Management, Education and Development in Digital Age

The present study was conducted with the aim of designing an e-learning model for Islamic Azad University, Bushehr Province. In terms of its purpose, the research is applied; regarding the data, it follows an exploratory mixed-methods design (qualitative-quantitative); and in terms of its nature, the qualitative phase employs a systemic grounded theory approach (paradigmatic), while the quantitative phase uses a cross-sectional survey method. The statistical population in the qualitative phase consisted of experts in the field of educational management with doctoral degrees. In the quantitative phase, the population comprised faculty members of Islamic Azad University, Bushehr, totaling 100 individuals (56 women and 44 men). The sampling method in the qualitative phase was purposive and theoretical, with 12 experts selected until theoretical saturation was reached. In the quantitative phase, the sample size was determined using Morgan's table, and 80 individuals (45 women and 35 men) were selected. The measurement tool was developed after open and axial coding, formatted into a structured form, and sent to experts for selective coding and validation. Based on their feedback, a researcher-made questionnaire was designed and distributed randomly among the statistical sample. The collected data were analyzed using descriptive and inferential statistics. Ultimately, five dimensions, 16 components, and 101 indicators were finalized for the e-learning model of Islamic Azad University, Bushehr. After final approval and prioritization by experts, the dimensions, components, and indicators of the model were illustrated, and the model was once again validated by the experts.

The present research employed a descriptive-survey method. The statistical population consisted of all secondary school biology teachers within the Iraqi Ministry of Education, totaling 431 individuals during the 2024–2025 academic year. The sampling method was stratified random sampling, and based on Cochran's sample size formula, 136 secondary school biology teachers from urban areas were selected for the study. The research instrument was a researcher-developed questionnaire comprising 94 items and five dimensions (causal conditions, contextual conditions, strategic conditions, intervening conditions, and consequential conditions). Data collection was conducted in two stages: library research and field study. Data were analyzed using both descriptive statistics (frequency and percentage) and inferential statistics (Kolmogorov-Smirnov test for normality, confirmatory factor analysis, and structural equation modeling). Overall, the findings indicated that the most significant component of causal conditions was environmental conservation culture-building (factor loading = 0.98); for contextual conditions, it was environmental learning activities (factor loading = 0.98); for intervening conditions, it was unexpected environmental changes and lack of appropriate educational conditions (factor loading = 0.96); for strategic conditions, it was environmental knowledge sharing and emotional engagement with the environment (factor loading = 0.98); and for consequential conditions, it was increased environmental awareness (factor loading = 0.98). The results also showed that the "Model for Institutionalizing Environmental Behavior among Iraqi Students with a Sustainable Development Approach" demonstrated a good model fit.

The aim of this study is to design a Total Productive Maintenance (TPM) management model using a combined approach of factor analysis and Interpretive Structural Modeling (ISM) in the power distribution companies of northwest Iran. This research is conducted within the power distribution companies of the northwest region, selected as the pilot area. Identifying the optimal maintenance strategy and selecting the most effective TPM management model are of significant importance. The results of this research will be provided to Tavanir and the Ministry of Energy for potential implementation in other affiliated companies. From a spatial perspective, the quantitative data pertains to operational data from the power distribution companies in the northwest region. The statistical population includes both experts and the self-assessment segment of all specialists and maintenance, repair, and warehouse personnel within these companies. Temporally, the data includes operational records from the inventory, accounting, and process systems of the power distribution companies for the years 2017 to 2022. The findings of this study indicate that the initiation of the TPM process should begin with enhanced management and leadership in maintenance and repair, followed by attention to reliability improvement, feedback and system outcomes, human resources and employee skills, and quality preservation in the process of preventive maintenance. Additionally, work processes, organizational structuring, task force formation, planned preventive maintenance, safety and health preservation, equipment and warehouse management, resource and contract management, adoption of preventive maintenance technologies, expansion of automation systems, self-controlled maintenance and repair, and ultimately, understanding the appropriate framework for TPM in power distribution companies—based on the insights of experts and system specialists—should be considered.

This study aims to identify the components of human resource excellence in the digital age, focusing on executive, structural, and technological dimensions within Iranian organizations. The research employed a qualitative design using thematic analysis. Data were collected through semi-structured interviews with 12 experts in human resources, technology, and organizational management. Participants were purposefully selected, and interviews continued until theoretical saturation was achieved. Data were coded in three stages—open, axial, and selective—using NVivo software. To ensure validity and reliability, triangulation, member checking, and the PAO index were applied. Findings revealed that human resource excellence in the digital era is categorized into three main domains: the implementation environment (38% of codes), the human resource environment (36.4%), and the digital age environment (25.6%). Key dimensions included organizational structure, meritocracy, execution tools, communication barriers, and institutional frameworks. The most prominent challenges were a lack of merit-based systems, ineffective political and managerial structures, and poor understanding of emerging technologies. Achieving human resource excellence in the digital era requires redesigning executive frameworks, enhancing merit-based HR systems, fostering inter-organizational communication, and empowering HR through digital capabilities. Policymakers are encouraged to adopt an integrated approach to enable digital transformation at all levels of human resource management.

