The digital era has transformed education, making digital literacy essential for teachers to integrate technology and enhance student outcomes effectively. This study aims to examine how school culture influences teachers’ performance through their digital literacy, focusing on junior high school teachers in Malang City, East Java, Indonesia. Employing a quantitative approach, data were collected from 214 teachers out of a 457 population using questionnaires. The analysis was conducted through AMOS for Confirmatory Factor Analysis (CFA), SPSS for descriptive statistics, and PLS-SEM for hypothesis testing. The findings reveal that school culture significantly affects teachers’ digital literacy (Ho1) and teacher performance (Ho2) with supportive and innovative environments, while rigid cultures limit creativity. Furthermore, digital literacy was found to enhance teachers’ performance (Ho3) and mediate the impact of school culture on teachers’ performance (Ho4), enhancing teachers’ effectiveness in planning, implementing, and evaluating instruction. This study highlights the critical role of school culture in shaping digital literacy and offers new insights for improving teacher practices in diverse educational settings. Moreover, the role of education policies in fostering a collaborative school culture that enhances teachers’ digital literacy and performance, leading to improved educational outcomes, plays a crucial implication.

The objective of this research was to analyze several reading and writing methods used in educational settings, evaluating their pedagogical approaches and their effectiveness in the process of learning to read and write in school-age children. A systematic review was carried out in the open databases Dialnet and ScieELO, using different inclusion and exclusion criteria, which resulted in 164 documents, applying the PRISMA protocol, 20 were selected. A narrative synthesis analysis was carried out on the following dimensions: reading and writing methods, applied strategies, similarities with other methods and impact on the development of literacy. It is concluded that the combined application of the methods of synthetic and analytical approaches to reading and writing paves the way to attend to the diversity of learning styles, facilitates the strengthening of specific linguistic skills, and strengthens reading comprehension and writing competence.

This research explores the necessity and the effect of job resources for undergraduates’ career satisfaction during work experience in an apprenticeship program. Additionally, we examine the extent to which a supportive environment enhances apprentice career satisfaction by providing access to valuable learning experiences. We propose PLS equation modelling with a sample of 81 students who completed a dual apprenticeship degree in Business Administration and Management at Spanish University. The study finds that all three workplace job resources are necessary for career satisfaction among apprentices. Learning opportunities and social relations have significant effects, while job control contributes only marginally. It highlights that learning opportunities enhance social relations, emphasizing the importance of feedback. The study extends job resource research to university level apprenticeships, showing that without these resources, apprentices lack career satisfaction. It highlights that learning opportunities are crucial for satisfaction through social relations and offers guidance for designing effective workplace training programs.

This study examines the impact of emotional intelligence (EI) and employee motivation on employee performance within the telecommunication industry in the Sultanate of Oman. The target population consisted of 4344 non-managerial employees across nine telecommunication companies, including Omantel, Ooredoo, Vodafone, Oman Broadband Company, Awasr Oman & Co, TEO, Oman Tower Company L.L.C, Helios Tower, and Connect Arabia International. Employing a deductive research approach, finally data were collected via an online survey from 354 respondents. The hypotheses were tested using multiple regression analysis. The results indicate that all dimensions of EI self-awareness, self-regulation, empathy, and social skills positively and significantly influence employee performance, with social skills having the strongest effect. Furthermore, both intrinsic motivation factors, such as work itself and career development, and extrinsic motivation factors, including wages, rewards, working environment, and co-worker relationships, significantly enhance employee performance. The interaction between EI and employee motivation was found to amplify these positive effects. Among control variables, age and education level showed significant impacts, while gender did not. These findings underscore the critical role of both emotional intelligence and motivation in driving employee performance. The study suggests that managers and policymakers should adopt integrated strategies that develop EI competencies and enhance motivational factors to optimize employee performance, thereby contributing to the success of organizations in the telecommunication sector.

The accessibility of FinTech services is increasing, and their convenience is making them more popular than traditional banks, particularly among Generation Z. The objective of this research is to identify and compare the factors influencing the conscious use of FinTech services among Generation Z members, who are the most active participants in this field of financial technology. The questionnaire based purposive sample consisted of Generation Z students who demonstrated adequate financial literacy and utilized FinTech, and who were learning in a university environment in Hungary and Romania. A sample of 600 respondents was selected for analysis after cleaning the data online. The methodological approach entailed the utilization of covariance-based structural equation modeling (CB-SEM). The results indicate that social influence (β = 0.18), consumer attitude (β = 0.53) and facilitating conditions intention (β = 0.11) all have a significant effect on the behavior intention, explaining 49% of the variance. In the context of performance expectation, the effect of facilitating conditions intention is not significant (p = 0.491). The motivation of Generation Z towards fintech solutions is evident in their preference for speed and ease of use. However, in order to reinforce consumer expectations and transfer the necessary experience and attitudes, it may be beneficial for service providers to adopt a partially different strategy in different countries. Generation Z can thus serve as a crucial reference point for the even more discerning expectations of subsequent generations. The findings may inform the formulation of strategies for fintech service providers to better understand customer behavior.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.