• 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.

The high cost, inherent risks, and limited accessibility of scuba diving significantly restrict public participation in underwater tourism activities. Virtual Reality (VR) technology offers a promising solution by providing safe, cost-effective, and immersive alternatives for experiencing underwater environments. This study developed and evaluated a VR scuba diving simulator designed to educate tourists about underwater activities and enhance their knowledge and perceptions of marine tourism. Using the Multimedia Development Life Cycle (MDLC) methodology, an immersive VR scuba diving application was systematically developed through six phases: concept, design, material gathering, assembly, testing, and distribution. The application was validated by three experts (one instructional design professional and two VR developers) and aligned with POSSI (Persatuan Olahraga Selam Seluruh Indonesia) Level 1/Open Water certification standards. Two hundred tourists (mean age 26.4 years, 52% male, 48% female; 38% had prior diving experience) participated in testing the VR Scuba Simulator to assess its educational impact on underwater tourism perception. A structured questionnaire measured three perception dimensions: cognitive (knowledge), affective (emotional), and conative (behavioural intention). Results revealed exceptionally high tourist perceptions across all dimensions, with an overall mean score of 92.41%. The cognitive dimension achieved 93.19%, indicating strong knowledge acquisition about scuba diving and underwater ecosystems. The affective dimension scored 93.59%, demonstrating positive emotional engagement and environmental appreciation. The conative dimension reached 90.46%, suggesting strong behavioural intention to participate in actual scuba diving activities. These findings confirm that the VR scuba diving simulator effectively enhances tourist understanding, emotional connection, and behavioural motivation toward underwater tourism. The consistently high scores across all perception dimensions demonstrate VR technology's significant potential as an educational and promotional tool for sustainable marine tourism development.

Integrating Internet of Things (IoT) technologies in healthcare has significantly enhanced service delivery and also introduced critical cybersecurity vulnerabilities, including ransomware attacks, data tampering, and unauthorised access. To address these challenges, this study proposes a Digital Twin-Enhanced Cybersecurity Framework (DT-CF) tailored for healthcare IoT systems within Industry 4.0 environments. The DT-CF employs real-time digital twins to generate synchronised replicas of physical devices (e.g., smart infusion pumps, wearable sensors), enabling continuous monitoring and predictive anomaly detection through machine learning models. The framework integrates Hyperledger Fabric blockchain and smart contracts to enforce secure access control and ensure data integrity. A conceptual case study, designed to mirror the operational dynamics of a modern healthcare facility, is presented to illustrate the framework’s practical applicability, highlighting its potential to enhance system reliability and support automated threat mitigation. Key contributions include a scalable DT-CF architecture, AI-driven analytics, and a step-by-step implementation guide for deploying context-aware digital twins in clinical environments. By bridging real-time physical–virtual synchronisation with proactive cybersecurity measures, the DT-CF offers a resilient and transparent solution to safeguard sensitive patient data and critical healthcare infrastructure.

• DESI framework customized for Kazakhstan’s unique digital needs. • Kazakhstan’s digital rise to 78th in Global Innovation Index analyzed. • Study reveals key gaps in human capital, infrastructure, and technology. • Mixed-method approach ensures a reliable, sector-wide digital assessment. • Aligning global standards with local strategies for sustainable growth. As digital transformation reshapes economies worldwide, the ability to meaningfully assess digital readiness has become essential for strategic development. This study develops a context-sensitive digital readiness framework, the Kazakhstan Digital Readiness Index (KZ-DRI), adapted from the European Union's Digital Economy and Society Index (DESI), to evaluate digital transformation progress in Kazakhstan. Following a systematic framework development approach and grounded in both national indicators and enterprise-level data, the study addresses two core challenges: reducing redundancy in the original DESI structure and aligning the framework with Kazakhstan’s distinct socio-economic, institutional, and infrastructural context. While the KZ-DRI framework reveals that Kazakhstan's overall digital readiness approximates the European average, disaggregated results expose sharp disparities across dimensions. The country demonstrates exceptional progress in digital public services, driven by early institutional prioritization and centralized implementation capacity. Conversely, persistent challenges in digital skills development and infrastructure underscore the need for targeted policy interventions to achieve balanced, inclusive digital growth. This research contributes a systematically developed framework (KZ-DRI) that preserves international comparability while providing a replicable methodology for other emerging economies. By highlighting structural asymmetries and strategic leverage points, the study advances a roadmap for integrated, future-oriented digital governance—one capable of architecting economic competitiveness, societal resilience, and regional digital leadership.