The present study aims to analyze the health and well-being of employees in Iran's banking industry. Employing a mixed-methods approach (qualitative–quantitative), the research is exploratory in nature and applied in purpose. In the qualitative phase, utilizing the meta-synthesis method and the seven-step model of Sandelowski and Barroso (2007), all domestic and international scholarly articles indexed in reputable databases were examined. The review covered Persian articles from 2011 to 2023 and foreign articles from 2010 to 2023. Following preliminary studies and article screening by the researcher, a set of indicators related to the research objective was compiled, resulting in an initial checklist comprising 53 studies, 6 dimensions, and 38 components. The six dimensions were identified through meta-synthesis using Sandelowski and Barroso's (2007) model. These dimensions of employee well-being and health were identified and confirmed by experts and include: financial well-being, occupational well-being, social well-being, physical well-being, legal well-being, and emotional well-being. Subsequently, in the quantitative phase, the Delphi technique was employed to validate the components through a survey of professors and experts. The results indicated sufficient correlation among the indicators. To examine the relationships between dimensions and components, questionnaires were distributed among branch managers of Bank Melli in Mazandaran Province. Factor analysis and path analysis tests were conducted using LISREL software, and the results confirmed all dimensions (financial well-being, occupational well-being, social well-being, physical well-being, legal well-being, and emotional well-being).

In the 21st century, technology has advanced significantly, leading to the development of numerous high-tech products that facilitate human work and activities. Among the most significant of these advancements is artificial intelligence (AI). AI enables the creation of solutions capable of addressing complex problems and offers novel approaches to problem-solving. It has opened the door to a revolution in the learning processes of social studies, transitioning from traditional educational methods to intelligent learning approaches. The present study aims to examine the challenges and opportunities presented by AI and information technology in universities within the modern world. This qualitative research employs a meta-synthesis methodology. Data collection tools include documents and prior research and library materials in this domain. The data analysis method utilized is open coding. According to the study's findings, out of 270 related articles, 37 primary or criterion studies were analyzed. The extracted propositions encompass 46 subcategories and 4 main categories. Based on the results of this study, 46 areas of AI transformation in the education of social and behavioral sciences were identified from the selected articles, categorized into four key domains: intelligent teaching and instruction in social sciences, intelligent learning in social sciences, intelligent management in social sciences, and intelligent assessment in social sciences. These areas contribute to the transformation of social sciences education.

In the 21st century, technology has advanced significantly, leading to the development of numerous high-tech products that facilitate human work and activities. Among the most significant of these advancements is artificial intelligence (AI). AI enables the creation of solutions capable of addressing complex problems and offers novel approaches to problem-solving. It has opened the door to a revolution in the learning processes of social studies, transitioning from traditional educational methods to intelligent learning approaches. The present study aims to examine the challenges and opportunities presented by AI and information technology in universities within the modern world. This qualitative research employs a meta-synthesis methodology. Data collection tools include documents and prior research and library materials in this domain. The data analysis method utilized is open coding. According to the study's findings, out of 270 related articles, 37 primary or criterion studies were analyzed. The extracted propositions encompass 46 subcategories and 4 main categories. Based on the results of this study, 46 areas of AI transformation in the education of social and behavioral sciences were identified from the selected articles, categorized into four key domains: intelligent teaching and instruction in social sciences, intelligent learning in social sciences, intelligent management in social sciences, and intelligent assessment in social sciences. These areas contribute to the transformation of social sciences education.

The aim of this study was to design an educational model suitable for the e-learning environment based on the domains of the Fundamental Reform Document of Education (FRDE) for the second stage of elementary school. This mixed-methods research followed an exploratory sequential design. In the qualitative phase, data were collected through semi-structured interviews with 10 selected education experts from Mashhad and analyzed using thematic content analysis to identify the primary dimensions of the model. In the quantitative phase, the statistical population consisted of 1,440 elementary teachers and education staff in Razavi Khorasan Province. Using stratified random sampling, 303 participants were selected. The main research instrument was a questionnaire with 4 dimensions and 31 items, whose reliability and validity were assessed via Cronbach’s alpha, AVE, CR, and confirmatory factor analysis. Data analysis was performed using LISREL software. Path analysis results indicated that all paths were statistically significant (P<0.001). Key components such as intrinsic motivation (β=0.40), having hope and purpose (β=0.74), creative and open space (β=0.69), school belonging (β=0.67), family support (β=0.51), and organizational structure (β=0.67) had a positive and significant effect on their respective constructs. Model fit indices including GFI=0.93, RMSEA=0.089, CFI=0.95, and CMIN/df=1.95 were within acceptable ranges, and the model’s explanatory power was strong (R²=0.82). The final model offers an integrated, multidimensional framework for designing e-learning in elementary education, aligned with the domains of the FRDE, and provides a suitable foundation for educational policy and planning in digital learning contexts.