Mariya Sira

Doomscrolling as the compulsive, self-reinforcing consumption of negative online news – has emerged as a documented occupational behaviour with measurable consequences for employee psychological well-being and work engagement. Despite growing empirical attention, there is a lack of studies examining doomscrolling in manufacturing environments, where attentional failures carry direct and potentially irreversible safety consequences. This paper addresses that gap through a systematic literature review conducted in accordance with PRISMA 2020 guidelines, retaining sixteen studies spanning multiple countries for synthesis. Evidence is organised into three streams. The first establishes theoretical and contextual foundations of doomscrolling as an affect-driven subtype of cyberloafing shaped by algorithmic amplification and developmental factors. The second synthesises empirical evidence on psychological effects among working adults: across four independent samples, doomscrolling consistently predicted rumination, psychological distress, existential anxiety, and reduced work engagement, with a weighted effect size of d = 0.88 for the distress–anxiety pathway – a large effect appears notably larger than effect sizes commonly reported for general internet use at work, though direct comparison is constrained by construct heterogeneity. The third draws on bridge evidence from industrial settings linking problematic internet use and deviant cyberloafing to diminished safety behaviour in mining and healthcare contexts. A conceptual model integrating these pathways through Conservation of Resources Theory is proposed, identifying three parallel depletion mechanisms – rumination, attentional depletion, and psychological distress – that translate doomscrolling into reduced safety compliance, elevated human error, and productivity loss. Four empirical propositions are advanced to guide future research in manufacturing contexts.

This systematic review synthesizes and classifies diverse theoretical models and frameworks guiding technology and artificial intelligence (AI) implementation in organizations. Through analysis of literature, this study develops a novel classification framework identifying six critical dimensions of implementation models: implementation object, analysis level, transformation character, implementation method, adaptation focus, and implementation depth. The research reveals three significant trends: (1) an evolution from technology-centric to holistic socio-technical approaches, (2) the emergence of specialized AI implementation frameworks addressing unique challenges of artificial intelligence, and (3) the necessity of multi-level synchronization during implementation processes. This review makes four primary contributions: establishing a comprehensive classification system that coherently organizes existing models, identifying emerging patterns in contemporary implementation approaches, proposing an integrated framework that combines complementary perspectives, and providing evidence-based recommendations for practitioners navigating complex implementation challenges. This unified perspective bridges previously fragmented implementation approaches across disciplines and offers a more cohesive foundation for both theoretical advancement and practical application in organizational transformation.

The automotive industry increasingly relies on outsourcing engineering services, software development, and technical support to specialized providers. Extensive research exists on artificial intelligence applications for product quality in manufacturing and on service quality assessment using the SERVQUAL model. However, no studies have examined the integration of AI technologies with service quality dimensions for managing outsourced automotive services. This conceptual paper addresses this gap by developing an AI-enabled SERVQUAL framework for automotive service outsourcing. The framework integrates five SERVQUAL dimensions (reliability, responsiveness, assurance, empathy, and tangibles) with intelligent monitoring systems including automated quality assessment tools, natural language processing for communication analytics, predictive analytics for risk assessment, sentiment analysis, and infrastructure assessment technologies. The proposed framework enables real-time service quality monitoring, predictive quality alerts, automated supplier benchmarking, and continuous improvement recommendations. This research bridges service quality theory, technological innovation, and automotive outsourcing practice, providing actionable framework for improving supplier relationship management and service delivery in the automotive sector.

Despite widespread artificial intelligence adoption (78% of organizations), only 1% achieve mature implementation, creating a critical research challenge in understanding the disconnect between technological deployment and operational integration. This study develops a comprehensive multidimensional gap analysis framework to systematically assess AI implementation effectiveness across technology adoption, human capital readiness, financial adequacy, and strategic alignment dimensions. The methodology integrates Parasuraman’s service quality gap analysis with strategic alignment theory, employing systematic analysis of authoritative data from McKinsey, IBM, PwC, and Deloitte. Integration depth coefficients differentiate reported adoption from functional operational effectiveness, addressing limitations in traditional binary assessment approaches. Results demonstrate substantial implementation gaps across all dimensions: strategic alignment (80%), technology integration (70.75%), financial optimization (57.31%), and human capital development (16.67%). The strategic gap emerges as the most critical barrier, with fewer than 20% of organizations tracking AI performance indicators. Notably, the human capital gap significantly underperforms industry perceptions, indicating systematic overestimation of skill requirements. Statistical validation confirms the framework’s robust predictive capability, with AI implementation level emerging as the primary efficiency driver and gap reduction serving as critical enablers. Industry analysis reveals substantial implementation variations across sectors, confirming the need for tailored organizational strategies. The validated framework provides systematic assessment tools for evaluating AI implementation maturity and identifying optimization priorities. Organizations must prioritize comprehensive implementation scaling alongside strategic measurement development to realize investment potential and achieve sustainable competitive advantages in digital transformation.

