Directory UMM :Data Elmu:jurnal:A:Accounting, Organizations and Society:Vol24.Issue4.May1999:

Accounting, Organizations and Society 24 (1999) 291±315

Mini-review

Testing contingency hypotheses in budgetary research: an
evaluation of the use of moderated regression analysis$
Frank G.H. Hartmann a, Frank Moers b, *
a

Department of Financial Management, Faculty of Economics and Econometrics, University of Amsterdam,
Roetersstraat 11, 1018 WB Amsterdam, The Netherlands
b
Department of Accounting, Faculty of Economics and Business Administration, Maastricht University, P.O. Box 616, 6200 MD
Maastricht, The Netherlands

Abstract
In the contingency literature on the behavioral and organizational e€ects of budgeting, use of the Moderated
Regression Analysis (MRA) technique is prevalent. This technique is used to test contingency hypotheses that predict
interaction e€ects between budgetary and contextual variables. This paper critically evaluates the application of this
technique in budgetary research over the last two decades. The results of the analysis indicate that the use and interpretation of MRA often do not conform to proper methodology and theory. The paper further demonstrates that these
problems seriously a€ect the interpretability and conclusions of individual budgetary research papers, and may also

a€ect the budgetary research paradigm as a whole. # 1999 Elsevier Science Ltd. All rights reserved.
Keywords: Budgetary research; Reliance on accounting performance measures; Budgetary participation; Methodology; Moderated
regression analysis; Interaction.

1. Introduction
Over the last 40 years a research paradigm has
developed in the management accounting literature that focuses on the use of budgets in organizations. An early study by Argyris (1952) provided
a ®rst attempt to describe the e€ects of using
budgets on the behavior of employees. Whereas

* Corresponding author. Fax: +31-20-525-5285.
$
The authors gratefully acknowledge the comments made by
Ken Merchant, David Otley and two anonymous reviewers. This
paper has further bene®ted from presentations at Maastricht University, the 21st annual EAA meeting, the Fourth International
Management Control Systems Research Conference, the EIASM
workshop on New Directions in Management Accounting, and
the 1999 AAA Management Accounting Research Conference.

Argyris and other researchers in the 1950s and

1960s often studied budget-related issues following
a case-study methodology, later studies predominantly relied upon survey data. In the 1970s
two such survey studies on budgeting appeared
that have become particularly in¯uential. These
studies, by Hopwood (1972) and Otley (1978),
focused on the behavioral and attitudinal e€ects of
using budgetary information to evaluate the performance of subordinate managers. Hopwood
(1972) found that a high reliance on budgetary
performance led to a high degree of stress, as well
as to dysfunctional managerial behavior. Believing
that Hopwood's results were likely contingent on
other organizational variables, Otley (1978)
designed a study that involved a research site
where it was expected that Hopwood's results

0361-3682/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved.
PII: S0361 -3 682(99)00002 -1

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F.G.H. Hartmann, F. Moers / Accounting, Organizations and Society 24 (1999) 291±315

would not hold. Indeed, Otley obtained results
that were contrary to those of Hopwood. He did
not ®nd negative relationships between the use of
budgetary performance information and subordinates' attitudes and behaviors; instead he
found either no correlation or positive correlations. The con¯icting results of these two studies
provided an important stimulus to other researchers to adopt contingency perspectives in studying
the e€ects of the formalized construct Reliance on
Accounting Performance Measures (RAPM). For
example, Brownell (1982a) expected that the difference in results could be explained by a construct
labeled Budget Participation that relates to subordinate managers' involvement in budget setting.
This variable had also received ample attention in
the literature during the sixties and the seventies
(cf. Shields & Shields, 1998). Generally, such contingency studies have aimed to ®nd a match
between the use of budgets and the context in
which they are used. Together, they form a body
of literature which has attained a dominant position in contemporary management accounting
research (cf. Chapman, 1997). Brownell and Dunk
(1991, p. 703) characterize the development of this

paradigm as:
The continuing stream of research devoted to
this issue constitutes, in our view, the only
organized critical mass of empirical work in
management accounting at present.
Over the last decade, several papers have provided overviews and evaluations of di€erent
aspects of this contingency literature on budgeting
(e.g. Briers & Hirst, 1990; Fisher, 1995; Hartmann, in press; Kren & Liao, 1988; Shields &
Shields, 1998). The purpose of the present paper is
to address and critically evaluate the statistical
method used in this literature to test contingency
hypotheses. It focuses on the use of Moderated
Regression Analysis (MRA), which has become
the dominant statistical technique in budgetary
research for testing contingency hypotheses. The
use of techniques such as MRA has received only
little attention in the overview papers mentioned.
Yet, such attention seems warranted for at least
two reasons. First, since the introduction of the

contingency theory paradigm in budgeting, statistical techniques have become increasingly important
(cf. Briers & Hirst, 1990, p. 385). They not only
a€ect the design, execution and success of individual studies, but also determine the paradigm's
overall success (cf. Lindsay, 1995). Second, attention to the MRA technique seems particularly
warranted given the complexity and speci®city of
the technique, and the problems associated with
its use (e.g. Arnold, 1982, 1984; Jaccard, Turrisi &
Wan, 1990). As will be shown in detail below,
budgetary papers often appear to neglect these
complexities, which causes ¯aws in the application of MRA and in the interpretation of
results.
The remainder of the paper is structured as follows. Section 2 begins with a short explanation of
the concept of `®t' in contingency theory. It continues with an explanation of the basic properties
of MRA. Section 3 discusses the selection of budgetary research papers for analysis. Section 4
describes speci®c characteristics of MRA and presents the ®ndings of the analysis of the use of
MRA in the selected research papers. Finally,
Section 5 discusses the implications of the ®ndings
for both the current state and required future
developments of budgetary research.

