Purpose: This paper aims to empirically explore the effects of globalization, corruption perception, political stability, macroeconomic vulnerability and gross domestic savings on illicit financial outflows of 60 developing countries from 2004 to 2013.Design/methodology/approach: Pedroni’s heterogeneous panel data methodology for co-integration is applied. Panel unit root tests reveal non-stationarity of each variable in level, and a battery of seven panel co-integration tests largely confirm long-run equilibrium relationship among the variables under study.Findings: The panel vector error correction model estimates show that variables tend to converge toward long-run equilibrium at a very slow pace amid some short-term random fluctuations. At the same time, political stability reduces illicit financial outflows.Originality/value: There are enhancing impacts of globalization, corruption perception, macroeconomic vulnerability and domestic gross savings on illicit financial outflows. Political stability dampens such outflows. To the authors’ knowledge, such studies are either very scant or non-existent.

Retail environments are increasingly leveraging technology to streamline operations and improve efficiency. This paper presents ’Smart Shelves’, an innovative application that utilizes the Internet of Things (IoT) and machine learning (ML) to facilitate shelf replenishment. The application is designed to assist retail stock clerks in managing inventory more efficiently, reducing the potential for human error, and enhancing overall retail performance. The system’s effectiveness is highlighted by its 99.35% accuracy in distinguishing between products, even when dealing with near-identical product features that require restocking. The system’s operational efficiency is demonstrated by its 8.66-second average response time to issue notifications. A significant contribution of the proposed work is the development of a synthetic dataset that closely mirrors real-world retail conditions as obtaining a real-world dataset presented significant challenges. The findings demonstrate the transformative potential of IoT and ML technologies in the retail industry, particularly in the realm of shelf replenishment. Finally, this paper opens the door for future explorations that integrate the proposed system with existing inventory management software.