Exploring the inclusion of risk in management accounting and control

This paper aims to contribute to the construction of a framework that makes risk management (RM) more effective and visible. This is done by investigating how the concept of “risk” is included in various activities in the management accounting and control (MA&C) system.,A cross-sectional analysis of 72 Danish organisations extracted from an alumni database is conducted together with a factor analysis and a partial least squares structural equation modelling approach.,The authors find four latent variables, namely, expectation, attitude, subjective norms, processes and culture, which all have risk activities in MA&C as the depending variable. Attitude seems to be a powerful antecedent, whereas supporting processes and culture play a crucial partial mediator role for the inclusion of risk.,The findings add to the understanding of the interrelationships between risk and MA&C. An important caveat is that the authors use soft and self-reported data for the dependent variable and for the various independent variables.,The authors propose a dynamic and holistic framework for the analysis of risk. This framework eliminates the limitations found in many prior studies that have neglected the interrelated importance of attitude and supporting processes and culture. The results of this study also provide valuable insights for managers who wish to consider and to explore the interrelations of a number of antecedent risk issues that influence different risk activities in MA&C.,This paper is one of the few papers that assess the impact of different risk issues on firms’ different MA&C activities by including the theory of planned behaviour. The potential key role that supporting processes and culture play as partial mediators for risk inclusion is particularly interesting. The research extends prior research by constructing a framework that makes that implementation of RM processes in the MA&C system more effective. It also proposes a validation process that can lessen the model risk possible.