2. Testing contingency theories of budgeting
Contingency theories of accounting are the
opposites of universal theories of accounting in
that they link the e€ects or the optimality of
accounting systems to the environment and context in which these systems operate. In a summary
of early management accounting studies that used
contingency frameworks, Otley (1980) concluded
that much needed to be done in the development
of a contingency theory of accounting, and he
outlined some minimal requirements for such a
theory, stating that:
(. . .) a contingency theory must identify speci®c aspects of an accounting system which
are associated with certain de®ned circumstances and demonstrate an appropriate
matching (Otley, 1980, p. 413).

F.G.H. Hartmann, F. Moers / Accounting, Organizations and Society 24 (1999) 291±315

Three elements in this prescription are essential,
relating to: (1) the `speci®c aspects' (2) the `de®ned
circumstances'; and (3) the `appropriate matching'. The ®rst element (i.e. speci®c aspects) points

to the demand for speci®city of the accounting
system variables in the formulation and test of
theories. The second element (i.e. de®ned circumstances) points to the conceptual di€erence
between a universal theory and a contingency
theory. The third and last element (i.e. appropriate
matching) forms the core of this paper, as it points
to the empirical di€erence between a universal
theory and a contingency theory. Otley (1980)
does not show how an `appropriate matching' is to
be de®ned theoretically, nor does he prescribe how
it is to be determined empirically. Such prescriptions and illustrations can be found, however, in
the organizational literature, which has a larger
history in contingency methodology (cf. Chapman,
1997) and pays ample attention to the theoretical
and empirical aspects of determining the `appropriate matching'. In the remainder of this paper, this
`appropriate matching' element will be addressed
with the more common term `contingency ®t'.
An overview of the organizational literature
reveals several di€erent concepts of `contingency
®t' (see, e.g. Drazin & Van de Ven, 1985; Schoonhoven, 1981; Venkatraman, 1989). These concepts

of ®t are each associated with a di€erent theoretical interpretation, and each require a di€erent
statistical test. In this paper, the discussion is
restricted to a type of contingency ®t called the
interaction type of ®t, which is the dominant conceptualization of contingency ®t in budgetary
research. The appropriate statistical technique to
test the interaction type of ®t is through Moderated Regression Analysis (MRA), that will be
explained below. Appendix A brie¯y outlines several other common types of ®t, stating both the
typical format of the underlying contingency
hypothesis and the appropriate statistical test.
2.1. Moderated Regression Analysis, the basic
format
Moderated Regression Analysis (MRA) is a
speci®c application of multiple linear regression
analysis, in which the regression equation contains

293

an `interaction term' (e.g. Champoux & Peters,
1987; Southwood, 1978). A typical equation for the
multiple regression of a dependent variable (Y) on

two independent variables (X1 and X2 ) is presented
in Eq. (1):
Y ˆ 0 ‡ 1 X1 ‡ 2 X2 ‡ "

…1†

In contrast, a typical regression equation used in
MRA has the format of Eq. (2):
Y ˆ 0 ‡ 1 X1 ‡ 2 X2 ‡ 3 X1  X2 ‡ "

…2†

Eq. (2) di€ers from Eq. (1) by the inclusion of the
product of the two independent variables (X1  X2 ).
This product term is said to represent the moderating
e€ect of variable X2 on the relationship between X1
and Y. In contrast, the other terms in the equation
(X1 and X2 ) are said to represent the main e€ects of
variables X1 and X2 on Y. The meaning of this product term in establishing a moderating e€ect can be
illustrated by taking the partial derivative of Eq. (2)

with respect to X1 …@Y=@X1 †, which has the format
expressed by Eq. (3):
@Y=@X1 ˆ 1 ‡ 3 X2

…3†

As Eq. (3) illustrates, the term representing the
partial derivative (@Y=@X1 ) is a function of X2 .
This means that the `form' of the relationship
between Y and X1 is a function of X2 , or in short,
that variable X2 moderates the form of the relationship between X1 and Y (cf. Champoux &
Peters, 1987, p. 244; Jaccard et al., 1990, p. 22). A
moderating e€ect can be graphically illustrated as
a variation in the slope of the regression line of Y
and X1 as a function of X2 . Fig. 1 below depicts a
situation in which the slope of the regression line
between X1 and Y is more positive for higher
values of X2 . This is expressed by stating that Y is
a function of the interaction between X1 and X2 .
Alternatively, it is said that the relationship

between Y and X1 is contingent upon X2 . To prove
the contingency hypothesis, therefore, one must
prove that X2 in¯uences the relationship between
X1 and Y, or that X1 and X2 interact to a€ect Y.
Although X2 is considered the moderator in the
example above, a similar analysis applies if X1 is

294

F.G.H. Hartmann, F. Moers / Accounting, Organizations and Society 24 (1999) 291±315

Fig. 1. Moderating e€ect.

considered the moderator of the relationship
between X2 and Y. Then, illustrated with Eq. (3a),
the partial derivative is taken with respect to X2
…@Y=@X2 †, and it follows that the relationship
between X2 and Y is a function of X1 .
@Y=@X2 ˆ 2 ‡ 3 X1

