Associative classification (AC) has been shown to outperform other methods of single-label classification for over 20 years. In order to create rules that are both more precise and simpler to grasp, AC combines the rules of mining associations with the task of classification. However, the current state of knowledge and the views of various specialists indicate that the issue of multi-label classification (MLC) cannot be solved by any AC method. Since this is the case, adapting or using an AC algorithm to manage multi-label datasets is one of the most pressing issues. To solve the MLC issue, this research proposes modifying the classification based on associations (msCBA) method by extending its capabilities to consider more than one class label in the consequent of its rules and modifying its rules order procedure to fit the nature of the multi-label dataset. The proposed algorithm outperforms several other MLC algorithms from various learning techniques across a variety of performance measuresand using six datasets with different domains. The main findings of this research are the significance of utilizing the local dependencies among labels compared to global dependencies, and the important rule of AC in solving the problem of MLC.

Achieving the ambitious economic, social, and environmental goals of the UN Sustainable Development Goals (SDGs) requires strategic digital governance improvements that promote long-term and equitable participation in emerging technologies. However, research lacks clear confirmation regarding how governments’ varied policy investments in spheres, like infrastructure expansion, skills programming, and cybersecurity, specifically contribute to holistic sustainable development progress monitored across international benchmarks. Addressing persistent uncertainties, this study statistically modeled if national expenditures directed toward information and communications technology (ICT) access, digital literacy initiatives, and online privacy protections predict higher performances meeting SDGs longitudinally. Analyzing recent country-level data across 27 European nations, structural equation modeling uncovered positive relationships between all three complementary digital governance priority areas and national SDG Index achievement over time. Beyond theoretically validating the instrumental role of availability, capabilities, and security advancements for balanced digitization, findings offer policymakers vital empirical guidance to amplify social returns on ICT investments. The results also demonstrate practical tools to track implementation impacts amidst unrelenting technological shifts. Ultimately, equitably mainstreaming technologies’ vast problem-solving potential necessitates evidence-based digital governance carefully expanding equitable participation. This work aims to equip leaders to purposefully craft enabling, empowering ICT policy ecosystems advancing urgent development aims.

Cybersecurity has become one of the top priorities in Saudi Arabia, playing a key role in achieving Vision 2030 and advancing the kingdom’s position in digital transformation. This study investigates how cybersecurity knowledge, attitudes, and awareness influence user behaviours in health applications within Saudi Arabia. An online cross-sectional survey was distributed between March and April 2025 among Saudi Arabian residents. The collected data (n = 629) were analyzed using Smart PLS Structural Equation Modelling (SEM) to assess the relationships among the study constructs. The majority of the participants (61.4%) were between the age of 18 and 24, and 87.6% reported using health applications such as Sehhaty or Labayh to manage their health information. Results demonstrated that all three constructs significantly predicted cybersecurity behaviours: knowledge showed the strongest influence (β = 0.372), followed by attitude (β = 0.343) and awareness (β = 0.199), with all paths being statistically significant (p < 0.05). The model explained substantial variance in cybersecurity behaviours. Knowledge, attitude, and awareness significantly predict cybersecurity practices in healthcare application contexts. Findings highlight the critical need for targeted educational interventions focusing on cybersecurity knowledge enhancement and awareness programmes to promote safer digital health behaviours and strengthen patient data protection in Saudi Arabia’s healthcare system.

This study investigates the behavioral elements that shape investor decision-making, particularly in the context of stock market investments supported by accounting information systems (AIS). Behavioral finance has emerged as an important field that examines how psychological traits and cognitive shortcuts influence financial judgments, challenging the assumption of purely rational investor behavior. The study gathered data from 270 retail investors and examined their responses utilizing SPSS and AMOS software. Advanced statistical methods, such as confirmatory factor analysis (CFA) and structural equation modeling (SEM), were utilized to examine the correlations among variables. The results indicate that psychological factors, including emotions, attitudes, moods, personality traits, and heuristic-based decision-making, profoundly influence investment behavior. Furthermore, the findings validate that these determinants persist in influencing investor decisions, even in the presence of dependable financial data disseminated via AIS. This work offers theoretical and practical contributions by integrating insights from behavioral finance and accounting systems research. It contributes evidence to the expanding body of literature that synthesizes human psychology with financial technology. The results provide direction to investors, financial institutions, and policymakers on mitigating biases and fostering more rational investment strategies through the appropriate utilization of AIS.

High population growth across developing Pacific countries has increased the share of young people, creating potential for a demographic dividend alongside accelerating digitalisation driven by technology transfer. This study empirically investigates the dynamic relationship between the demographic dividend, digitalisation (ICT access), and economic growth. An endogenous growth framework is employed and extended to incorporate demographic change and information technology adoption, along with education (human capital) and renewable energy use to capture the broader dimensions of sustainable economic growth. The results show that both the demographic dividend and ICT are positively associated with per capita income. Moreover, technology adoption and the demographic dividend exhibit a complementary relationship, implying that improvements in one reinforce the growth effects of the other, while education exerts a positive and statistically significant impact on economic growth. In contrast, renewable energy shows a positive but statistically insignificant effect, suggesting its role is emerging but important for long-term sustainability. In assessing the determinants of digitalisation, the findings indicate that demographic structure and urbanisation contribute positively; however, a substantial share of variation in digitalisation is explained by income levels and mobile phone technology. These results highlight the importance of policies that leverage demographic transition through education and digital technologies, and sustainable energy transitions to accelerate sustainable economic growth and improve welfare outcomes in Pacific developing economies.