Adoption of a probabilistic network model investigating country risk drivers that influence logistics performance indicators

Abstract

There is a two-way interaction between the logistics performance and economic development of a country. The World Bank Group regularly publishes the Logistics Performance Index (LPI), which is a composite measure of the country-level logistics performance comprising six indicators, namely customs, infrastructure, international shipments, logistics competence, timeliness, and tracking and tracing. This study aims to explore dependencies among different drivers of country risk, including business environment, corruption, economic, environmental, financial, health and safety, and political risks, and the LPI indicators using a data-driven Bayesian Belief Network model. This study has made two unique contributions to the literature on environmental impact assessment. First, it explores dependencies among various country risk drivers and LPI indicators in a probabilistic network setting while mapping cause-effect relations between the input and outcome LPI indicators. Second, this study operationalizes a new data-driven methodology to help researchers and practitioners identify critical risk drivers influencing the LPI indicators. The results indicate a moderate to a strong correlation between individual risks and LPI indicators. Logistics competence is the most critical indicator impacting international shipments, timeliness, and tracking and tracing. On the other hand, economic and financial risks significantly impact all six LPI indicators.