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Increased business competition requires even more rapid and sophisticated information and data analysis. These requirements challenge performance management to effectively support the decision making process. Business analytics is an emerging field that can potentially extend the domain of performance management to provide an improved understanding of business dynamics and lead to a better decision making. The purpose of this positional paper is to introduce performance management analytics as a potential extension of performance management research and practice. The paper clarifies the possible application areas of business analytics and their advantages within the context of performance management.

The paper employs a literature based analysis and from this a conceptual argument is established. Finally, a business analytical model is presented to be used to undertake future research.

The paper clarifies the possible application areas of business analytics and their advantages within the context of organizational performance management.

The main implication is that the paper provides evidence of the use of business analytics for understanding organizational performance. Several insights are provided for management accounting research and education.

The purpose of this paper is to develop and apply a framework that examines the effectiveness and efficiency of managing knowledge in organizations for competitive advantage.

Reviews knowledge management and strategic cost management literatures to identify key elements that determine and facilitate the enhancement of competitive advantage. Develops a cost‐knowledge management (CKM) framework that integrates these elements and enables the analysis of how knowledge utilization in organizational activities can be made more effective and efficient.

The CKM framework is usefully applied to a sample of four Italian firms operating in the mechanical industry. Both the results of applying the CKM framework and the insights that are generated are discussed.

The CKM framework allows organizations to analyze the activities performed in terms of cost structure and cost drivers, value created, and knowledge utilized, the latter in terms of knowledge specificity and type. The framework can also be used to highlight specific areas of effectiveness improvements in terms of identifying which activities should be leveraged and how knowledge can be better mobilized. In addition, the framework enables an assessment of the non value added but required and waste elements of organizational activities and the specific drivers of costs in these activities, thereby enabling an identification of efficiency improvement opportunities.

This paper integrates strategic cost management and knowledge management perspectives to examine how organizations can usefully analyze and improve the effectiveness and efficiency of managing knowledge for competitive advantage. Thus far, this integration has not occurred in either literatures.

For over 20 years, management control literature has indicated the importance of supporting the strategy development and implementation process with strategic performance measurement systems (SPMS) and integrating traditional financial indicators with a set of multidimensional forward-looking measures focusing on the long term and linked to cause-effect relationships. Nevertheless, knowledge on the specific SPMS models used in practice and their effectiveness in supporting the managerial decision-making process is still fragmented and ambiguous. The purpose of this paper is to first analyse the SPMS models used in practice, also considering the role of strategy and firm size as drivers of adoption, thereafter analysing the capability of SPMS models to provide managers with measures that are consistent with their strategic information needs.

The research is based on a survey involving 88 Italian medium-large sized firms (or subsidiaries of multinational firms) operating on a global level.

The cluster analysis identifies two very different SPMS models used in practice. The first is the Short-term Financial Model, and as its name indicates, is based on short-term, internally focused and unconnected financial indicators. The second is the Multidimensional Additive Model, which integrates financial and non-financial measures but without a fully developed fit with the strategy. The research primarily indicates unsatisfied information needs in both clusters, presenting a significant challenge to the further development of existing SPMS models and in defining new theoretical SPMS frameworks.

The adoption of an incremental approach to SPMS, simply adding new operational and strategic non-financial measures without a real fit with the strategy does not increase the information effectiveness of the system.

The paper analyses the characteristics and use of SPMS models in practice from an exploratory perspective, defining and applying a model to evaluate the information effectiveness of SPMS.

Two reasons are identified for studying the impact of capacity measurements on organizations. First, firms which make the best use of their resources can be expected to outperform their competitors. The second arises from the potential structuration effect of capacity metrics. Such an investigation makes capacity a visible, and hence an actionable, construct. This paper aims to address these issues.

To explore these issues, a combination of analytics and qualitative field research methodology was used. The measurement dimensions were developed by analyzing the different reports, baseline measures, and metrics included in the various capacity models as suggested by the literature. These analytics were enriched with observations obtained from field research.

Maximizing the value created within an organization starts with understanding the nature and capability of all the company's resources. The outcome is the identification of capacity systems specifically suited for particular types of operations, both manufacturing and service.

Such frameworks would allow organisations in developing economies, to make visible, the drivers of waste and productivity and to identify the primary assumptions and implications of various capacity limits.

This paper fills the gap between defining and measuring the productive limits of a machine or system, and the impact of various assumptions about the productive potential of the nature and informativeness of capacity cost management systems. The authors focused on the various ways in which multi‐dimensional limits (for example, time, space, volume and/or value‐creating ability) can be used to define productive capacity. Specifically, the research suggests that the limits used in establishing the capacity cost management system restrict the amount and nature of the information the system is capable of providing to management.