British University in Dubai

Purpose The use of service-oriented artificial intelligence-powered virtual agents (VAs) in higher education is expanding, particularly in academic advising. However, their adoption is hindered by challenges such as a lack of transparency, trust, system capability and organisational readiness. This study aims to investigate the factors influencing students’ behavioural intention to adopt academic advising VAs in Higher education. Design/methodology/approach This study applies an extended Fit–Viability Model (FVM) to investigate the fit requirements and viability of the advising VA system. The model incorporates perceived risk as a key predictor and examines the moderating role of demographic variables for this model. Survey data were collected from 239 students and analysed using partial least squares structural equation modelling to test the extended FVM framework. Findings The findings showed that both perceived fit and perceived risk significantly impact students’ behavioural intention to use advising VAs. Moreover, the study revealed that gender influences the relationship between risk and student intention, highlighting demographic influences on adoption. Practical implications This study provides practical implications for academic institutions seeking to enhance student adoption of advising VAs by addressing system fit and risk concerns. Theoretically, it contributes to adoption research by extending the FVM model with risk considerations and demographic moderators, offering a more comprehensive framework for future studies. Originality/value Unlike prior academic advising studies that rely primarily on acceptance-based models, this study extends the FVM by incorporating perceived risk and demographic moderators.

ABSTRACT Globally, solar energy initiatives are increasingly recognised not only as a means of decarbonisation but also as vehicles for promoting the circular economy (CE) principles throughout infrastructure life cycles. The expansion of solar installations and the associated material intensity require a transition to circularity to alleviate resource depletion, waste production and lifecycle emissions. Circularity redefines solar infrastructure as a sustainable material repository, enhancing asset value via predictive maintenance, component recovery and closed‐loop supply chains. Circular economy business models (CEBM) implement these practices by transitioning from single‐asset sales to service‐oriented, performance‐based frameworks. Thus, in this study, we sought to identify critical factors to improve a circular economy business model (CEBM) for solar projects. A structured quantitative survey was employed to gather data from 426 solar project practitioners across various worldwide locations, offering a cross‐geographical perspective on circularity in solar projects. The analysis encompassed multiple procedures, including model creation, measurement model estimation and structural model estimation. Structural equation modelling (SEM) was employed to evaluate the study's model. First, our findings empirically enhance the theory in the following three respects: by redefining circularity as a capability for configuring business models; by clarifying that sustainability outcomes are inherently integrated within energy organisations rather than merely added; and by conceptualising solar projects as transient yet transformative platforms that instantiate long‐term circular value logics. Secondly, we have empirically validated a ce MB that surpasses CE principles by focusing on organisational elements and project‐level implementation techniques, demonstrating how energy firms can incorporate CE value‐creation principles into the design and execution of solar projects.

The construction sector plays a significant role in the economic development of the United Arab Emirates (UAE) as an engine of growth attracting investors and participants from different parts of the world. The complexity of the construction industry coupled with the diverse nature of the participants in a typical construction project often leads to disputes. Mediation offers the parties an opportunity to resolve their disputes in a timely and cost-efficient manner while preserving their business relationships. Although study has shown that mediation is the second most utilized method of resolving construction disputes in UAE after negotiations, the main obstacle in its uptake has been the lack of enforceability of mediated settlement agreements across borders due largely to UAE’s nonmembership of the Singapore Convention on Mediation. The lack of a legal framework that provides for effective enforceability of mediated settlement agreements (MSAs) concluded in the UAE in foreign jurisdictions and vice versa acts as a disincentive to the use of mediation in resolving international business disputes including construction disputes in the UAE. Accession to the Singapore Convention will not only encourage parties to international construction contracts in UAE to resort to mediation as a preferred method of resolving their disputes but could potentially, strengthen the country’s competitive position as a center for not only international arbitration but also international commercial mediation as membership of the Convention makes the enforcement of MSAs concluded in the UAE in other jurisdictions and vice versa much easier.

• Advanced data analytics and PySpark improve fake product review detection. • Classification and clustering methods identify patterns and reduce deceptive reviews. • Visual data exploration aids anomaly detection in review characteristics. • Techniques protect consumers and foster trust in e-commerce platforms. • Study bridges theoretical research and practical spam detection applications. In e-commerce, online reviews significantly influence consumer purchasing behavior, with authenticity directly impacting business revenues and consumer trust. This study addresses the critical issue of fake product reviews (FPRs) by analyzing their effects on consumer choice and the overall reliability of online marketplaces. To improve FPR detection, we employed PySpark and advanced data science techniques to analyze a labeled dataset of user reviews, uncovering patterns and anomalies indicative of review manipulation. By integrating classification methods such as K-Nearest Neighbors (KNN), the study demonstrates how machine learning can effectively identify and mitigate the impact of FPRs, thereby enhancing the credibility of online reviews. The results contribute to ensuring fair competition, consumer protection, and the long-term integrity of digital commerce. Future research may expand this framework by incorporating additional datasets, alternative classification algorithms, and deeper linguistic or sentiment-based analyses.

Assistive technology plays a pivotal role in supporting individuals with developmental conditions by fostering independence and addressing needs across physical, cognitive, and communicative domains. Despite its benefits, widespread adoption remains limited due to high costs, usability challenges, and limited real-world applicability. This study offers a comprehensive overview of assistive technology's role in the context of developmental conditions by outlining key research areas and influential publications. Using a scientometric approach, we examined 1322 documents from Scopus and their 44,699 references. Co-citation analysis revealed three main thematic clusters and identified four particularly impactful publications, with the most influential authored by Lancioni and Singh (2014). A qualitative analysis of the clusters highlighted three recurrent research themes: (1) communication and mobility in individuals with profound developmental conditions; (2) cognitive functions and autonomy in individuals with developmental and intellectual conditions; and (3) communication and social cognition in autism. These areas reflect the increasing integration of assistive technologies into therapeutic, educational, and daily life contexts, enhancing quality of life, autonomy, and social participation. Emerging research also underscores the ethical need to design technologies that respect the preferences and lived experiences of individuals with developmental conditions, avoiding the imposition of neurotypical norms. Co-participation in design is gaining prominence, promoting more personalized, inclusive, and neurodiversity-oriented approaches.

Even though the process of integrating AI into the educational domain continues to increase, the literary analysis remains a difficult problem to automate because of the intricate and subtle character of literary works. The classic approaches do not necessarily represent the complicated literary devices, themes, and character development. In order to address the concerns, the paper uses AI based Automated Literary Analysis (AI-ALA) model which uses Generative Pretrained Transformer (GPT-4), a state of the art generative language model in order to automate literary analysis in high school literature courses. The fact that this method is interactive at the time of lesson and that the teacher works with pupils to assess literary tools such as metaphors and sarcasm as well as discuss the character and theme in a setting is revolutionary. The successful adjustment of the algorithm to a set of books with high school students is the guarantee of their adequacy and applicability in the field of education. The experiments indicate that GPT-4 is more precise and context-responsive as compared to rule-based. It is an in-depth examination of characters, themes, and literary strategies. The strategy, according to the statistics, can present teachers with an effective and scalable tool to enhance student learning. The concept simplifies the process of literary analysis through interactive interaction combined with questions and answers sessions. Literary analysis automation and personalization will break the existing boundaries and enhance literature classes teaching.