…3a†

For this reason, the moderating e€ect expressed
by the interaction term in Eq. (2) is called `symmetrical' (cf. Arnold, 1982; Southwood, 1978).
This implies that if X2 moderates the relationship
between X1 and Y, then X1 necessarily also moderates the relationship between X2 and Y. It is
because of this symmetry that the neutral expression refers to an `interaction' of X1 and X2 to
a€ect Y. Whether an independent variable is
labeled as a moderator or an independent variable
is a matter of theory rather than statistics. A
moderator variable theoretically a€ects the relationship between the independent variable and the
dependent variable, but is not itself theoretically
related with either the dependent or independent
variables (e.g. Arnold, 1982, p. 154; 1984, p. 216;
Shields & Shields, 1998, p. 51). Typically, variables
are labeled moderators that are exogenous or
uncontrollable (e.g. Cohen & Cohen, 1983, p. 305).
Sharma, Durand, and Gur-Arie (1981) provide an
overview and taxonomy of moderator variables.
In empirical contingency research, MRA is used
to establish the existence of a statistically signi®cant interaction e€ect. A method to do so is
through hierarchical regression analysis (e.g.
Arnold & Evans, 1979; Cohen & Cohen, 1983;
Cronbach, 1987; Southwood, 1978). This method
requires running two regressions, one with the

main-e€ects-only [cf. Eq. (1)] and a second with
both main e€ects and the interaction term [cf. Eq.
(2)]. A signi®cant interaction e€ect is con®rmed by
the statistical signi®cance of the additional variance explained by the inclusion of the interaction
term (i.e. the signi®cance of the increase in R2 ).
This method is equivalent to the simpler and more
direct assessment of the signi®cance of the t-value
associated with the coecient of the product term
(see Southwood, 1978, p. 1168; Arnold, 1982,
p. 157; Jaccard et al., 1990, p. 22). The equivalence
of these two methods is illustrated by Cohen and
Cohen (1983), who show that the F-statistic for the
increase in R2 equals the square of the t-statistic for
the interaction term.1
The equivalence of these two methods is illustrated by Cohen and Cohen (1983), who show
that the F-statistic for the increase in R2 equals the
square of the t-statistic for the interaction term. In
the example used above, this means that a test for
a statistically signi®cant moderating e€ect of X2
on the relationship between X1 and Y implies a
test whether the coecient of the interaction term
…3 † in Eq. (2) is statistically signi®cant. The symmetry applies here as well; a signi®cant t-value of
the coecient of the interaction term thus simultaneously implies a signi®cant moderating e€ect of
X1 on the relationship between X2 and Y.

1
See, for example, Chenhall (1986) for a redundant test for
the signi®cance in incremental explanatory power after testing
for the signi®cance of the interaction term. Surprisingly, the Fstatistic of incremental explanatory power and the squared tstatistic of the interaction in this study do not match (F equals
7.27, t-square equals 28.30). The only explanation seems to be a
calculation error.

F.G.H. Hartmann, F. Moers / Accounting, Organizations and Society 24 (1999) 291±315

295

Y ˆ …0 ‡ 2 † ‡ …1 ‡ 3 †X1 ‡ "

…for X2 ˆ 1†
…2b†

The interaction in Eq. (2) above is commonly
called a two-way interaction, since the equation
contains two variables and their interaction.
Moreover, given that in the example (see Fig. 1)
the relationship between X1 and Y is more positive
(or: less negative) for higher values of X2 , it is
called a `positive interaction' between X1 and X2 . A
`negative interaction' signi®es that the relationship
between X1 and Y is more negative (or: less positive)
for higher values of X2 . Additionally, the interaction
can be either monotonic or non-monotonic. A monotonic interaction exists when the partial derivative
does not `cross' the horizontal axis. This means that
the moderating e€ect of X2 changes the slope of the
relationship between X1 and Y within positive
values, or negative values, only (cf. Schoonhoven,
1981). Appendix A presents verbal examples of both
monotonic and non-monotonic interactions.
2.2. Moderated Regression Analysis with a dummy
variable
A special and often used form of MRA is
obtained when the moderator variable is a dummy
variable, taking on only discrete values (e.g. 0 and
1). If, for the example above, the moderator X2
has only values of 0 and 1, the original equation
expressing the interaction e€ect between X1 and
X2 in Eq. (2), can be rewritten in the formats of
Eq. (2a) and Eq. (2b) below.
Y ˆ 0 ‡ 1 X1 ‡ 2 X2 ‡ 3 X1  X2 ‡ "

Y ˆ  0 ‡ 1 X 1 ‡ "

…for X2 ˆ 0†

…2†

…2a†

While this does not change the interpretation of
the coecient of the interaction term (3 ), the
decomposition illustrates that an analysis is done
for two subgroups. Therefore, the MRA with a
dummy variable is sometimes called `subgroup
regression analysis' (e.g. Stone & Hollenbeck,
1984) in which the `subgroups' are distinguished
based on extreme (e.g. high and low) values of the
moderator variable. Arnold (1984, pp. 219±221)
argues that the label `subgroup regression analysis' may lead to the incorrect belief that this
method di€ers from general MRA. In fact, MRA
is always concerned with di€erent `subgroups',
however, introducing a dummy variable reduces
the number of `subgroups' to two. A graphical
example of MRA when the moderator variable is
a dummy variable is presented in Fig. 2.
Fig. 2 shows two regression lines, one for each
of the two values of X2 . The ®gure illustrates a
positive interaction, which means that the coecient (3 ) of the interaction term is positive. From
comparing Eqs. (2a) and (2b) above, it follows
that a positive and signi®cant coecient (3 ) suggests that the slope of the regression line for the
`X2 =1' subgroup is signi®cantly `more positive'
than the slope of the regression line for the `X2 =0'
subgroup. It should be noted that the associated
label `positive interaction0 is only meaningful if the
dummy values 0 and 1 re¯ect underlying values
(e.g. low and high) of the moderator variable.
However, MRA is also meaningfully applied if a
dummy does not re¯ect an underlying quantitative
variable, for example, if it re¯ects a natural dummy

Fig. 2. Interaction e€ect when the moderator is a dummy variable.

