Challenges in studying MCS as a package While there are good reasons to study MCS as a package there are a range of challenges in doing so; three of which will be explained in this editorial. The first involves the difficulty of clearly defining the concept of MCS. This includes making a distinction between MCS and information/decision-support systems. Furthermore, if we focus on control rather than decision-support, what is it that MCS is supposed to control; is it human behaviour or artefacts, such as cash or material flows; and at what level, the organisation, business unit, management, or individual? When the definition parameters of MCS are set, the second issue arises of what conceptually constitutes an MCS package; what is included, what is left out, and why? An analytical conception, which provides a sufficiently broad yet parsimonious approach, is required to study the empirical phenomenon. In addition, while studies have looked at control systems individually and at times in combination, the challenge is to understand how all the systems in an MCS package operate as an inter-related whole. Abernethy and Brownell (1997) captured this issue in stating: “It is clear that organisations rely on combinations of control mechanisms in any given setting, yet virtually nothing is known about how the effects of any one control are governed by the level of simultaneous reliance on other forms” (p. 246). Thirdly, there are challenges in empirically studying an MCS package as they are often very large and complex systems. This creates difficulties in how field and/or case study researchers gather and make sense of the complexity that exists in each of the elements of the MCS package and then report their findings in journal articles at a sufficient level of abstraction to make the reading comprehensible. Furthermore, there are problems with how survey researchers test the form of these large and complex packages across organisations so that systematic relationships can be established. This includes the difficulty of developing survey instruments to capture the underlying phenomena in a meaningful way as well as gathering adequately large samples. The purpose of this editorial is to enlighten the abovementioned issues and lay a foundation to enable researchers to continue developing research on MCS. The first issue to be addressed is what control is, and what is meant by MCS? We will then introduce what we consider to be a comprehensive but parsimonious typology of an MCS package which may be used to inform empirical work. Next we will discuss the four papers in this special issue including their overall conclusions. Finally, the implications of these discussions for further research will be outlined.
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