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New logistic regression approach for identification factors affecting the partition of costs and risk in the international trade

Sternad Marjan, Dragan Dejan

LogForum

2024

Vol. 20, no. 4

483--496

Background: The selection of the right Incoterms is crucial for minimising risks and costs in international trade. This paper aims to develop a model that identifies the key factors influencing the selection of Incoterms. The main contribution of the research is the creation of a new statistical modelling process that effectively identifies the variables impacting trade costs and risk. The study uses import and export data from non-EU countries in the context of a Slovenian case study. Methods: A novel model selection mechanism is developed, combining the logistic regression (logit) modelling with Monte Carlo simulations to identify influential factors in Incoterms selection. This mechanism incorporates heuristic techniques, which guide a sequential process of gradually searching through logit model candidates to determine the best-fit model for both import and export scenarios. Results: The application of the new logit modelling procedure reveals that the delivery location is the most significant factor affecting Incoterms selection. Additionally, the value of goods and the type of transport (containerised vs. non-containerised) also have a considerable influence. For imports, the mass of goods is found to be a significant factor as well. Conclusions: The research results offer valuable insights for companies formulating their international business strategies. By selecting the appropriate Incoterm, companies can reduce transportation risks and costs. Managing costs and risks is especially important for higher-value goods. The research finds that, for lower-value goods, sellers often take on associated costs and risks. This pattern is particularly notable with imports, where sellers are more likely to assume responsibility for lighter-weight goods.