296

F.G.H. Hartmann, F. Moers / Accounting, Organizations and Society 24 (1999) 291±315

(e.g. male±female). Despite the prevalence of such
`natural dummies', subgroup regression analysis is
commonly performed based on a categorization of
the scores on an underlying continuous variable.
Such categorization has been argued to be unadvisable, since it implies a loss of information (e.g.
Cohen & Cohen, 1983, p. 310; Pedhazur & Pedhazur-Schmelkin, 1991, p. 539), yet it has substantial
advantages relating to the understandability of the
MRA outcomes and the statistical power of the
MRA technique (Arnold, 1984, pp. 221±222).
These advantages are especially important when
the analysis incorporates interactions of a higher
order than the two-way interactions discussed so
far. Such higher-order interactions are discussed
further below.

3. Selection of the budgetary research papers
The budgetary research papers were selected for
analysis using four criteria. These were: (1)
research method; (2) publication date; (3) journal
of publication; and (4) subject of study. The ®rst
criterion aimed at the selection of papers that used
a survey methodology using questionnaires. The
second criterion resulted in papers published in the
period from 1980 to 1998. This period was chosen
because it lies after the in¯uential conceptual
paper of Otley (1980). The third criterion aimed at
selecting papers from high-quality accounting
journals. This criterion was applied to limit the
total number of papers, as well as to exclude
`lower journal quality' as a potential explanation
for the ®ndings. Based on an examination by
Brown and Huefner (1994) of the perceived quality of accounting journals among di€erent respondents, papers were selected from The Accounting
Review, Journal of Accounting Research, and
Accounting, Organizations and Society.2 Finally,
regarding the subject of study, papers were selected
that hypothesize and test contingency ®t concerning budget-related variables, such as RAPM and
2

The Journal of Accounting and Economics belongs to the
four journals with the highest perceived quality in the Brown
and Huefner (1994) study. It has not published, however, contingency studies of budgeting.

Budgetary Participation, using an `interaction'
concept of ®t. The application of these criteria
resulted in the selection of 28 papers. Table 1
shows the dispersion of the papers over the three
journals. Appendix B provides an overview of the
28 papers that meet the selection criteria.

4. Analysis of the budgetary research papers
This section analyzes the application and interpretation of MRA in the 28 budgetary research
papers reviewed. In addition to the basic format of
MRA discussed above, here di€erent subsections
explain di€erent speci®c characteristics of MRA.
These speci®c characteristics relate to interaction
and (1) the strength of relationships; (2) the formulation of hypotheses; (3) lower-order e€ects; (4)
multiple and higher-order interactions; (5) e€ect size;
and (6) (non-) monotonicity. Each subsection illustrates and discusses how these MRA characteristics
appear in the individual studies.
4.1. Interaction and the strength of relationships
In Section 2.2 above, a reference was made to
the `subgroup' method for illustrating the di€erence in the slope of the regression line for two
subgroups. The literature uncovers another use of
subgroup analysis which tests for di€erences
between subgroups in the strength of the relationships between the independent and dependent
variables (e.g. Champoux & Peters, 1987, p. 243;
Stone & Hollenbeck, 1984). Within the context of
the example, this `subgroup correlation analysis'
implies that a test is made for di€erences between
the correlation of X1 and Y for extreme values of

Table 1
Dispersion of reviewed budgetary articles over journals
Journal (acronym used)

Number of
articles

The Accounting Review (TAR)
Journal of Accounting Research (JAR)
Accounting, Organizations and Society (AOS)

5
6
17

Total

28

F.G.H. Hartmann, F. Moers / Accounting, Organizations and Society 24 (1999) 291±315

297

Fig. 3. Interaction as strength and as form.

X2 . The di€erence in substantive meaning of the
two kinds of analyses is graphically illustrated in
Fig. 3. In this ®gure, the shaded areas represent
the `clouds' of observations (scatter diagrams) of
the relationship between X and Y. A `wide' cloud
indicates a low correlation and a `narrow' cloud
indicates a high correlation. For each cloud the
appropriate regression lines are depicted as well.
Panel A of Fig. 3 shows no sign of interaction,
since both correlations and regression lines are
equal for the two subgroups. Panel B illustrates a
`form' interaction since the slope of the regression
line is di€erent for the two subgroups. No indication of interaction for `strength' exists, since X
appears to predict Y in both subgroups equally
well, evidenced by the absolute values of the correlation coecients being equal. Panel C is the opposite of panel B since there is no di€erence in slope,
but there is a di€erence in `strength'. This means
that in subgroup 1, X is a better predictor of Y than
in subgroup 2. Panel D shows a combination of
`form' and `strength' interactions, since both the
slope of the regression line and the correlation of X
and Y are di€erent for the two subgroups.
There has been some confusion in the literature
about whether contingency hypotheses re¯ect differences in `form' or in `strength' (see, e.g. Arnold,

1982, 1984; Stone & Hollenbeck, 1984). The common understanding, however, now seems to be
that contingency hypotheses are of the form type
(panels B and D, Fig. 3), and it is even argued that
di€erences in strength are commonly meaningless
(Arnold, 1982, pp. 153±154; Schmidt & Hunter,
1978, p. 216). A problem, therefore, exists with
respect to the relationships hypothesized and tested in many budgetary studies. In 15 of the 28
papers used in this paper there is either no
hypothesis explicitly stated (Brownell & Dunk,
1991; Brownell & Hirst, 1986; Brownell & Merchant, 1990) or it is stated in a null form predicting
that there is `no interaction' between the measured
variables (Brownell, 1982a, b, 1983, 1985; Chenhall, 1986; Dunk, 1989, 1990, 1993; Frucot &
Shearon, 1991; Harrison, 1992, 1993; Mia &
Chenhall, 1994). This raises the question about the
meaning of the word `interaction' in these cases, in
particular whether it relates to the strength of the
relationship or the form of the relationship. Since
these types of ®t are not equivalent for either the
theoretical interpretation or the statistical test,
null hypotheses are inadequate for describing the
speci®c contingency formulations and statistical
test to be used. Obviously, if an author states that
there is an `interaction' (e.g. Hirst & Lowy, 1990),

