not they achieve
(and have achieved last year) the level of performance their supervisor expects
(expected) them to achieve within their subunit. This approach is similar to that
used by Govindarajan and Gupta (1985).4
Asking managers to assess their
performance relative to what their superior expects enables factors influencing
superiors’ expectations to be considered. For example, if market demand were
influenced by an economic downturn, this would influence top management’s
expectations for the subunit. We assessed the validity of the instrument using an
alternative instrument (not reported here). This instrument asked managers
to assess their performance along eight dimensions (e.g., meeting production
targets, quality requirements). The correlation between the sum of the two item
performance measures used in this study and the sum of the items in this alternative
measure is 0.57 (p < 0.001). Further support for the validity of the instrument was
provided by the square root variance extracted from the two-item measure (0.90),
which is well beyond conventional levels of confidence necessary to support
instrument validity.
User Satisfaction
We adapted an instrument developed by Ives and Olson (1984) to measure user
satisfaction. This instrument asked respondents to indicate their perception of the
accuracy, relevance and quality of the information provided by the system. The
square root variance extracted is 0.91, which supports instrument validity.
3
Our model was analysed with all six items and the results were almost identical.
4
We wanted to make sure that we measured overall performance of the unit. The reason is that the
MAS purportedly helps the manager making decisions in all functions the manager has in the
department. Indeed our theory is built on the idea that differences occur to the extent that the new
system helps managers in different departments. To test the validity of our two-item instrument we
also asked managers to rate their performance in eight performance areas specific to production
departments. We considered the use of the Govindarajan and Gupta (1985) measure but were con-
cerned with the length of the questionnaire and the consequential impact on response rates. The use
of their instrument requires the inclusion of at least sixteen items. In addition, the Govindarajan
and Gupta instrument still does not provide a measure of overall unit performance. Given that we
provide evidence to validate our instrument we felt the two items used here were a reasonable
compromise.

11. DETERMINANTS OF ACCOUNTING INNOVATION
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Control Variables
Size is measured by asking respondents to select one of eight size categories that
represent the number of employees in the business unit (i.e., 1 < 50, 8 > 2000). To
capture the timing of the MAS innovation we ask managers to indicate how
long ago the change in MAS occurred using a four point-scale (0 years, one to
three, three to five and over five years). Industry is measured with a dummy that
separated between ‘simple’ manufacturing (steel production) and more complex
production processes (electronic equipment).
RESULTS
The descriptive statistics for all of the variables are presented in Table 2.
We estimated the model using path analysis. Partial least squares (PLS) is used
to estimate path parameters. The PLS algorithm comprises two steps (Fornell and
Cha, 1994). The first step involves the approximation of latent variable scores.
During the final step parameters are estimated using ordinary least squares (OLS)
techniques. Due to the partial nature of the estimation procedure, using PLS
requires fewer assumptions to be met with respect to multicollinearity, omitted
variables and skewed responses than does OLS (Barclay et al., 1995; Cassel et al.,
1999). The results are reported in Table 3 and illustrated in Figure 2.
As expected, adaptability of the subunit is positively related to the level of
decentralization (coefficient = 0.368, p < 0.00). Our expectation that the ability of
the subunit to adapt in response to the ‘new’ information provided by the account-
ing system will significantly increase managerial acceptance of MAS change is also
supported (coefficient = 0.336, p < 0.00). We find a positive relation between
influence on MAS design and decentralization (coefficient = 0.367, p < 0.00).
Similarly, when subordinates are actively involved in designing and implementing
these systems, acceptance is also significantly increased (coefficient = 0.21, p < 0.00).
There is no significant relation between MAS acceptance and decision making
authority (coefficient = 0.07, p < 0.66) after allowing for the effect of the two
intervening variables.
Our expectations concerning the impact of MAS acceptance on organizational
outcome variables are also supported. We find that MAS acceptance positively
influences the level of satisfaction with the information provided (coefficient =
0.256, p < 0.05). While there is no direct effect between MAS acceptance and
performance, we do observe a positive and significant relation between satisfaction
and performance (coefficient = 0.295, p < 0.01). These results suggest that accept-
ance of MAS innovations positively affects performance via satisfaction with the
system. The only control variable to affect our results is industry, and this is only
significant at the 10 per cent level of confidence. It appears that higher levels of
acceptance exist in more complex industries.
We test the robustness of our model by undertaking three sensitivity tests. First,
we test an unconstrained model (i.e., one that includes links among all of the test
variables). The results (not presented here) provide strong support for our model.
There are no significant paths omitted in our model. This suggests that neither