Organizations implement intelligent automation across diverse operational contexts but often lack comprehensive frameworks for strategic decision-making and cross-domain integration. The existing literature frequently examines isolated applications with limited implementation guidelines addressing environmental interdependencies. This study conducts a systematic review of 69 publications (2019–2024) using thematic analysis to examine automation patterns across six environmental domains: social, economic, educational, scientific, technological, and ecological. The analysis identifies three implementation patterns: efficiency-focused domains (economic, technological) emphasizing operational optimization; capability-focused domains (social, educational) prioritizing human augmentation; innovation-focused domains (scientific, ecological) developing transformative applications. Cross-domain analysis reveals integration opportunities and sustainability considerations. The study proposes a strategic decision-making framework incorporating environmental assessment tools, quality enhancement mechanisms, and planning capabilities. This framework supports organizations in selecting domain-appropriate strategies while addressing sustainable transformation objectives. The research provides systematic environmental categorization of intelligent automation applications and offers implementation guidelines for practitioners pursuing coordinated digital transformation across organizational contexts.

This study examines how integrating digital technologies into government operations can enhance urban governance in smart cities. It specifically looks at how these advancements can contribute to the development of sustainable smart cities in alignment with the Sustainable Development Goals (SDGs). The research employs two analytical frameworks to assess how advanced technologies impact and integrate with government functions. The first framework examines the effects of digitalization on sustainable smart cities, while the second offers a holistic approach to optimizing government processes through digital transformation. The research demonstrates the various advantages of digitalization, such as enhanced public services, advanced infrastructure, and greater transparency and accountability. Nevertheless, obstacles like data security, privacy issues, and disparities in digital access are recognized, requiring cautious handling. The research affirms that while integrating digital technologies in government operations can significantly support urban sustainability, the strategy is crucial to manage potential threats and foster inclusivity and environmental responsibility. These models provide practical recommendations for governments to responsibly and effectively utilize digital tools, ultimately fostering the development of resilient, efficient, and inclusive smart cities.

Effective knowledge management is crucial for organizational success, but persistent challenges exist in capturing, transferring, and utilizing institutional knowledge. This research examines the intersection of artificial intelligence (AI) capabilities with knowledge management practices, focusing on the potential of AI to overcome organizational silos. The study employs a mixed-method approach, including a systematic literature review, content analysis of implementation cases, and comparative analysis. The findings reveal that successful AI integration in knowledge management requires a balanced approach addressing both technical implementation and organizational readiness. Key success factors include robust data governance, strong leadership support, and comprehensive stakeholder engagement. The research provides valuable insights for organizations seeking to leverage AI technologies for improved knowledge management and organizational effectiveness.

This article provides an exploration of the intersection between management, artificial intelligence (AI), and occupational health and safety (OHS), delving into the evolution of safety systems and the integration of advanced technologies. To address the barriers to AI adoption in OHS and maximize the benefits of AI integration, the article proposes an AI Adoption Roadmap for OHS, delineating phases for assessment, planning, implementation, monitoring, continuous improvement, and the establishment of a data-driven safety culture. By embracing AI-driven solutions and fostering a culture of innovation and collaboration, organizations can proactively address safety challenges and cultivate safer, healthier work environments.

Environmental management systems (EMS) are essential in promoting sustainable practices and mitigating the adverse effects of human activities on the environment. As technology continues to advance, there is an increasing opportunity to utilize advanced technologies to improve environmental management systems. This article examines the potential of different advanced technologies, such as artificial intelligence (AI), blockchain, big data, and the Internet of Things (IoT), within the context of environmental management systems. This article intends to offer valuable insights to researchers, practitioners, and policymakers by examining the potential uses of AI, blockchain, big data, and IoT in environmental management systems. The goal is to demonstrate how these advanced technologies can be leveraged to enhance sustainability, boost environmental performance, and yield favourable environmental results across different sectors and industries.

Purpose: purpose of this paper is to explore the profound impacts of advanced technologies— such as AI, blockchain, IoT, and robotics—on management practices across various sectors. By examining sector-specific case studies, this paper seeks to uncover the nuanced ways in which advanced technologies influence management practices and to identify the competencies required for effective organizational leadership in this evolving landscape. The analysis highlights how different sectors adapt to technological advancements and the critical managerial skills needed to navigate these changes successfully. Design/methodology/approach: To achieve the objectives, the paper employs a qualitative research approach, incorporating a comprehensive review of existing literature and case studies from diverse sectors including healthcare, finance, manufacturing, retail, and transportation. The approach includes a detailed analysis of the integration and impact of advanced technologies on management practices. Findings: The adoption of advanced technologies across sectors reveals the need for specific managerial competencies. In healthcare, managers must excel in data analysis and ethical AI integration. The finance sector requires expertise in blockchain, AI, and cybersecurity. Manufacturing demands skills in technology management, workforce retraining, and cybersecurity. Retail managers need to integrate AI and IoT solutions while safeguarding data privacy, and those in transportation and logistics must oversee autonomous systems, real-time data, and workforce adjustments. Overall, managers need technological proficiency, analytical skills, cybersecurity awareness, and change management expertise. Continuous learning and adaptability are also crucial for navigating technological advancements and maintaining a competitive edge. Originality/value: This paper provides a novel examination of how advanced technologies influence management practices across different sectors, offering insights into both the benefits and challenges associated with these innovations. The value of the paper lies in its comprehensive analysis and sector-specific case studies, which contribute to a deeper understanding of the intersection between technology and management. The research is addressed to academics, industry professionals, and policy-makers interested in the evolving landscape of technology-driven management. Keywords: advanced technologies, management competencies, technology integration, sector- specific impacts. Category of the paper: Literature review.