This study investigates the implications of social engineering attacks on mobile device security in the UAE, situating these threats within broader socioeconomic challenges. It employs a mixed methodology to understand both quantitative patterns and qualitative experiences of mobile device users across the seven emirates. The study combines large-scale survey data from 1,247 respondents with 45 in-depth qualitative interviews conducted between November 2024 and February 2025 to examine mobile-based social engineering vulnerabilities in the UAE. It examines demographic factors, cultural influences, technology adoption patterns, and vulnerability to various social engineering attack vectors, including SMS phishing, voice phishing, and mobile application-based deception. The results indicate that social engineering attacks are widespread among UAE mobile users. Age, educational background, and cultural factors, such as trust in authority and collectivist communication trends, influence vulnerability. Qualitative analysis outlines technological empowerment, cultural trust patterns, language barriers in security messaging, and the generational digital divide, as well as inadequate security awareness programs, as critical themes. The results also prove that social engineering attacks targeting mobile devices in the UAE exploit both universal human psychology and culture-specific trust patterns, thus deepening existing socioeconomic challenges related to digital inequality and cyber risk exposure. The study further reemphasizes the need for policymakers to develop culturally adapted security education programs that address the intersection of mobile security vulnerabilities and socioeconomic challenges to build a more resilient digital environment.

Contemporary organizations increasingly leverage artificial intelligence technologies to revolutionize human resource management practices, fundamentally altering how companies approach workforce optimization. This empirical investigation examines critical determinants and consequences of implementing AI-powered talent management systems across organizational contexts. The research specifically analyzes how three primary dimensions—technological capabilities within HR functions, data governance and ethical frameworks, and organizational preparedness coupled with cultural alignment—collectively determine the success of AI-integrated talent strategies. Additionally, this study explores the mechanisms through which these strategic implementations influence workforce perceptions and confidence in algorithmically-driven HR systems. Grounded in Strategic Human Resource Management (SHRM), Resource-Based View (RBV), Technology Acceptance Model (TAM), and socio-technical systems theory, the investigation employs qualitative methodology involving in-depth interviews with fifteen HR practitioners representing varied industry sectors across the United Arab Emirates. Drawing upon a conceptual model validated through four testable propositions, the theoretical framework underscores the complex, multifaceted character of AI integration in human capital management, encompassing technological infrastructure, organizational dynamics, and ethical imperatives. Empirical evidence demonstrates that effective deployment of AI in talent management necessitates comprehensive technological infrastructure alongside rigorous data stewardship, organizational change readiness, and transparent stakeholder communication to cultivate workforce confidence. The study also addresses critical risks including algorithmic bias, employee resistance, privacy concerns, and legal implications that organizations must navigate. This research contributes practical guidance for HR practitioners and organizational decision-makers pursuing implementation or enhancement of AI-driven talent management initiatives, while advancing scholarly discourse on the intersection of artificial intelligence and strategic human resource management.