Cross-border B2C e-commerce to China

Purpose

The purpose of this paper is to support companies’ risk-informed selection of a logistics solution to operate in China via cross-border e-commerce (CBEC).

Design/methodology/approach

Decision theory is applied to the recent field of CBEC. This theoretic setup involves a decision maker who must choose among a set of alternatives, whose consequences depend on uncertain factors (Savage, 1954). The study develops an activity-based model to calculate logistics costs in a deterministic setting. Simulations and probabilistic sensitivity analyses are later performed to evaluate the impact of uncertainty.

Findings

There are four main solutions to enter China, determined by the adopted international transport mean and the presence of a local warehouse. The most important risk factors affecting the choice of the logistics solution are change of CBEC regulation, product value, expected service level and demand level.

Originality/value

From a theoretical perspective, this study improves CBEC literature, so far characterised by descriptive papers, often lacking industry focus or empirical exploration. It also provides new application opportunities for decision theory, whereas previous contributions have proposed different theoretical approaches, such as transaction cost or institutional theory. From a practical viewpoint, the paper is the first to compare the costs of the main logistics solutions to sell online to China, by taking uncertainty into account. The results can be used to better understand the differences among solutions and identify the most critical parameters. Finally, this research provides some observations for policy implementation.

A Common Rationale for Global Sensitivity Measures and Their Estimation

Measures of sensitivity and uncertainty have become an integral part of risk analysis. Many such measures have a conditional probabilistic structure, for which a straightforward Monte Carlo estimation procedure has a double-loop form. Recently, a more efficient single-loop procedure has been introduced, and consistency of this procedure has been demonstrated separately for particular measures, such as those based on variance, density, and information value. In this work, we give a unified proof of single-loop consistency that applies to any measure satisfying a common rationale. This proof is not only more general but invokes less restrictive assumptions than heretofore in the literature, allowing for the presence of correlations among model inputs and of categorical variables. We examine numerical convergence of such an estimator under a variety of sensitivity measures. We also examine its application to a published medical case study.