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F.G.H. Hartmann, F. Moers / Accounting, Organizations and Society 24 (1999) 291±315

this hypothesis has the same shortcomings as the
null hypothesis described above.
Despite the dominance of form interactions in
contingency research, budgetary researchers have
investigated hypotheses that seem to express
strength interactions. If there is a theory supporting
such relationships, the appropriate statistical analysis consists of calculating z-scores of the correlation coecients. Since the interest is in the
di€erence in predictive power between subgroups,
it is important that the z-scores are calculated
using absolute values of the correlation coecients. Obviously, their sign does not contain
information about predictive power. Merchant
(1981, 1984, 1990) and Govindarajan (1984) test
for di€erences in correlation coecients, but do
not calculate nor speci®cally mention the `absolute' z-score.
4.2. Interaction and the formulation of hypotheses
The examples above point to a weak link
between the verbal and statistical format of
hypotheses, which has been noted and discussed
before by Schoonhoven (1981). She criticizes studies in the organizational literature for their lack
of clarity in stating contingency hypotheses, which
a€ects the obviousness of the statistical test to be
used. Above, a reference was made to Govindarajan (1984), who tests a strength hypothesis. A
further problem with Govindarajan's (1984) analysis relates to the substantive content of his contingency hypothesis, which is incompatible with
his theoretical arguments.3 His hypothesis predicts
that `organizational e€ectiveness' a€ects the
strength of the relationship between `environmental uncertainty' and `evaluation style'. The
format of the statistical analysis is in conformity
with this hypothesis, since it compares the correlation coecients for the two subgroups. The
subsequent interpretation of these results, however, is not. Govindarajan (1984, p. 133) concludes
that environmental uncertainty has a `signi®cant

3
Govindarajan's (1984) theoretical arguments indicate an
interaction of the form type. Govindarajan even speci®cally
proposes a non-monotonic relationship (see Fig. 1, p.128)

moderating e€ect' on the relationship between
evaluation style and organizational e€ectiveness.
This is a peculiar interpretation since the subgroup
correlation analysis is based on high and low `e€ectiveness' subgroups. This suggests that `e€ectiveness' is the moderator instead of the theoretically
relevant moderator `environmental uncertainty'.4
Overall, therefore, there is no match between the
theoretical arguments, the formulation of the
hypothesis, and the interpretation of the statistical
analysis of the hypothesis. Govindarajan's (1984)
conclusion about the moderating e€ect of `environmental uncertainty' is clearly unfounded.
In another six of the 28 papers (Merchant, 1981;
1984; Brownell, 1982b; Imoisili, 1989; Frucot &
Shearon, 1991; Dunk, 1992), obvious di€erences
exist between the hypothesis and the statistical test
used, which makes the conclusions by the authors
debatable.5 Merchant (1981, 1984) hypothesizes
an interaction a€ecting the form of the relationship, as shown by an example of the following
hypothesis:
Organizational performance tends to be
higher where there is a `®t' between the use of
budgeting and the situational factors, as
described in Hypotheses 1±4. (Merchant,
1984, p. 294; emphasis added.)6
Recall from the previous section that the statistical test used is a correlation per subgroup and
relates to the strength of the relationship. The results
of the analysis are therefore of little relevance and
value to the contingency hypothesis stated.
A second example of a di€erence between the
hypothetical and measured ®t are the papers of

4

Note that labeling an independent variable the `moderator' in
MRA is a question of theory because of the symmetry mentioned
earlier. Yet, Govindarajan does not switch the moderator and the
independent variable, but switches the moderator and the dependent variable. In this case, the symmetry does not apply.
5
The papers of Imoisili (1989) and Dunk (1992) will be discussed in Sections 4.4 and 4.5, respectively.
6
This hypothesis is the ®fth in Merchant's paper and refers
to hypotheses 1±4. In these hypotheses, Merchant applies the
selection type of ®t and uses correlation analysis. For example
hypothesis 4 states: ``Larger, more diverse departments tend to
place greater emphasis on formal budgeting.''

F.G.H. Hartmann, F. Moers / Accounting, Organizations and Society 24 (1999) 291±315

Brownell (1982b) and Frucot and Shearon (1991,
p. 85). Frucot and Shearon (1991) state a null
hypothesis in the following form:

299

in either direction reduce the value of performance. (cf. Schoonhoven, 1981).9
4.3. Interaction and lower-order e€ects

In Mexico, locus of control does not have,
through an interaction e€ect with budgetary participation, a signi®cant e€ect on
performance.
Subsequently the following equation is statistically
tested:
Y ˆ 0 ‡ 1 X1 ‡ 2 X2 ‡ 3 X1 ÿ X2 ‡ "

…4†

This equation does not, however, measure ®t as
interaction but ®t as matching (Venkatraman,
1989, p. 431). Because ®t as interaction (i.e. form)
is hypothesized, the verbal statement and statistical measure are not compatible.7 Given this
incompatibility, the conclusion cannot be interpreted and the statistical results become exploratory at best. The reason the authors give for using
the matching variable is that:
(. . .) the multiplicative interaction term does
not provide a good measure of this matching
condition'' (Frucot & Shearon, 1991, p. 90).
Although this is true, the hypothesis expresses `®t'
as interaction and simply does not predict the
matching condition. Frucot and Shearon (1991,
p. 90) also state that the variable should be measured by the absolute di€erence, arguing that this
better re¯ects the relationship among the three variables. If so, the hypothesis should have been altered
to state the matching condition, and the alternative
and only proper formulation would have been:8
In Mexico, given the value of locus of control, there is a unique value of budgetary participation that produces the highest value of
performance; deviations from this relationship
7
The authors also state that the relationship is `accentuated'
and `attenuated' (p. 85). This refers to accelerating and decelerating e€ects and thus to the form of the relationship.
8
The same line of reasoning applies to Brownell (1982b).

