Technical efficiency analysis using data envelopment analysis (DEA) and Tobit regression on convenience store networks in Indonesia

This study aimed to evaluate the technical operational efficiency of the convenience stores managed by PT ALFAMART, one of the largest retail chains in Indonesia. Performance disparities are still found among convenience stores, particularly in Region A—an area with high store density and intense retail competition. This research used a Data Envelopment Analysis (DEA) approach to measure the relative technical efficiency, as well as Tobit regression to analyse the influence of operational factors on efficiency scores. The sample consisted of 30 stores purposively selected from a total population of 90 homogeneous stores based on geographic distribution and variations in sales performance. The results showed that the average scale efficiency (SE) reached 98.8%, with VRSTE and CRSTE scores indicating the presence of technical inefficiencies. Tobit regression showed that operational hours have a positive influence on efficiency, while fast food and beverage services show a negative influence. Efficiency can be improved through the optimization of operational hours and the management of service diversification. This study contributes empirically to the literature on retail efficiency in developing countries, focusing on nationally scaled convenience store networks in densely populated urban environments. Additionally, these findings provide strategic insights for the management in formulating data-driven operational policies.This study provides empirical evidence on the operational efficiency of convenience stores managed by PT ALFAMART, a leading retail chain in Indonesia. By applying Data Envelopment Analysis and Tobit regression, the research identifies key factors influencing efficiency, including operational hours and service diversification. The findings offer actionable insights for optimizing store performance in highly competitive urban retail environments. This work enhances the understanding of retail efficiency in developing economies and supports data-driven decision-making in large-scale retail operations.