Contactless payments are being increasingly used with people demonstrating their interest in the payment technologyby waving or tapping their cards and devices at point-of-sale (POS) terminals across the world to make purchases. Eventhough the shift to contactless payments systems was already taking place before the COVID-19 outbreak, it is essential tostudy if the current crisis has accelerated this transformation. In this study, an online survey was used to examine the experience ofutilizing contactless payment technology in Saudi Arabia during the COVID-19 outbreak. We also investigated the factors thatcould affect this experience. The results showed that positive experiences in utilizing contactless payment technology have increased after the start of the COVID-19 pandemic. Furthermore, the study showed that health safety and hygiene were the mainreasons for using contactless technology for payment during the coronavirus crisis, while the security of transactions was the mainconcern in not using it for payments.

The rapid rise in the use of social media platforms has resulted in a recent surge of fake rumours, particularly among Arab countries. Such false information could potentially be detrimental to individuals and society. Detecting and blocking the spread of the fraudulent news in Arabic is critical. Many artificial intelligence algorithms, including contemporary transformer models, such as BERT, have been employed to detect the fake news in the past. Therefore, the fake news in Arabic can be detected using a revolutionary combined BERT architecture implemented in this paper. Extensive experiments were conducted to test the technique on real-world Arabic fake news datasets. In two of the fake news datasets, covid19fakes and Satirical, the suggested technique had a higher accuracy score than the current state-of-the-art Arabic fake news model. A comparable result can be achieved in other datasets; however, the proposed strategy fails to do so. All datasets except AraNews show an average F1 score improvement of 10% by implementing the proposed strategy. It was found that the proposed method was effective and superior to numerous other baselines of Arabic fake news detection models.

Gamification has emerged as a promising strategy to enhance engagement and learning outcomes in education. However, its adoption in developing countries faces persistent challenges. This study systematically reviews 43 research articles to identify key barriers to gamification adoption and provides new insights into their underlying causes. The findings highlight specific cultural misconceptions, such as the perception of gamification as mere entertainment rather than an educational tool, alongside language barriers and resistance to non-traditional teaching methods. Technological barriers, including limited access to digital devices, high costs, and low digital literacy, further hinder adoption. Institutional challenges, such as rigid curricula, insufficient teacher training, and a lack of supportive policies, exacerbate these issues. Unlike previous studies, this research not only categorizes these barriers but also provides a framework for addressing them. Specific policy recommendations include integrating gamification into national education strategies, subsidizing digital infrastructure, and designing teacher training programs focused on gamified pedagogy. By advocating for localized and context-sensitive gamification strategies, this study offers practical insights for educators, policymakers, and technology developers.

Purpose: This study explores barriers to IoT adoption in the circular economy (CE). While IoT enhances resource efficiency and automation, its implementation faces challenges. Existing research overlooks specific IoT-related barriers and their interconnections within CE. This study identifies key obstacles and examines their relationships using Fuzzy DEMATEL, helping managers make informed decisions under uncertainty to support effective CE adoption. Design/methodology/approach: Using Fuzzy DEMATEL, expert opinions on ten barriers were collected through pairwise comparisons to assess their interrelationships. The method quantifies both direct and indirect influences, distinguishing causal and effect barriers. This analytical approach aids managers in understanding complex barrier interactions to improve strategic decision-making in IoT-enabled CE implementations. Findings: Key barriers include high costs, privacy concerns, data-access issues, and resistance to change. These affect scalability, interoperability, and connectivity challenges. Addressing these will ease IoT integration. The study offers actionable insights and a structured framework to guide managers and policymakers in prioritizing barriers and designing targeted strategies for smoother IoT-driven circular economy transitions. Originality/value: This study uniquely focuses on IoT adoption barriers in CE, identifying causal relationships with Fuzzy DEMATEL to prioritize interventions. Unlike general Industry 4.0 studies, it highlights IoT-specific challenges and their interdependencies, supporting managers and researchers in navigating complexity and uncertainty. The findings provide a valuable foundation for accelerating sustainable, IoT-driven circular economy adoption.

Victims and witnesses of cybercrime often hesitate to report incidents due to concerns over privacy, complexity, and fear of retaliation. Traditional reporting mechanisms require manual data entry, creating accessibility barriers and delaying response times. To address these challenges, this paper introduces ReACT_OCRS, an AI-driven voice-based cybercrime reporting system that allows victims and witnesses to anonymously submit complaints through audio recordings. Leveraging speech recognition transformers, a recent language model, and encryption, the system processes real-time multilingual voice inputs, extracts meaningful content, and classifies reports with high precision using a hybrid voting mechanism. Experimental evaluations on synthetically generated and human-validated datasets confirm the system’s ability to accurately transcribe, classify, and securely process audio complaints while preserving user anonymity. This work improves cybercrime reporting by making it more accessible, efficient, and secure, fostering greater participation from victims and witnesses.