As illustrated above, the equation representing
an interaction e€ect [Eq. (2)] includes not only
the interaction term (X1  X2 ), but also the two
main e€ects (X1 ) and (X2 ). At least three topics
related to the inclusion of lower-order e€ects
warrant discussion. These are (1) the reason for
including lower-order e€ects; (2) the interpretation of the coecients for lower-order e€ects
found in the regression analysis; and (3) the
potential problem of multicollinearity. First, the
reason for the inclusion of lower-order e€ects in
MRA is to prevent conclusions of the existence of
an interaction e€ect when such an e€ect is solely
due to lower-order e€ects. An example is found
with Hirst (1983) who tests a model represented
by Eq. (5) below that does not include main
e€ects:
Y ˆ 0 ‡ 1 X1  X2 ‡ "

…5†

In this case, ®nding a signi®cant coecient 1 does
not necessarily indicate the existence of an interaction e€ect, since it could be due to a signi®cant
relationship between X1 and Y regardless of X2 . In
other words, because the interaction term is the
product of the two main e€ects, it is likely to `steal
variance' from its constituting parts (cf. Cohen &
Cohen, 1983, p. 305). Stone and Hollenbeck (1984,
p. 201) argue in this respect:
(. . .) while the cross-product term in a
regression equation ``carries'' the interaction,
the same cross-product term is not the interaction.
This implies that when testing for an interaction
e€ect, the lower-order e€ects should be `partialed
out' by including them in the regression equation
(Southwood, 1978, p. 1164; Cohen & Cohen,
9
For a discussion of the use of di€erence scores, see
Bedeian, Day, Edwards, Tisak, and Smith (1994).

300

F.G.H. Hartmann, F. Moers / Accounting, Organizations and Society 24 (1999) 291±315

1983, p. 348; Stone & Hollenbeck, 1984, p. 201).
Consequently, Hirst's (1983) conclusion based on
the results of Eq. (5) is unfounded.10
A second issue relates to the proper interpretation of coecients obtained for lower-order e€ects
in MRA. Southwood (1978, p. 1168) argues that
such coecients generally have no theoretical
meaning. The reason is that in the behavioral sciences the variables are usually measured using
interval scales, and not ratio scales. This means
that scale origins and thus linear transformations
of variable scores are arbitrary, and have no substantive meaning. Southwood (1978, p. 1668, pp.
1198±1201) shows that such linear transformations do change the coecient of the lower-order
e€ects in the MRA equations, and therefore, these
coecients cannot be easily interpreted. This does
not imply that the coecients of the lower-order
e€ects in an interaction model are lacking all
meaning. In particular, they signify the main e€ect
of the variable (e.g. X1 ) when the value of the
other variable (X2 ) is zero (Jaccard et al., 1990).
Only for ratio scale variables is this zero `meaningful' (Southwood, 1978, p. 1165). For interval
scale variables, the zero value obtains a speci®c

10
Hirst (1983, p. 600) claims to have found an interaction
e€ect of the form-type based on subgroup regression-analysis.
Since the regression coecients are standardized, and since
standardized regression coecients in simple linear regression
equals correlation coecients, the subgroup analysis is in fact
subgroup correlation-analysis. This latter statistical format,
however, tests strength and not form, so that Hirst's ®ndings
seem unfounded. However, in this speci®c case additional
information suggests the existence of a form interaction for one
of the two dependent variables (job-related tension). The correlation coecients between RAPM and job-related tension for
low and high task uncertainty are ÿ0.33 and 0.53 respectively.
Both coecients are statistically signi®cant, which means that
the regression coecients per subgroup are also signi®cant. As a
result, there will be a signi®cant negative coecient for the low
task uncertainty subgroup and a signi®cant positive coecient
for the high task uncertainty subgroup. Since signi®cant means
signi®cantly di€erent from zero, a positive (negative) signi®cant
coecient is also signi®cantly di€erent from any negative (positive) coecient. Thus, the di€erence between these two regression coecients is also signi®cant. The signi®cance of this
di€erence is equal to the signi®cance of the interaction term in
MRA and the ®t as interaction (form) is supported. Note that
Hirst's (1983) paper, as it is published, thus does not prove a ®t
as interaction (form); the above analysis does.

meaning if the variables are `centered' around
their respective means. In this case, the coecients
of the main e€ects represent the e€ect of one
variable at the (sample) average of the other (Jaccard et al., 1990, p. 34). Despite this meaning, it is
important to point out that the coecients
obtained for the main e€ects when applying MRA
are, in general, di€erent from those that would be
obtained through a regression model without the
interaction term. Furthermore, it is important to
note that linear transformations do not change the
coecient of the interaction term, nor its t-statistic
and level of signi®cance (Cohen & Cohen, 1983,
pp. 305±306; Southwood, 1978, p. 1168). Thus,
Brownell (1982a), Chenhall (1986) and Mia (1988)
are wrong in assuming that `centering' the variables provides a:
clearer basis for predicting the sign of (. . .) the
coecient of the interaction term (Brownell,
1982a, p. 20; emphasis added).
Since the coecient of the interaction term is not
sensitive to the scale origins, it also follows that for
tests of an interaction e€ect using MRA the independent variables need not be ratio±scaled (Southwood, 1978, p. 1167; Arnold & Evans, 1979).
The analysis of the budgetary papers shows that
the interpretation of lower-order e€ects is subject
to both Type-I and Type-II errors; interpretation
of invalid lower-order e€ects and no interpretation
of valid lower-order e€ects. First, ®ve papers show
an invalid interpretation of main e€ects in two-way
interactions (Brownell, 1982a, 1983, 1985; Chenhall, 1986; Mia, 1989). For example, Brownell
(1982a) studies (among others) the moderating
e€ect of Budget Emphasis on the relationship
between Budgetary Participation and Performance.
He interprets the interaction e€ect, and both main
e€ects [see Eq. (2)]. The Budgetary Participation
variable in the MRA equation is measured as a
`deviation score' from the overall mean (i.e. centered). A linear transformation of the raw score of
one independent variable leads to a change in the
regression coecient of the other independent
variable. This implies that the coecient of the
main e€ect of Budget Emphasis changes. As a
result, the coecient of Budget Emphasis illustrates

