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Prediction of maritime logistics service risks applying soft set based association rule: An early warning model

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  • Jia, Xiaohui
  • Zhang, Donghui

Abstract

Risk management has been an increasing concern in maritime logistics services due to higher uncertainty. This study aims to build an early warning model for mitigating maritime logistics service risks by empirically examining the roles of information risk events in stimulating other risks. The risk factors were identified through literature review combined with an email survey and measured by a website survey on the experts in the maritime logistics companies. The results derived from the mined association rules based soft set theory present information risk can trigger all the other types of risk, of which information security risk is the most influential event. The risk of governing uncertainty triggered by information inaccuracy or information delay is distributed in the green warning level as well as the risk of uncertain inventory and contract disputes triggered by information inaccuracy, and the others in the yellow warning level. Moreover, it is demonstrated that the effective application of the approach of soft set based association rule to mining interrelationship between risk sets. The findings can help the maritime practitioners to make risk mitigation policies and encourage scholars to conduct more risk investigation in the systematic perspectives.

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  • Jia, Xiaohui & Zhang, Donghui, 2021. "Prediction of maritime logistics service risks applying soft set based association rule: An early warning model," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:reensy:v:207:y:2021:i:c:s0951832020308310
    DOI: 10.1016/j.ress.2020.107339
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    8. Alptekin Ulutaş & Ieva Meidute-Kavaliauskiene & Ayse Topal & Ezgi Demir, 2021. "Assessment of Collaboration-Based and Non-Collaboration-Based Logistics Risks with Plithogenic SWARA Method," Logistics, MDPI, vol. 5(4), pages 1-14, November.
    9. Kai Yu & Lujie Zhou & Pingping Liu & Jing Chen & Dejun Miao & Jiansheng Wang, 2022. "Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management," Mathematics, MDPI, vol. 10(21), pages 1-20, October.

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