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Table 2
DESCRIPTIVE STATISTICS OF THE VARIABLES
Minimum Maximum Mean Median Percentile 25 Percentile 75 SD
1. Decentralization of decision rights 11.00 35.00 25.73 26.00 22.00 29.00 4.99
2. Adaptability 9.00 27.00 17.96 17.50 15.00 21.00 3.96
3. Influence MAS design 5.00 25.00 12.81 12.50 10.00 16.00 4.50
4. MAS acceptance 4.00 21.00 12.00 12.00 9.00 14.75 3.92
5. Performance 5.00 14.00 9.24 9.00 8.00 11.00 2.12
6. User satisfaction 5.00 40.00 19.95 19.00 14.08 31.27 6.91
7. Years passed since latest change 0 5 2.26 2.00 1 3 1.03
8. Size 10 1500 182 108 50 180 265.49

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Table 3
PATH ANALYSIS: PLS REGRESSION RESULTS
Path from:
Predicted sign
Path to:
Flexibility
Path to:
Influence on
MAS design
Path to:
MAS acceptance
Path to:
Satisfaction
with MAS
Path to:
Performance
R2
0.1348 R2
0.1358 R2
0.2739 R2
0.0658 R2
0.0903
Decentralization of decision rights +,+,−,NR,NR 0.367*** (3.43) 0.368*** (3.34) 0.069 (0.66)
Adaptability NR,NR,−,NR,NR 0.336*** (2.41)
Influence MAS design NR,NR,−,NR,NR 0.21*** (2.14)
MAS acceptance NR,NR,NR,−,NP 0.256** (1.99) 0.019 (0.12)
User satisfaction NR,NR,NR,NR,+ 0.295*** (2.45)
Control variables
Time passed since last change −0.132 (1.32)
Size department −0.100 (1.03)
Industry dummy −0.177* (1.76)
* p < 0.1, ** p < 0.05, *** p < 0.01 (N = 83).

14. ABACUS
230
adaptability nor influence on the system affects performance per se. Second, we
explore whether managers engage in what Milgrom and Roberts (1992) refer to as
influence costs. This phenomenon occurs when the delegation of decision rights
enables subordinates to ‘manage’ their superior’s expectations concerning subunit
performance. If this is the case, our conclusions relating to the relation between
acceptance of MAS innovations and performance are questionable. Data collected
but not used in this study enable us to test for this. We asked respondents to rate
the importance their superiors placed on eight items of subunit performance.
Respondents were asked to rank their performance on these eight items. We
correlated the importance ranking with the performance ranking for the eight
items and found a statistically significantly positive relation for only one of the
items at less than the 5 per cent significance level. While it is expected that
managers focus on the areas their superior consider important and that this focus
increases the likelihoods of managers achieving performance in those areas (true
in only one of the eight cases), these results provide us with confidence to exclude
the possibility that respondents have successfully ‘managed’ the superior’s
expectations so as to influence performance ratings. The results of this analysis
are not presented here but are available from the authors. And third, we test the
reciprocity between performance-satisfaction and performance-MAS acceptance.
There is concern in any cross-sectional study that the hypothesized relations are
not unidirectional. Researchers tend to rely on theoretical arguments to discount
this possibility. However, we test empirically for this possibility in relation to these
Figure 2
RESULTS MODEL
Note: Coefficients and (t values); *, ** and *** refer to <0.10, 0.05, 0.01 levels of significance.