F.G.H. Hartmann, F. Moers / Accounting, Organizations and Society 24 (1999) 291±315

the e€ect of Budget Emphasis on Performance at
the average level of Budgetary Participation.11 This
interpretation di€ers from Brownell's (1982a, pp.
20±21) who incorrectly interprets the regression
coecient unconditional on Budgetary Participation.
Further, the Budget Emphasis variable is not centered
around its mean, which makes the coecient of
Budgetary Participation uninterpretable. Brownell's conclusion regarding Budgetary Participation is therefore unfounded.12 In studies using
higher-order interactions, similar problems are
found (e.g. Brownell & Hirst, 1986; Imoisili,
1989). These studies are discussed below in the
section on higher-order interactions.
The above ®ve studies precede the introduction
of the Southwood (1978) paper into the management accounting research literature.13 The more
recent and uncritical use of Southwood's paper,
however, has led to the reverse situation; no
interpretation of valid lower-order e€ects. Examples include the following. Mia and Chenhall
(1994) study the moderating e€ect of Function on
the relationship between the Use of Broad Scope
MAS and Managerial Performance. The MAS
variable is a `di€erence score' from the overall
mean (i.e. centered). This means that the coecient of the main e€ect of Function is interpretable.
The authors refer to Southwood and state:
No attempt was made to interpret the coef®cients (. . .) that related to extent of use of
broad scope Management Accounting System
(MAS) information or function. (p. 9; emphasis added.)
Thus, they incorrectly state that the coecient of
Function cannot be interpreted and therefore lose
information. Using this information would have

11

More speci®cally, the results show that at the average
level of Budgetary Participation, lower Budget Emphasis leads
to increased Performance.
12
Brownell concludes that higher Budgetary Participation is
associated with higher Performance.
13
Schoonhoven (1981) explicitly incorporates several main
e€ects in her multiple interaction equation and incorrectly
interprets them all. This is peculiar since Southwood (1978) is
among her references.

301

allowed the supplementary conclusion that Function by itself has a direct in¯uence on managerial
performance, in that marketing managers perform
better than production managers at the `average'
MAS. In Lau, Low and Eggleton (1995) all independent variables are centered. This means that
the lower-order e€ects in their interaction models
could have been interpreted. The authors, however, refer to Southwood and reject the interpretation of lower-order e€ects (p. 369).
Although, the papers of Mia and Chenhall (1994)
and Lau et al. (1995) are the only concrete and
explicit examples of a Type-II error, seven other
studies speci®cally refer to Southwood and a
priori reject the interpretation of lower-order
e€ects (Brownell & Dunk, 1991; Dunk, 1990,
1992, 1993; Harrison, 1992, 1993; Gul & Chia,
1994). The conclusion seems warranted that
researchers are unaware of the possibilities and
problems of interpreting lower-order e€ects in
MRA. In a way, Southwood's paper has had both
a positive and a negative e€ect on this. The positive e€ect is that main e€ects indeed cannot
usually be interpreted, while the negative e€ect has
been that it is believed that they should never be
interpreted in MRA. Unfortunately, this false idea
even shows up in handbooks aimed at novice
management accounting researchers (Brownell,
1995). Here it says that:
It is now widely understood that the estimated coecients for variables, included in
an equation along with their cross product
with another variable, are not interpretable.
(Brownell, 1995, p. 55; emphasis added.)
It also says that Southwood has shown that if a
constant is added to interval scale data that:
(. . .) this will alter the estimated coecient for
the variable to which the constant was added
(p. 55; emphasis added).
This is, of course, incorrect. The coecient of the
variable to which the constant is not added, changes. The `easy' reference to papers like Southwood's, without critically evaluating its wisdom, is
remarkable at least.

302

F.G.H. Hartmann, F. Moers / Accounting, Organizations and Society 24 (1999) 291±315

The third and ®nal point related to lower-order
e€ects in MRA is the potential problem of multicollinearity, caused by the fact that the lowerorder e€ects (e.g. X1 and X2 ) and their product are
likely to be correlated (e.g. Drazin & Van de Ven,
1985). Yet, from the same arguments that show
the insensitivity of the coecient of the interaction
term in MRA (X1  X2 ) to changes in scale origins
of X1 and X2 , it follows that multicollinearity is
not a problem when applying MRA (cf. Dunlap &
Kemery, 1987). In particular, it is possible to use
linear transformations of X1 and X2 that remove
the correlation between the main terms and the
interaction term (Southwood, 1978, p. 1167; Jaccard et al., 1990, p. 22). The `correct' linear transformation of variable scores to minimize the
correlation between the independent variables and
their product is the centering procedure discussed
before (e.g. Jaccard et al., 1990, p. 34). Recall that
since no such linear transformation ever a€ects the
coecient of the interaction term, the coecient
of the interaction term is always interpretable, and
centering is not required. As was illustrated, this is
not always recognized in budgetary studies
(Brownell, 1982a, 1983; Chenhall, 1986; Mia,
1988, 1989; Lau et al., 1995). As an example, Lau et
al. (1995) use deviation scores from the mean
because, as they state, it reduces the `problem' of
multicollinearity (p. 368). However, no such problem
exists.14
4.4. Multiple and higher-order interactions
The previous sections discussed the general format of MRA and focused on the analysis of a
single two-way interaction. MRA, however, is not
restricted to the analysis of a single two-way
interaction. It can also be used to analyze (1)
multiple two-way interactions, and (2) any n-way
interaction. First, multiple two-way interactions
can be tested by extending Eq. (2) to include an
additional main and interaction e€ect, as shown
by the example of Eq. (6a):

14
The only `problem' that multicollinearity can cause is that
the statistical program is unable to calculate the regression
coecients due to singularity of the matrix.

