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Several studies have shown that the mechanism of labour-intensive construction (LIC) projects can mitigate high unemployment and create skilled development, especially in developing nations. The guidelines and practices for implementation may have faced some encumbrances in some countries. Whether the current guidelines and practices for municipal infrastructure support agent (MISA) to execute LIC projects face hindrances in South Africa has yet to receive in-depth studies. Thus, this study attempts to proffer policy solutions to improve the proposed revised guidelines and practices for MISA in LIC project execution in South Africa.

The study's objectives were accomplished via a combination of 16 virtual interviews of built environment professionals and government officials involved in LIC project execution in South Africa and supported by the analysed documents. A thematic approach was used to analyse the data and presented two main themes.

Findings show lax enforcement of discretionary funds, lax institutional capacity and inadequate individual skills, among others, as the gaps in existing South Africa's LIC guidelines and practices. Also, policy solutions to address the gaps were proffered.

The suggested feasible policies will improve the proposed revised guidelines and practices for MISA in LIC project execution in South Africa. This guide will promote the development of individual skills, institutional capacities and increase employment across South Africa.

This study promotes the use of LIC to create employment and contribute to proffering measures that will improve the proposed revised third edition of the guidelines and practices for MISA to execute LIC.

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.