This study conducts an in-depth exploration of the formation process behind the global impact of artificial intelligence research papers (AI papers). At the macro level, it conducts a cross-national comparison of AI papers from the perspectives of publication volume and policy citations. At the meso level, the Criteria Importance Through Intercriteria Correlation method is employed to conduct separate single-dimensional evaluations of the global impact of AI papers across four key dimensions: academic impact, perceived impact, societal impact, and policy impact. At the micro level, a comprehensive evaluation of the global impact of AI papers is carried out using the Projection Pursuit Classification Model, with the results subsequently validated using convolutional neural networks and correlation analysis. This study reveals the formation mechanisms of the global impact of AI papers, develops an evaluation framework based on four impact dimensions, and empirically validates its reliability and validity.
A literature review on research publication in mainstream journals was carried out in this paper, focusing on global patterns, determinants, and strategies in contemporary scholarship (2015–2025). The authors synthesized evidence on cross-regional and cross-disciplinary differences in mainstream journal publication and examined personal, institutional, and systemic factors that influence publishing outcomes. Key factors influencing research publication in mainstream journals include collaboration, research self-efficacy, and persistence, while common barriers include limited and unevenly distributed funding, language restrictions, high publication costs, and weak research environments. Furthermore, strategies to strengthen publication outcomes at individual and institutional levels are discussed, with particular attention to settings where structural constraints limit access to global scholarly communication. The authors argue that improved access to reputable journals is essential for visibility, scholarly impact, and fairness in international academic publishing and call for research and policy interventions that address historically rooted inequalities rather than only their recent expressions.
In the era of digital transformation and academic innovation, knowledge management (KM) has emerged as an important factor in driving innovation in higher education institutions. While previous studies have explored KM activities in an academic context, empirical evidence on how KM affects innovation in higher education, especially from the perspective of academic faculty, is limited. This study investigates the impact of KM through four factors: knowledge sharing, knowledge creation, knowledge application, and KM systems integrated with big data, on innovation performance from the perspective of faculty at Vietnamese universities. A quantitative survey was conducted with 397 university lecturers across Vietnam. The data were analysed using partial least squares structural equation modelling. The results showed that all four KM dimensions significantly and positively influenced innovation performance, with knowledge application and knowledge creation being the two most influential dimensions. These findings highlight the importance of knowledge application for innovation through teaching, research, and community service. While KM systems provide organizational support, people-centred activities for knowledge creation and application remain the key. This study contributes to the KM management literature by integrating cognitive, behavioural, and technological KM dimensions into a unified framework and by examining the links between KM and innovation in a higher education setting. Implications for policy and leadership are discussed, highlighting the need for financial investment and digital infrastructure to enable strategies that empower faculty as central actors in promoting and creating knowledge.
The study examined the impact of hybrid information technologies on the formation of regional information fields in Central Asia and their role in the socio-economic development of the region, taking into account international experience. The study revealed that hybrid technologies enhance the transparency of information flows, automate content production, and improve audience engagement in Central Asia's media landscape. Significant digital inequality between urban and rural areas in access to modern technologies was identified, particularly acute in countries such as Tajikistan and Turkmenistan. Key barriers to digitalisation were determined: a shortage of skilled personnel, weak infrastructure for foreign technology providers. Successful cases of adapting hybrid technologies to cultural specificities were identified, including the use of national languages in educational platforms and the digitalisation of traditional crafts. Kazakhstan demonstrated leadership in digital transformation through its "Digital Kazakhstan 2025" programme, while other countries in the region exhibited a tendency to replicate this success.
Based on innovation diffusion theory, this study constructs a journal impact diffusion evaluation framework. Thirteen indicators were selected according to five characteristics influencing diffusion speed. The Entropy Weight-AHP method was employed to determine the comprehensive weights of these 13 indicators, and the ELECTRE method was used to rank journals' impact diffusion capabilities. The results demonstrate a high correlation between the ELECTRE ranking and those derived from the impact factor and TOPSIS methods, although significant discrepancies exist in rankings for individual journals. Compared to other approaches, the ELECTRE method more effectively identifies journals exhibiting uneven development. The constructed framework assists journals in enhancing their development, identifying existing issues, and provides a methodological reference for overcoming the limitations of single indicator evaluations to achieve more comprehensive academic assessment.
