The purpose of this paper is to investigate the effectiveness of different configurational archetypes of strategy and strategic management accounting and to appraise how management accounting's horizontal and vertical alignment with strategy can facilitate performance.
The study deploys a holistic configurational approach to examine the relationship between strategy, strategic management accounting, and performance. Configurations are derived empirically, using an inductive approach, from a sample of 109 manufacturing companies.
The observed configurations (i.e. “analytics”, “blue‐chips”, “first movers”, “domestic protectors”, “laggards and socialism relics”) constitute varying levels of performance and varying degrees of fit. Support is provided for the equifinality proposition that different strategic and structural alternatives are associated with similar performance levels. Equivocal support is provided for the configurational proposition that internally consistent configurations are associated with higher performance.
The variables examined do not fully capture the complexity of pertinent configurations. Limitations revolve around application of the cluster analytical technique and its reliance on researcher judgement.
The study's most important message concerns the manner in which it highlights the fallibility of assuming a singular relationship between strategic choices and management accounting system design. While prior research has tended to offer fragmented and unidirectional management accounting prescriptions, the authors raise the notion of how key variables can interact to create an effective organization.
The paper breaks new ground by showing that multiple designs of strategy and strategic management accounting may be equally effective in a particular context. This finding challenges much traditional contingency‐based modelling in management accounting.
Cadez, S. and Guilding, C. (2012), "Strategy, strategic management accounting and performance: a configurational analysis", Industrial Management & Data Systems, Vol. 112 No. 3, pp. 484-501. https://doi.org/10.1108/02635571211210086Download as .RIS
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