Abstract
Education 5.0 is a new paradigm in education posing many challenges and opportunities. This paper uses qualitative methods to explore students’ and teachers’ experiences with online learning to understand the challenges, benefits, and vision for a successful blended learning model, proposing a dynamic framework for blended learning. Results of in-depth interviews show the three main challenges of blended learning: pedagogical design, technological design, and environment/ setup design. Finally, the study discusses insights into future directions for developing Education 5.0, including the need for ongoing research, collaboration communities, curricula personalization, and innovation in the field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
This study examines the scientific contributions of the Higher Institute of Accounting and Administration at the University of Aveiro (ISCA-UA) from 2019 to 2022, focusing on how these align with Education 5.0 and Society 5.0 goals. Using a case study approach, data were collected from institutional records, analyzing publications by type and thematic focus, emphasizing areas that promote societal well-being, multiliteracy, and educational innovation. The methodology involves a mixed-methods approach: quantitative analysis assesses publication trends, distribution by faculty rank, and output frequency, while qualitative analysis identifies themes relevant to societal and educational advancements. This approach provides insights into how ISCA-UA's research aligns with Education 5.0 objectives, fostering both technical and socio-emotional skills needed for a super-smart society. Findings highlight an increase in publications addressing digital transformation, sustainability, and governance, reflecting the institution's adaptability and responsiveness to societal shifts, particularly noticeable during the COVID-19 pandemic. This emphasis supports Education 5.0s aims of preparing students with versatile skills for modern challenges. The study contributes to the academic literature by showing how higher education institutions can align research outputs with global educational frameworks, promoting interdisciplinary skills and social responsibility. Future research could explore the impact of these themes on curriculum design and student development, further supporting the evolution toward Education 5.0.

Abstract
This chapter examines how GenAI and predictive modelling strategies affect hyperpersonalised marketing. Through a comprehensive literature review and case studies, it examines hyper-p ersonalisation's theoretical frameworks, technical infrastructures, and ethical and governance issues. Large language models, generative adversarial networks, and diffusion models combined with advanced predictive analytics allow firms to scale real- time, highly individualised customer experiences. Effective implementation requires sophisticated data architectures, algorithmic transparency, and strong privacy protections. Integration complexity and ethical accountability are major barriers to consumer engagement and conversion, according to the research. Based on these findings, the chapter proposes an integrated framework that combines technological innovation with ethics and customer focus. This research advances marketing theory and provides practical advice for companies using AI- driven hyper-personalisation while maintaining consumer trust and regulatory compliance. © 2026, IGI Global Scientific Publishing. All rights reserved.

Abstract
In many e-commerce platforms user communities share product information in the form of reviews and ratings to help other consumers to make their choices. This study develops a new theoretical framework generating a bipartite network of products sold by Amazon.com in the category musical instruments, by linking products through the reviews. We analyze product rating and perceived helpfulness of online customer reviews and the relationship between the centrality of reviews, product rating and the helpfulness of reviews using Clustering, regression trees, and random forests algorithms to, respectively, classify and find patterns in 2214 reviews. Results demonstrate: (1) that a high number of reviews do not imply a high product rating; (2) when reviews are helpful for consumer decision-making we observe an increase on the number of reviews; (3) a clear positive relationship between product rating and helpfulness of the reviews; and (4) a weak relationship between the centrality measures (betweenness and eigenvector) giving the importance of the product in the network, and the quality measures (product rating and helpfulness of reviews) regarding musical instruments. These results suggest that products may be central to the network, although with low ratings and with reviews providing little helpfulness to consumers. The findings in this study provide several important contributions for e-commerce businesses' improvement of the review service management to support customers' experiences and online customers' decision-making.

Abstract
This chapter aims to discuss about the potential Return on Investment (ROI) measures from Artificial intelligence (AI) investments that business can leverage. It discusses the concepts and describes the dimensions, features and tools of AI investments in Marketing business, to assist the readers to understand about the topic. The authors also describe the major drivers of ROI measures for business applications and discusses the concerns and limitations of tangible measures. So, this document contributes to the literature on ROI (in)tangibles measures that leverage AI investments and features issues in digital marketing, at large and potentially offers a theoretical grounding for many empirical and theoretical future studies. © 2025 by IGI Global Scientific Publishing. All rights reserved.