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.
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 and 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 and 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 and 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 and 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.
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 Table 3 can hopefully serve as a means of selecting/developing appropriate statistical tests consistent with the particular sub-theory in question. However, it is important to ensure that the technique (or set of techniques) has a precise functional format and cover all the assumptions made.
