Month: May 2020

We have published this paper in the journal Simulation: Transactions of the Society for Modeling and Simulation International. DOI: https://doi.org/10.1177/00375497241296542

ABSTRACT:

Business-to-Consumer (B2C) e-commerce has become a dominant, continuously evolving force in global retail. Most consumers today make cross-border purchases in marketplaces, while enterprises have found great opportunities, and competition has become increasingly fierce. To understand B2C properties is an important endeavor. This paper achieves two goals: it develops a method to prove tendencies in a simulation and shows the oligopolistic nature of cross-border B2C e-commerce marketplaces. These achievements enhance the understanding of cross-border B2C e-commerce, by employing novel approaches: Social simulation, agent-based modeling (ABM) tools, theorem-proving techniques, and scenario analysis. The proving method began with an experimental design to explore the model’s dynamics for parameters defined for specific scenarios of interest, and the agents’ options were randomly selected for a significant number of runs (Monte Carlo experiment). This procedure allowed us to identify and prove the necessity of a tendency (the oligopolistic character of cross-border B2C e-commerce) and determine the factors that drive it. In the second part of the proof procedure, the persistence, independence of the agents’ choices, and scaling validity of the tendency were shown by significantly increasing the number of random experiments and the number of simulated agents. The model was also validated. The developed method satisfactorily addresses some challenges of theorem-proving. In all these experiments, the variable of interest was market share. The resulting order of influence of the factors driving market share was recognition, product and service attributes of the marketplaces, and word of mouth. Surprisingly, word of mouth was the least important factor.

We have published in the journal Environmental Science & Policy. DOI: https://doi.org/10.1016/j.envsci.2024.103856

ABSTRACT:

The science, technology, engineering, and mathematics (STEM) education research delves into the core of sustainable development goals (SDGs), including the pillars of quality education (SDG4), robust economic growth
(SDG8), and diminished inequalities (SDG10). These pursuits stand as keystones in sculpting inclusive societies and bridging societal gaps. While previous studies utilising data envelopment analysis (DEA) have explored educational performance mainly from a macro-perspective, there is a lack of micro-perspective investigation. Our study aims to fill this gap by proposing a DEA approach to assess the relative efficiency of engineering classes. We analysed 70 classes covering 38 subjects in the first semester of 2022 at a South American school. Methodologically, we employed the slack-based measure (SBM) model under the benefit of doubt (BoD) condition. Unlike prior research, we analysed classes’ relative performance considering different pedagogical approaches active-learning classes (15.7 %) and 59 passive-learning classes (84.3 %). Our results showed that 18 classes were efficient (25.7 %). Active classes were more efficient, but few subjects maintained similar efficiencies for all classes. Moreover, efficient classes were concentrated in the last two years prior to graduation (57.9 %). This may represent an additional barrier for low-income students, who tend to drop out in the first years. The findings support several improvement recommendations, such as integrating digital technologies, boosting active learning opportunities, and bolstering classes in foundational subjects. Also, implications for researchers, decision- and policy-makers are discussed. Our approach can be replicated in diverse educational contexts, enabling the identification of strengths and weaknesses for more efficient educational management.