15. DETERMINANTS OF ACCOUNTING INNOVATION
231
two sets of variables as they are the most likely to exhibit reciprocity. The results of
our analysis5
(not presented here) do not affect the conclusions drawn in this study.
DISCUSSION, FUTURE RESEARCH AND LIMITATIONS
There has been little research examining the factors that influence acceptance of
MAS innovation by lower level managers. We are particularly interested in under-
standing how organizations can implement structures and processes to optimize
the use and diffusion of new technologies. We view this study as exploratory.
Given the limited understanding of the barriers to effective accounting innovation
implementation, we designed a relatively simple empirical model. The findings,
however, do shed some light on how systems can be designed and implemented to
increase the extent to which lower level managers will actually embrace MAS
innovations and use these systems to support decision making. The study makes
several theoretical and methodological contributions to the literature. Furthermore,
5
Our model investigates the extent to which performance is a function of MAS acceptance and user
satisfaction with the MAS. It is possible, however, that satisfaction and MAS acceptance are a
function of performance. This would become problematic to the extent that this would render the
results MAS acceptance-performance and satisfaction-performance insignificant. Hence the alter-
native hypotheses would read as follows: (1) performance is not affected by MAS acceptance and
(2) performance is not affected by user satisfaction. The use of PLS does not enable us to develop
a non-recursive model to test for reciprocity. To perform such a test in PLS it was necessary to
construct a variable that represents a common effect. We used an alternative performance measure
as the test variable. This instrument includes eight dimensions of specific performance, namely, timely
production, material use, labour productivity, cycle time production, cost reduction, variances,
quality and start-up of new processes. We hypothesize that if performance in those specific areas
affects MAS acceptance and/or satisfaction, that the relation satisfaction-performance MAS acceptance
is likely to disappear if this was the ‘driving’ force behind the satisfaction-performance relation. We
also test whether it is performance that leads managers to evaluate MAS change favourably. Thus,
we test the following system of equations:
Performance = f (MAS acceptance, user satisfaction with MAS) (1)
MAS acceptance = f (decentralization, adaptability, influence on MAS design, specific
performance, controls) (2)
User satisfaction = f (MAS acceptance, specific performance) (3)
Equation (1) is equal to our original model. However, we now test equation (1) in a system of three
equations to investigate whether the significance levels in equation (1) are affected by reciprocity
between performance and the other test variable of user satisfaction and MAS acceptance (i.e.,
equations (2) and (3)).
The results of our test are available from the authors. We find that specific performance is weakly
positively related to MAS acceptance (coefficient = 0.153, p < 0.11), suggesting that managers are
more likely to accept MAS change if they perform well. We find a positive and significant relation
between specific performance and satisfaction (coefficient = 0.32, p < 0.01), suggesting that managers
with better performance are more satisfied with the MAS system per se. Most importantly, however, the
relation between satisfaction and specific performance does not affect the satisfaction-performance
relation we presented in our original model. The t value even improves from 1.99 to 2.12 if we
include specific performance in our model. We can thus conclude that all conclusions regarding our
results remain untouched by the tests.

16. ABACUS
232
it provides the basis for the development of more complex models designed to
extend our understanding of the antecedents of MAS resistance.
The findings indicate that choice of internal structural arrangements within the
firm influences the extent to which production managers embrace accounting
system change. It is important, however, to ensure that production managers
have the flexibility to respond to the signals the system provides. These results
confirm prior arguments that individual responses to MAS innovation will be
influenced by how the individual perceives the potential wealth redistribution
effect of the system (Foster and Ward, 1994). Subunit managers who are able to
adapt in response to the information provided by the ‘new’ MAS are more
likely to welcome MAS than if this possibility is absent. This result highlights the
importance of creating conditions where the subunit has the capacity to make
technological change and has a workforce that is able and willing to assimilate and
respond to new information (Cohen and Levinthal, 1990; Tsai, 2001). Only when
these conditions exist can a subunit manager redeploy capital and labour in
response to changing conditions.
Increasing the user’s influence in the design of the system is also an important
mediating variable affecting production managers’ attitudes to accounting change.
This is consistent with the information systems literature that demonstrates that
involving users in the design and implementation of information systems will
significantly influence their use of the system for decision making. Furthermore,
when MAS are implemented to serve both the decision control and decision
management role (Zimmerman, 2003), the evidence presented here indicates that
user influence is an important factor in minimizing resistance to these systems.
When firms decentralize, MAS are used by top management to minimize control
loss. The results demonstrate that one means of overcoming resistance to changes
in MAS that have the potential to affect the wealth and security of lower level
managers is to implement processes that encourage the involvement of these
managers in the design of the system. Failure to do so is likely to lead to consid-
erable resistance, and at worst to sabotage of the system. Our results reinforce
research that documents the dysfunctional consequences that occur when budgeting
systems are used for performance evaluation by superiors without the participation
of the subordinates (see Kren, 1997, for a review of this literature).
The findings presented here demonstrate the importance of considering factors
that influence the operating environment of the user of the MAS. While organiza-
tional factors have been identified as important in explaining why MAS innovations
often fail, these factors are at a different level of analysis. It is, therefore, difficult
theoretically to link a firm’s strategy (Gosselin, 1997) or its human resource
management strategies (Shields, 1995) to lower level managerial behaviour. This
is not to say that organizational level factors are not important in understanding
MAS failures, but rather that they are more likely to influence individual level
behaviour via their impact on the operating context at lower levels.6
Further
6
For example, HR policies, no doubt influence the adaptiveness of individuals at the subunit level.