Y ˆ 0 ‡ 1 X1 ‡ 2 X2 ‡ 3 X3 ‡ 4 X1  X2
‡ 5 X1  X3 ‡ "

…6a†

As for any equation, the inclusion of an additional
two-way interaction in MRA should be based on
theory (Jaccard et al., 1990, p.40±41). More speci®cally, the theory should state that the relationship between X1 and Y is not only a function of
X2 , but also of X3 . In other words,
@Y=@X1 ˆ 1 ‡ 4 X2 ‡ 5 X3

…6b†

Imoisili (1989, p. 327) exactly hypothesizes the
relationship depicted by Eqs. (6a) and (6b). However, this hypothesis is incorrectly tested, since the
equation analyzed also includes a redundant twoway interaction term X2  X3 , and a redundant
three-way interaction term X1  X2  X3 . The
result of analyzing this equation does not provide
an answer to the hypothesis, since the partial derivative …@Y=@X1 † of the function that Imoisili uses is
given by Eq. (7), which contains an `interaction
e€ect' …X2  X3 † not hypothesized.
@Y=@X1 ˆ 1 ‡ 4 X2 ‡ 5 X3 ‡ 7 X2  X3

…7†

The only correct statistical test would have been
Eq. (6a). Harrison (1992, 1993) provides an
example of similar problems. Harrison (1992) uses
Eq. (8) to test for the existence of two-way interactions between Budgetary Participation (X1 ),
RAPM (X2 ), and the dummy variable Nation (X3 )
to a€ect Job-Related Tension (Y):
Y ˆ 0 ‡ 1 X1 ‡ 2 X2 ‡ 3 X3 ‡ 4 X1  X2
‡ 5 X1  X3 ‡ 6 X2  X3 ‡ "

…8†

Harrison (1992, p. 11) is only interested in the
moderating e€ect of X1 on the relationship
between X2 and Y, and his analysis is therefore
¯awed for two reasons.15 First, there is no theoretical foundation for using the multiple interaction
equation. Only the ®rst interaction (X1  X2 ) is
hypothesized by Harrison, both other interactions
15
The same conclusion can be made with respect to Imoisili
(1989).

F.G.H. Hartmann, F. Moers / Accounting, Organizations and Society 24 (1999) 291±315

(X2  X3 and X1  X3 ) are not.16 Second, this
equation has a chance of being overspeci®ed,
which means that extra predictor variables are
unnecessarily included. Overspeci®cation of the
model leads to an increased standard error of the
regression coecients (Cryer & Miller, 1991,
p. 639), in¯uencing the signi®cance test of the
coecient. Harrison states that:
4 , as the (highest order) interaction term, is
both stable and interpretable for its standard
error and signi®cance test. (Harrison, 1992,
p. 11; emphasis added.)
Although the interaction term, standard error
and signi®cance test may be stable, they are hardly
interpretable if the model is a€ected by an unnecessary overspeci®cation. A standard error in¯uenced by overspeci®cation is not interpretable, no
matter how stable it is. In this case, therefore, the
signi®cance of coecient 4 is not interpretable.
Moreover, the partial derivative of this equation
…@Y=@X2 † is:

Y ˆ 0 ‡ 1 X1 ‡ 2 X2 ‡ 3 X3 ‡ 4 X1  X2
‡ 5 X1  X3 ‡ 6 X2  X3 ‡ 7 X1  X2  X3 ‡ "
…10†
In conformity with the interpretation of the product term for two-way interactions, the product
term …X1  X2  X3 † in Eq. (10) represents the
three-way interaction among the (three) independent variables. The issues discussed above regarding use and interpretation of MRA for two-way
interactions are also important for three-way
interactions. Regarding the inclusion of main
e€ects in MRA, for a three-way interaction equation, all two-way interactions should be included,
and the user should observe that the coecients
obtained for these two-way interactions are not
directly interpretable (Cohen & Cohen, 1983,
p. 348).18,19 In general, for an n-way interaction,
the main e€ects and all possible interactions of a
lower-than-n order should be included. The di€erence between two-way and higher-order interactions

18

@Y=@X2 ˆ 2 ‡ 4 X1 ‡ 5 X3

…9†

This reveals that the relationship between X2 and Y
is not a linear function of X1 , but a linear function
of both X1 and X3 . As a result, the hypothesis
remains unanswered because the coecient 4 is
not a measure of the hypothesized moderating
e€ect of X1 on the relationship between X2 and Y.17
Apart from the use of multiple two-way interactions, MRA can be used to analyze n-way
interactions. As an example of higher-order interactions, a three-way interaction generally has the
format of Eq. (10):
16
Harrison (1992) refers to both Schoonhoven (1981) and
Jaccard et al. (1990) for using the multiple interaction equation.
Schoonhoven's statistical analysis is incorrect, and referring to
her analysis may have caused the incorporation of similar
shortcomings. The reference to Jaccard et al. (1990, pp. 40±41)
is intriguing, si