MCS CONFIGURATIONS AND EFFICIENCY IN MECHANICAL ENGINEERING FIRMS: AN EMPIRICAL EVIDENCE BASED ON STOCHASTIC FRONTIER ANALYSIS

Although recent years have witnessed a growing interest in studying broader management control system configurations, it has been difficult to assess the outcomes of various control choices. Using firm overall financial performance as an indicator of control configuration effectiveness is questionable as there are many other factors in addition to MCS configuration that has an impact on firm financials. Assessing the quality of MCS against control problems those systems are to deal with is equally difficult in large empirical studies. This study contributes to discussio9n showing how the stochastic frontier method can be applied to empirically assess the performance effects of archetypal control configurations. The research focuses on a single industry - mechanical engineering -, in one country, where we have collected data from a cross-sectional sample of 242 medium-sized firms. We identify control configurations in use, assess their efficiency, and address the interplay of formal and informal controls. After controlling for heterogeneity of firms as well as a number of contextual factors, firms in the decentralized control mode appear more efficient than firms relying on centralized control. Based on our empirical analysis we develop a theory of efficient management control configurations for medium-sized mechanical engineering firms. Implications of findings for both research and practice are discussed.