17. DETERMINANTS OF ACCOUNTING INNOVATION
233
research could be directed towards integrating research at the organizational
level and our study at the individual level into a more complex model of system
failure.
Like most research, our study is subject to potential limitations. The results
represent co-variation among the constructs of interest. Co-variation does not imply
causation and thus it is difficult to make any causal statement concerning the
nature of the relations studied here. This possibly threatens the study’s internal
validity. That is, the variance in the dependent variable may be influenced by an
unobserved independent variable. There is little that can be done in the design of
cross-sectional survey research to overcome problems associated with internal
validity. We must always rely heavily on the theoretical arguments developed
(Cook and Campbell, 1979). We did, however, ensure that the way in which we
operationalized the empirical study was consistent with our theory (Luft and
Shields, 2003). In addition, we attempted through the inclusion of the control
variables to minimize the potential for omitted variable bias to emerge. We also
tested if there were reciprocal relations where there was not theoretical reason to
believe that the relations might not be unidirectional. We did not find this to be
the case. Nevertheless, some caution is required in interpreting the results.
There may be some concern as to whether the study generalizes to other
geographical regions. There is, however, no a priori reason to believe that this
would threaten the external validity of the study. We have no theory that predicts
that culture or regional location affects the relationship under study. What is
important here are that the results can be said to be generalizable to the population.
Unlike most prior survey research in managerial accounting, we ensured that our
sample was random and demonstrated minimal random bias error.
Our study is potentially subject to measurement error. Measurement issues are
always a concern in managerial accounting research, as most of the constructs of
interest cannot be measured using archival data. Researchers are faced with a trade-
off between using a proxy that is limited in terms of its external validity or using
measurement instruments designed to capture the construct of interest (Lanen,
1995). We chose the latter. Where possible we used measurement instruments
from prior literature. Some were adapted from prior literature, others were pur-
pose developed. We did, however, pay careful attention to the assessment of the
reliability and validity of each of the constructs. Further research, of course, is
necessary to provide additional support for the measurement properties of each
of our purpose-developed or adapted measurement instruments
Notwithstanding these potential limitations, this study does add to our under-
standing of the determinants of managerial resistance to MAS innovation. The
findings suggest that when MAS innovations are implemented the behavioural
effects associated with these innovations are likely to influence their success.
Provided that top management ensures that lower level managers are involved in
the design and implementation of the system and that appropriate organizational
mechanisms are in place that provide subunit managers with sufficient flexibility,
resistance will decline. If these factors are considered, it would appear that MAS
innovations have a higher probability of successful implementation.

