Impact assessment of country risk on logistics performance using a Bayesian Belief Network model

Purpose: This paper aims to assess the impact of different drivers of country risk, including business environment, corruption, economic, environmental, financial, health and safety and political risks, on the country-level logistics performance. Design/methodology/approach: This study utilizes three datasets published by reputed international organizations, including the World Bank Group, AM Best and Global Risk Profile, to explore interactions among country risk drivers and the Logistics Performance Index (LPI) in a network setting. The LPI, published by the World Bank Group, is a composite measure of the country-level logistics performance. Using the three datasets, a Bayesian Belief Network (BBN) model is developed to investigate the relative importance of country risk drivers that influence logistics performance. Findings: The results indicate a moderate to a strong correlation among individual risks and between individual risks and the LPI score. The financial risk significantly varies relative to the extreme states of the LPI score, whereas corruption risk and political risk are the most critical factors influencing the LPI score relative to their resilience and vulnerability potential, respectively. Originality/value: This study has made two unique contributions to the literature on logistics performance assessment. First, to the best of the authors’ knowledge, this is the first study to establish associations between country risk drivers and country-level logistics performance in a probabilistic network setting. Second, a new BBN-based process has been proposed for logistics performance assessment and operationalized to help researchers and practitioners establish the relative importance of risk drivers influencing logistics performance. The key feature of the proposed process is adapting the BBN methodology to logistics performance assessment through the lens of risk analysis.