In today’s fast-moving, disrupted business environment, supply chain risk management is crucial. More critically, Industry 4.0 has conferred competitive advantages on supply chains through the integration of digital technologies into manufacturing and logistics, but it also implies several challenges and opportunities regarding the management of these risks. This paper looks at some ways emerging technologies, especially Artificial Intelligence (AI), help address pressing concerns about the management of risk and sustainability in logistics and supply chains. The study, using a systemic literature review (SLR) backed by a mapping study based on the Scopus database, reveals the main themes and gaps of prior studies. The findings indicate that AI can substantially enhance resilience through early risk identification, optimizing operations, enriching decision-making, and ensuring transparency throughout the value chain. The key message from the study is to bring out what technology contributes to rendering supply chains resilient against today’s uncertainties.

Nowadays, customer service in telecommunications companies is often characterized by long waiting times and impersonal responses, leading to customer dissatisfaction, increased complaints, and higher operational costs. This study aims to optimize the customer service process through the implementation of a Generative AI Voicebot, developed using the SCRUMBAN methodology, which comprises seven phases: Objectives, To-Do Tasks, Analysis, Development, Testing, Deployment, and Completion. An experimental design was used with an experimental group and a control group, selecting a representative sample of 30 customer service processes for each evaluated indicator. The results showed a 34.72% reduction in the average time to resolve issues, a 33.12% decrease in service cancellation rates, and a 97% increase in customer satisfaction. The implications of this research suggest that the use of Generative AI In Voicebots can transform support strategies in service companies. In conclusion, the implementation of the Generative AI Voicebot has proven effective in significantly reducing resolution time and markedly increasing customer satisfaction. Future research is recommended to further explore the SCRUMBAN methodology and extend the use of Generative AI Voicebots in various business contexts.

This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.

This study investigates the escalating complexity and unpredictability of global supply chains, with a particular emphasis on resilience in the agricultural sector of Antioquia, Colombia. The aim of the study is to identify and analyze the dynamic capabilities, specifically flexibility and adaptability that significantly enhance resilience within agri-food supply chains. Given the sector’s vulnerability to external disruptions, such as climate change and economic volatility, a thorough understanding of these capabilities is imperative for the formulation of effective risk management strategies. This research is essential to provide empirical insights that can inform stakeholders on fortifying their supply chains, thereby contributing to enhanced competitiveness and sustainability. By presenting a comprehensive framework for evaluating dynamic capabilities, this study not only addresses existing gaps in the literature but also offers practical recommendations aimed at bolstering resilience in the agricultural sector.

This study explored the relationships between green market orientation and competitive advantage, with a particular focus on the mediating role of green sustainable innovation. The research utilized a structured questionnaire to gather data from managers involved in environmental protection and professionals working in the manufacturing sectors of computers, electronics, optical products, and electrical equipment. The survey targeted respondents from key regions in Saudi Arabia, including Riyadh, Qassim, and the Eastern Province, resulting in a total of 273 responses. The collected data were analyzed using structural equation modeling (SEM), a robust statistical technique that allows for the examination of complex relationships between variables. The findings confirmed a mediational model where green sustainable innovation—comprising both green product and green process innovation—served as a critical intermediary linking green market orientation to competitive advantage. Furthermore, the study validated direct effects of green market orientation on both green sustainable innovation and competitive advantage. These results emphasize the dual pathways through which green market orientation influences business performance. The research concludes by offering actionable insights for Saudi managers, highlighting strategies to maximize profitability and competitiveness through the adoption and implementation of green sustainable innovation practices.

Balancing broad learning outcomes in graduate programs with detailed classroom learning outcomes is increasingly crucial in education systems. This study employs a qualitative paradigm through a case study method to address the gap between learning outcomes at the graduate program level and those at the course level. Using the ESSENTIA CURRICULUM framework—a curriculum design methodology derived from software engineering practices—we propose an innovative and adaptable approach for aligning program-wide and course-specific learning outcomes. The ESSENTIA CURRICULUM, named for its focus on the “essence of the curriculum”, is applied to the ICT for Research course within the M.Sc. program in University Teaching at the University of Nariño. This framework fosters a consistent educational journey centered on learning achievements and demonstrates its effectiveness through a comprehensive self-assessment process and stakeholder feedback. The implications of this research are twofold: it highlights the potential of adopting interdisciplinary methodologies for curriculum design and provides a scalable and alternative strategy for harmonizing learning outcomes across diverse educational contexts. By bridging principles from software engineering into education, this novel approach offers new avenues for improving curriculum coherence and applicability.