Elsevier

Accounting, Organizations and Society

Volume 33, Issues 7–8, October–November 2008, Pages 995-1009
Accounting, Organizations and Society

The appropriateness of statistical methods for testing contingency hypotheses in management accounting research

https://doi.org/10.1016/j.aos.2007.07.003Get rights and content

Abstract

In recent years, the contingency-based management accounting literature has been criticized for being fragmentary and contradictory as a result of methodological limitations. This study adds to this picture by showing that the theoretical meaning of some commonly used statistical techniques is unclear, i.e. the functional forms are not precise enough to be able to discriminate between several sometimes even conflicting theories of contingency fit. The study also shows that the techniques differ significantly in terms of how interaction effects between context and management accounting are modeled. This implies that some methods are only appropriate when theory predicts interaction effects in general while others are only appropriate in cases where theory specifies a more precise functional form of interaction such as symmetrical or crossover interactions. Based on these observations, several recommendations for future research are proposed.

Introduction

In recent years, several literature reviews have highlighted that many different ways of conceptualizing ‘contingency fit’ between context and Management Accounting System (MAS) have been used in the literature (Chenhall, 2003, Luft and Shields, 2003) and that few researchers fully acknowledge the difficulties of relating these forms to each other (Gerdin & Greve, 2004). There has also been a growing interest in (and debate about) how individual statistical techniques have been applied in contingency-oriented MAS research (Dunk, 2003, Gerdin, 2005a, Gerdin, 2005b, Hartmann, 2005, Hartmann and Moers, 1999, Hartmann and Moers, 2003). The purpose of this paper is to combine these two streams of research by providing a systematic analysis of the appropriateness of commonly used statistical techniques for testing the different forms of fit found in the literature.

In so doing, we propose a conceptual framework which identifies a number of possible perspectives of contingency fit. Unlike most of the existing MAS literature (e.g. Chenhall, 2003, Gerdin and Greve, 2004, Luft and Shields, 2003), the framework explicitly elaborates on the distinction between a matching and a multiplicative model of fit (Schoonhoven, 1981). The framework also contributes to the more general discussion about the use of statistical techniques in contingency research (Donaldson, 2001, Drazin and Van de Ven, 1985, Meilich, 2006, Venkatraman, 1989) by highlighting that the paradigm seems to accommodate at least three levels of theory specification.

The paper proceeds as follows. Drawing upon seminal contingency work, three levels of precision in the functional form of context/MAS interactions and four principal and conflicting approaches to contingency fit are identified. Next, it is discussed to what extent statistical methods frequently applied in contingency-based MAS research can be used to test the different levels of interaction and to distinguish between the four approaches. This results in several conclusions and recommendations for future research which finalize the paper.

Section snippets

Levels of theory specification in the contingency paradigm

The essence of contingency theory is that organizations must adapt their structure to contingencies such as the environment, organizational size and business strategy if the organization is to perform well (Burns and Stalker, 1961, Donaldson, 2001, Drazin and Van de Ven, 1985, Lawrence and Lorsch, 1967, Pennings, 1992, Woodward, 1965). Galbraith (1973, p. 2) formulated this core idea of contingency theory in the following way:

  • 1.

    There is no one best way to organize.

  • 2.

    Any way of organizing is not

Conceptualizations of contingency fit

Over the years, there has been considerable debate in the contingency theory literature (for overviews see Donaldson, 2001, Drazin and Van de Ven, 1985, Pennings, 1992, Venkatraman, 1989). The following three issues of controversy are directly linked to the conceptualization of contingency fit:

Commonly used statistical techniques in management accounting studies to conceptualize contingency fit

According to several recent literature reviews, many different statistical techniques have been used in the literature (see e.g. Chenhall, 2003, Gerdin and Greve, 2004, Hartmann and Moers, 1999, Luft and Shields, 2003). In Table 2, frequently occurring techniques identified in these literature reviews are outlined.

In the following sections, these statistical techniques will be briefly described and classified in terms of their level of theory specification and it will be discussed to what

Results of the analysis of statistical methods

In Table 3, the different uses of statistical methods found in the contingency-style MAS literature have been sorted into the classificatory framework developed above. A method’s row position thus denotes the level of specificity of the theory being tested and the column denotes the particular model of fit being tested. A line that crosses several columns represents the method’s inability to discriminate between the models of fit in question. Finally, a dotted line marks that the interaction

Suggestions for future research

Based on these observations, a procedure for the analysis of the appropriateness of statistical methods may be sketched. However, before so doing, the study should be positioned theoretically by specifying the form of fit and the level of interaction (see Table 3).

Specify the form of fit. This is an important step as earlier studies have suggested that the theoretical assumptions underpinning congruence- and contingency-type of models, respectively, and Cartesian- and Configuration-type of

Conclusion

Our objective has been to examine the appropriateness of commonly used statistical methods in the contingency-based MAS literature. In so doing, we developed a framework (Table 1) which highlights that we need to make more specific choices of theory than has typically been noted. In particular, we should pay more attention to the theoretical differences between matching- and multiplicative-type of models, and between interaction levels. Given these specifications, the analysis summarized in

Acknowledgements

The authors gratefully acknowledge the useful comments made by the anonymous reviewers, Frank G. H. Hartmann, and participants at workshops at CEROC (Centre for Empirical Research on Organizational Control) at Örebro University and at the Fifth EASM conference on New Directions in Management Accounting, Brussels 2006. Financial support for this project was provided by Örebro University, and the Jan Wallander and Tom Hedelius Foundation.

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