In an era in which e-commerce has become an integral facet of our lives, the delivery process has evolved into a vital part of online shopping. As the sole tangible interaction in an otherwise digital undertaking, it forms a decisive aspect of consumers’ e-commerce experience. In the face of the ever-growing prevalence of e-commerce, the ecological footprint left by delivery practices has emerged as a pressing concern. This focus is attributed not only to the environmental externalities resulting from e-commerce delivery but also due to the system’s inherent inefficiencies. As the primary beneficiaries of e-commerce, consumers form a pivotal force in driving sustainable delivery initiatives. This study embarks on an exploration of consumers’ attitudes toward environmentally friendly e-commerce-delivery practices. We aim to investigate whether consumers are willing to bear additional costs for assured green deliveries and identify which sustainable practices they perceive as most effective. A survey was conducted among e-commerce users to show their readiness to incur higher costs for green delivery and accept delayed deliveries if they supported sustainability. However, our findings reveal a conflicting scenario. While consumers display a readiness to opt for green deliveries, the price and speed of delivery emerged as more critical considerations. This study also uncovers significant discrepancies in attitudes towards green delivery based on demographic factors, such as gender and generation. Utilizing multivariate logistic regression, the study outlines the main factors that predict a willingness to pay for guaranteed green delivery. This study’s findings stress the need to balance the drive for speed and cost-effectiveness in e-commerce delivery with the urgency to adopt sustainable practices. It underscores the necessity for e-commerce platforms and logistics partners to take the lead in crafting and implementing innovative green delivery solutions while educating and incentivizing consumers to choose sustainable delivery options.

Decentralization techniques have been radically changed during the last few years thanks to new decentralized technologies, such as the blockchain. Blockchain-based Online Social Media (BOSMs) have been proposed as the future of Social Media where users are rewarded for their valuable content. Even if these platforms are used by millions of users, they are not fully decentralized. The next generation of Social Media requires the decentralization as the primary characteristic. HELIOS is a decentralized social media platform that addresses the dynamic nature of human communications, and it includes techniques such as decentralisation, context detection in IoT environment, real and virtual object networking, peer-to-peer based content streaming and validation. The paper defines a rewarding system that incentives and engages existing and potential users on the use of the HELIOS Platform, by exploiting the main characteristics of current rewarding strategies applied to BOSMs. The paper proposes an analysis of current blockchain technologies, and the main characteristics of a rewarding system for a decentralized Social Media. The framework is based on the Quorum Network.

Artificial intelligence (AI) is transforming higher education, although most research addresses its integration in terms of frequency of use or technological acceptance, without examining how it translates into specific curricular and instructional decisions. That is why this study has a dual aim: to develop and validate the AI Instructional Design Questionnaire for Critical Thinking (AID-CTQ) and to analyze how university faculty integrate AI into instructional design practices in higher education. The sample included 144 faculty members from a university in Madrid, selected by convenience. Exploratory and confirmatory factor analyses of the questionnaire supported a three-factor structure: Activity Design (F1), Critical Thinking Assessment (F2), and Self-Regulation and Reflection (F3). The final 12-item model shows good model fit (CFI = 0.98, TLI = 0.98, RMSEA = 0.05, SRMR = 0.05) and adequate overall reliability (α = 0.86). At the item level, responses related to assessment and reflective practices showed consistently high agreement, whereas items linked to activity design displayed greater variability. Faculty members with more than 10 years of experience obtained significantly higher scores, indicating that the educational value of AI depends less on the tools used and more on the quality of instructional decisions. Reported use of AI was high, with ChatGPT and Copilot being the most frequently used tools. Overall, the findings indicate that the integration of AI in higher education is evolving from predominantly instrumental uses toward more pedagogical and curriculum-oriented forms of implementation. Accordingly, the educational value of AI lies less in the tool itself than in the quality of the instructional decisions through which it is meaningfully embedded in the curriculum.

Artificial intelligence (AI) is commonly used in university academic work, yet existing instruments mainly assess AI literacy, readiness, or levels of permitted use rather than the critical-thinking processes students activate when working with AI. This study developed the Critical Thinking with AI Scale for Students (CTAI-S) and provided initial psychometric evidence for its use. An initial 15-item questionnaire was administered to 271 university students, and one item was removed after item analysis. Parallel analysis favored one predominant factor (KMO = 0.927), and a theory-guided three-factor representation was also examined. Ordinal confirmatory factor analyses (WLSMV; n = 257) yielded scaled CFI = 0.964 and RMSEA = 0.072 for the one-factor model, and CFI = 0.977 and RMSEA = 0.059 for the equivalent correlated-three-factor and second-order models; robust indices were less favorable, and latent correlations were very high (0.85–0.88). Total-score reliability was high (ω = 0.898), whereas the third grouping showed low reliability (ω = 0.557). Mean responses were above the scale midpoint (M = 3.97), with Reflective Judgment and Self-Regulation being highest (M = 4.07). The CTAI-S is best interpreted as a global self-report measure of critical thinking in AI-mediated academic tasks; independent validation is needed before dimensional or diagnostic applications.

Sustainability of urban logistics or last-mile logistics (LML) has been extensively studied from the perspective of experts and professionals whose objectives and interests have been closely linked to their organisations. For this reason, the conclusions drawn from these studies have had an inherent bias that is difficult to overlook. The aim of this study is to identify perceived research priorities in sustainable urban logistics through an innovative approach based on weightings established by a ‘university community’. To carry out this study, a hybrid multi-criteria model is proposed, used to weight twelve criteria employed to compare fourteen categories grouping the lines of research in LML. The main contributions of this work have therefore been: to provide an atypical perspective grounded in the ‘university community’, and to design a hybrid multi-criteria model capable of efficiently forecasting which perceived research priorities will be most relevant in the future in LML. The most significant conclusions of this study highlight the strong value that the ‘university community’ assigns to research areas in LML focused on collaborative workstreams among stakeholders. This is understood as a systemic and strategic approach grounded in the implementation of urban logistics infrastructures that enhance process efficiency through regulatory and collaborative mechanisms aimed at increasing the sustainability of LML.

One of the fundamental services for a city is the existence of an efficient urban distribution of goods that allows the needs of its citizens and businesses to be met. This service is known as last-mile urban logistics. A little-studied approach, which this work has set as a priority objective, is to determine how this logistics is strongly related to certain relevant economic factors for the companies that provide the service, the public authorities that regulate it, and the citizens who must live with it.