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The rate of change in the world is increasing both in scope and magnitude by rapidly developing digital technologies. The challenging problem for performance measurement and management (PMM) in the digital era is twofold: firstly, the constant change in the external environment is compelling PMM to be more dynamic. Secondly, organisations have to deal with different varieties and volumes of data to create competitive advantage. The aim of this paper is to explore how PMM models and practices should be renovated to be resilient and reflect advances in the digital economies. Literature review on the state of the art was conducted covering the issues faced by organisations in the digital economies and their relevance to PMM. A case study was conducted to explore the practitioner perceptions of dealing with the issues faced in digital economies as well as to understand how they are making changes to their PMM. The key findings from the study include: (1) Organisations should refocus their measurement efforts to incorporate evaluation of their performance over a wider network involving various stakeholders. (2) Organisations need to understand how technological developments could create competitive advantage through their strategy and deploy it to relevant positivistic and behavioural measures. ARTICLE HISTORY

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This paper proposes the use of data available at Manchester Metropolitan University to assess the variables that can best predict student progression. We combine virtual learning environment (VLE) and management information systems student records datasets and apply the Random Forest (RF) algorithm to ascertain which variables can best predict students' progression. RF was deemed useful in this case because of the large amount of data available for analysis. The paper reports on the initial findings for data available in the period 2007–2008. Results seem to indicate that variables such as students' time of day usage, the last time students access the VLE and the number of document hits by staff are the best predictors of student progression. The paper contributes to VLE evaluation and highlights the usefulness of RF, a technique initially developed in the field of biology, in evaluating an educational and learning environment. Copyright © 2012 John Wiley & Sons, Ltd.

Managed Learning Environments (MLEs) in higher education institutions (HEIs) are relatively new to the arena of higher education, even though there are over 90% of institutions in the higher and further education sector who are currently engaged in some kind of MLE development activity (University of Brighton 2003). However, when it comes to the task of assessing the performance of an MLE there are no universally recognisable frameworks for evaluating MLEs in HEIs currently discussed within the literature. The paper advances a general systemic framework for evaluating MLEs based on Checkland's SSM and reports on the first stages of our attempt to evaluate the MLE at Manchester Metropolitan University involving the team developing the system and the stakeholders concerned. Two of three iterations of this research have been completed and, whilst outside the scope of this paper, have found that SSM has coped with the criteria demanded of the evaluation framework within its context. After completing a stakeholder analysis, the criteria for evaluating an MLE, based on the stakeholders' requirements, emerged. These iterations have tentatively concluded that by contextualising SSM to the evaluation requirements of an MLE in a UK HEI, the measures of performance suggested by SSM need to be adjusted. The final iteration will check this outcome.