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The purpose of this paper is to explore the domain of metaverse commerce and conduct a thorough examination of the complex dynamics that contribute to impulsive purchasing behavior. This study aims to examine the impact of vividness, interactivity and effectiveness on social presence and telepresence within the metaverse, a digital landscape. Specifically, it seeks to understand how these factors influence consumers' impulsive buying behavior.

The methodology used in this study consisted of distributing self-administered questionnaires via a survey. Data collection was conducted among a targeted sample of 348 participants in Vietnam who had direct experience with metaverse commerce services. Then, the collected data was subjected to analysis using two distinct methodologies: partial least squares structural equation modeling and artificial neural networks.

The findings of this study provide significant insights into the correlation between social presence, telepresence and impulsive buying behavior within the field of metaverse commerce. The research findings also indicate that the impact of social presence and telepresence on impulsive purchasing behavior is contingent upon the enhanced vividness, effectiveness and interactivity of the virtual environment.

The present investigation unveiled a range of linear and non-linear mechanisms that elucidate the functions of effectiveness, vividness and interactivity in facilitating the complex interplay between social presence, telepresence and impulsive buying behavior in the context of metaverse commerce. The study provides both theoretical and practical contributions to the existing body of literature on Metaverse commerce.

To achieve the Sustainable Development Goals (SDGs) in the era of Logistics 4.0, machine learning (ML) techniques and simulations have emerged as highly optimized tools. This study examines the operational dynamics of a supply chain (SC) in Vietnam as a case study utilizing an ML simulation approach.

A robust fuel consumption estimation model is constructed by leveraging multiple linear regression (MLR) and artificial neural network (ANN). Subsequently, the proposed model is seamlessly integrated into a cutting-edge SC simulation framework.

This paper provides valuable insights and actionable recommendations, empowering SC practitioners to optimize operational efficiencies and fostering an avenue for further scholarly investigations and advancements in this field.

This study introduces a novel approach assessing sustainable SC performance by utilizing both traditional regression and ML models to estimate transportation costs, which are then inputted into the discrete event simulation (DES) model.