Online tutoring is a substantial instructional approach in distance education, yet current literature lacks a structured framework that explains the contextual relationships between the factors of online tutoring. This research employs interpretive structural monitoring–Matrice d’Impacts Croisés Multiplication Appliquée à un Classement analysis to transform expert insights into an interpretive multi-level hierarchical structure out of the complex inherent relationships of factors in online tutoring. Results reveal that platform usability, flexibility, institutional support, and social reinforcement are the primary drivers of online tutoring in pre-university education. Prioritizing these key factors creates a cascading effect that promotes successful online tutoring implementation. These findings offer critical insights for stakeholders to improve tutor training, strengthen technological infrastructure, and design supportive policy guidelines. This research contributes to the limited pre-university literature and demonstrates the utility of interpretive structural monitoring–Matrice d’Impacts Croisés Multiplication Appliquée à un Classement in translating complex, qualitative expert judgments into actionable policy insights for successful outcomes.
The integration of artificial intelligence (AI) into creative industries is rapidly transforming how content is produced, distributed, and interpreted. This study uses a sociotechnical lens to investigate the adoption and diffusion of AI-generated content (AIGC) across media, gaming, and marketing sectors in the Asia-Pacific region. Grounded in sociotechnical systems and innovation diffusion theories, its research employs a quantitative methodology using secondary data sources to analyze patterns of technological integration, regulatory influence, and creative workflow evolution. Findings indicate that the uneven adoption of AI across sectors is shaped not only by a sector’s technological capabilities but also by its institutional readiness, national policy frameworks, and cultural attitudes toward automation. The study highlights the importance of human-centered design, ethical considerations, and platform dynamics in facilitating the sustainable and equitable implementation of AI tools.
Advanced manufacturing technologies have a positive impact on the efficiency of firms, facilitating product design and fabrication, improving connectivity among resources, and optimizing planning processes. Consequently, the adoption of these technologies has garnered increasing interest in the field. However, the literature has not fully explored the interrelationships among the enablers and barriers that influence adoption decisions. This study investigates these factors using interpretive structural modeling and cross-impact matrix multiplication applied to a classification analysis, complemented by bootstrap resampling to provide statistical validation of the results. The findings indicate that corporate structure as an enabler and economic barrier significantly drives adoption decisions. These results highlight the need for stakeholders to address these factors to ensure the successful implementation of advanced manufacturing technologies.
Digital transformation is widely recognized as a critical driver of organizational productivity; however, its effectiveness largely depends on the successful management of employee change. This study examines the relationships between digitalization, employee change management, and organizational productivity while reviewing the key theoretical perspectives that underpin these linkages. This study develops a theoretical framework that explains how digitalization and employee change management jointly influence organizational productivity. Digitalization is grounded in the resource-based view, and employee change management is theoretically anchored in the theory of planned behavior. Organizational productivity is framed using socio-technical theory to emphasize the principles of joint optimization between social and technical subsystems. The proposed framework demonstrates strong applicability across diverse organizational and national contexts and contributes to the literature by integrating strategic digital investments, employee engagements, and productivity outcomes.
This study investigates the impact of digital leadership on employees' innovative work behavior and knowledge sharing behavior, with a particular focus on the mediating role of psychological ownership. The independent variable, digital leadership, is examined through its supportive and innovative sub-dimensions, while psychological ownership is explored through emotional and work-based ownership. The dependent variables are innovative work behavior and knowledge sharing behavior. To test the research model, eight sub-models were developed and analyzed using PLS-SEM. Mediation was assessed through the Variance Accounted For (VAF) metric. Data were collected from 287 managers and employees working in IT departments within technology parks located in Ankara, Türkiye. The results reveal that both supportive and innovative forms of digital leadership have a positive and statistically significant influence on employees' innovative behavior and knowledge sharing. However, a portion of this effect operates indirectly through psychological ownership.
