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This paper discusses the effectiveness of a consortium-based student mobility programme by investigating the impact of the Asian International Mobility for Students (AIMS) Programme. AIMS is a regional multilateral large-scale student mobility programme based on a consortium of 10 member countries and 87 member universities with the Southeast Asian Ministers of Education Organization Regional Centre for Higher Education and Development (SEAMEO RIHED) as a facilitator. Over 6,000 students have participated in a semester-long intra-regional student exchange under AIMS since 2010.

The study employed questionnaire surveys and semi-structured interviews to investigate the impact of AIMS and its advantages as a consortium-based student mobility programme.

It was found that AIMS significantly impacted member universities by accelerating their internationalisation processes through increasing the number of inbound and outbound students and courses offered in English and so on. AIMS has promoted harmonisation among the members by developing common procedures and guidelines, providing platforms for mutual sharing of experiences and good practices and capacity building of international relations offices. AIMS has also had a significant impact on students by enhancing their regional identity and knowledge about the region of Asia, contributing to their development as future regional and global citizens. As advantages of AIMS, member universities efficiently built a foundation for international collaboration with common procedures and guidelines and shared their experiences through such venues as Annual Review Meetings. Students also feel supported by having clear guidance and find programmes prepared by host universities and SEAMEO RIHED useful.

This study is unique in that it empirically studies the impact of one of Asia’s largest student mobility programmes for the first time by analysing large-scale qualitative and quantitative data.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.