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

dc.contributor.author Qazi, Abroon
dc.contributor.author Simsekler, Mecit Can Emre
dc.contributor.author Formaneck, Steven
dc.date.accessioned 2022-01-18T09:59:20Z
dc.date.available 2022-01-18T09:59:20Z
dc.date.copyright © 2021
dc.date.issued 2022
dc.description This article is not available at CUD collection. The version of scholarly record of this article is published in Kybernetes (2021), available online at: https://doi.org/10.1108/K-08-2021-0773 en_US
dc.description.abstract 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. © 2021, Emerald Publishing Limited. en_US
dc.identifier.citation Qazi, A., Simsekler, M. C. E., & Formaneck, S. (2022). Impact assessment of country risk on logistics performance using a bayesian belief network model. Kybernetes, https://doi.org/10.1108/K-08-2021-0773 en_US
dc.identifier.issn 0368492X
dc.identifier.uri https://doi.org/10.1108/K-08-2021-0773
dc.identifier.uri http://hdl.handle.net/20.500.12519/498
dc.language.iso en en_US
dc.publisher Emerald Group Holdings Ltd. en_US
dc.relation Authors Affiliations : Qazi, A., School of Business Administration, American University of Sharjah, Sharjah, United Arab Emirates; Simsekler, M.C.E., Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Formaneck, S., Faculty of Management, Canadian University of Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseries Kybernetes;
dc.rights This article is © Emerald Publishing Limited and permission has been granted for this version to appear here (https://repository.cud.ac.ae/). Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Publishing Limited.
dc.rights.holder Copyright : © 2021, Emerald Publishing Limited.
dc.subject Bayesian Belief Network en_US
dc.subject Business environment en_US
dc.subject Economic en_US
dc.subject Financial en_US
dc.subject Health and safety risks en_US
dc.subject Logistics performance index en_US
dc.subject Political en_US
dc.title Impact assessment of country risk on logistics performance using a Bayesian Belief Network model en_US
dc.type Article en_US
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