As the digitalization process has become more and more important in our daily lives, during recent decades e-commerce has greatly increased in popularity, becoming increasingly used, therefore representing an extremely convenient alternative to traditional stores. In order to develop and maintain profitable businesses, traders need accurate forecasts concerning their future sales, a very difficult task considering that these are influenced by a wide variety of factors. This paper proposes a novel e-commerce sales forecasting method that dynamically builds a Directed Acyclic Graph Neural Network (DAGNN) for Deep Learning architecture. This will allow for long-term, fine-grained forecasts of daily sales revenue, refined up to the level of product categories. The developed forecasting method provides the e-commerce store owner an accurate forecasting tool for predicting the sales of each category of products for up to three months ahead. The method offers a high degree of scalability and generalization capability due to the dynamically incremental way in which the constituent elements of the DAGNN’s architecture are obtained. In addition, the proposed method achieves an efficient use of data by combining the numerous advantages of its constituent layers, registering very good performance metrics and processing times. The proposed method can be generalized and applied to forecast the sales for up to three months ahead in the case of other e-commerce stores, including large e-commerce businesses.

To empirically prove that there are gender differences in the way online consumers react to online marketing communication attempts, the present study builds on a previously tested and validated empirical model showing the influence of online marketing communication on behavioral intentions by continuing in a SEM approach. Significant differences between male and female users are found and implications online communication practitioners are discussed, together with strategic proposals stemming from these novel results

Sustainable, improved rescue operations contribute to both the safety of traffic participants and the quality of transportation services. From this perspective, we considered the need to determine to what extent the target audience, consisting of owners and/or users of road vehicles, is prepared to purchase and use an eCall In-Vehicle System (eCall IVS) for passenger vehicles, using the Technology Acceptance Model (TAM) as a research framework. To confirm the hypotheses of our research, we resorted to structural equation modelling (SEM), based on the analysis of variance (PLS—partial least squares), and we were able to validate all the causal relationships of the conceptual model, with significant path coefficients at a chosen significance threshold of p < 0.01. Analyzing the obtained results achieved in the field of eCall IVS, one can state that the integration of these approaches with the TAM research framework gives a much more complete picture of a driver’s acceptance of using an aftermarket eCall IVS, which can also serve as a starting point in aftermarket vehicle implementation and future studies.

Ensuring a higher degree of road safety contributes both to the quality of transport services and to the level of the costs involved in rescue operations. The initiation points of managing a rescue operation are situational awareness on accident detection and the appropriate assessment of the required resources. The eCall in Vehicle System (eCall IVS) for passenger vehicles aims to minimize rescue team arrival times to accident sites, and meet assignment needs in the rescue chain. Implementation costs and benefits for the eCall IVS have been investigated, providing implications for rescue operations management. The findings show that the benefits of eCall IVS implementation outweigh the costs, and savings achieved in the rescue operations are obtained by shortening the time to reach the place of the accident and by efficient intervention through a more accurate allocation of the necessary resources, due to timely and relevant information.

Education plays an essential role in ensuring the social sustainability of blood donation. As altruism may be insufficient to support donor engagement, this study assesses the effectiveness of incentives on ensuring the social sustainability of blood donation. A self-administered questionnaire was used to collect data from 319 medical students about socio-demographic variables, donation frequency, altruism dimensions (impure, self-regarding, reluctant, egalitarian warm glow, and kinship), the perceived importance of monetary (travel compensation, meal vouchers) and non-monetary incentives (free blood screening, paid leave, refreshments, recognition gifts), and willingness to donate during a blood donation social marketing campaign in November 2021 and November 2022. Data were analyzed in SPSS 20 using chi-square, ANOVA tests, and multiple regression models. The key findings indicate no significant associations between donor categories and incentives, but meal vouchers, free medical testing, refreshments, and recognition gifts were linked to self-regarding altruism. Additionally, neither incentives, altruism dimensions, nor their interaction predicted willingness to donate blood. These findings highlight the need for education-driven approaches to ensure a long-term commitment of blood donors, by integrating educational, sustainable curricular or extracurricular activities. Integrating blood donation awareness into formal education may cultivate a culture of civic responsibility, expanding the donor pool and strengthening the social sustainability of blood donation.