18. ABACUS
234
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21. D
E
T
E
R
M
I
N
A
N
T
S
O
F
A
C
C
O
U
N
T
I
N
G
I
N
N
O
V
A
T
I
O
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237
APPENDIX A
SURVEY ITEMS AND CORRESPONDING FACTOR LOADINGS FROM THE ROTATED (VARIMAX) FACTOR PATTERN, TOTAL
VARIANCE EXPLAINED 68.43%
Item Factor pattern
I II III IV V VI
MAS acceptance 1: I think the change in systems is excellent .83 −.01 −.06 −.15 −.03 −.17
MAS acceptance 2: I am one of the change initiators .70 −.18 −.10 −.45 .10 .00
MAS acceptance 3: I think the budget unit has done a great job in bringing about the change .81 −.08 −.07 .08 −.05 −.25
Decision authority 1: Salary of employees −.14 .59 .39 .05 −.09 −.05
Decision authority 2: Trade-off between servicing internal or external customers .15 .65 .18 .11 −.140 −.10
Decision authority 3: Order filling acceptance .04 .65 −.00 .08 .10 .02
Decision authority 4: Making non-budgeted investments −.29 .69 −.11 .10 .07 .12
Decision authority 5: Cost increasing decisions −.15 .81 .03 .20 −.07 .07
Decision authority 6: Source of raw materials/supplies −.04 .60 −.30 .25 .42 −.02
Adaptability 1: My employees in can be easily deployed to work on other tasks −.33 .26 .66 −.08 .21 .19
Adaptability 2: My employees are good at initiating and implementing new working methods. −.06 −.23 .53 .33 −.02 −.17
Adaptability 3: I regularly alter production methods in response to changes in demand. −.32 .17 .56 .10 .14 .12
Adaptability 4: I usually get quick responses to my proposals for new investment in production facilities .18 .01 .75 −.07 .05 .09
Influence MAS 1: Design of the system −.20 .08 .02 .73 .04 .25
Influence MAS 2: Implementation of the new system −.16 .03 .14 .83 .03 .131
Influence MAS 3: Provision of data for input into the new system .04 .17 −.17 .71 −.05 −.06

22. A
B
A
C
U
S
238
Influence MAS 4: Ongoing modifications to the new system −.10 .21 .03 .74 .23 .03
Influence MAS 5: Maintenance of the new system .05 .15 .10 .82 −.05 .09
Performance 1: How would you rate the current performance of the production department, compared to your
superior’s expectations?
.11 .05 .15 .01 .88 .114
Performance 2: How would you rate last year’s performance of your department, compared to your superior’s
expectations?
−.14 −.06 .10 .06 .89 .193
Satisfaction 1: I am satisfied with the overall accuracy of the AIS .01 .02 .07 .07 .28 .81
Satisfaction 2: The information usually meets my needs −.15 −.04 −.01 .07 .06 .91
Satisfaction 3: The information provided by the firm’s AIS supports this department’s day-to-day operations well −.22 .03 .11 .09 .13 .90
Satisfaction 4: Given the goals this department tries to meet, the information provided by the AIS is relevant −.03 −.05 .07 .05 −.07 .87
Satisfaction 5: The overall quality of the information is high −.14 .132 −.01 .09 .06 .91
Item Factor pattern
I II III IV V VI
APPENDIX A
CONTINUED

23. DETERMINANTS OF ACCOUNTING INNOVATION
239
APPENDIX B
MEASUREMENT INSTRUMENTS
Acceptance of MAS Innovations
1. I think the change in systems is excellent
2. I am one of the initiators of MAS change
3. I believe that the business unit has done a great job in bringing about this
change
Decision Rights
What influence do you have on the following decisions (1 to 5)?
1. Salary of employees
2. Trade-off between servicing internal or external customers
3. Order-filling acceptance
4. Making non-budget investments
5. Cost increasing decisions
6. Source of raw material/supplies
Subunit Adaptability
Coded so that a high score represented high adaptability.
1. I regularly alter production methods in response to changes in demand
2. I usually get quick responses to my proposals for new investment in production
facilities
3. Employees in this department are good at initiating and implementing new
working methods
4. Employees in this department can be easily deployed to work on other
tasks
Influence on the System Design
Respondents were asked to indicate who had the most influence on the design of
the MAS. Coded so that a high score represented high influence by the produc-
tion manager.
1. Design of the system
2. Implementation of the new system
3. Provision of data for input into the new system
4. Ongoing modifications to the new system
5. Maintenance of the new system
User Satisfaction
Coded so that a high score represented high perceived quality.
1. I am satisfied with the overall accuracy of the MAS
2. The information usually meets my needs
3. The information provided by the firm’s MAS supports this department’s day-
to-day operations well

24. ABACUS
240
4. Given the goals this department tries to meet, the information provided by the
MAS is relevant
5. The overall quality of the information is high
Subunit Performance
Coded so that a high score represented high performance.
1. How would you rate the current performance of the production department,
compared to your superior’s expectations?
2. How would you rate last year’s performance of the production department,
compared to your superior’s expectations?