Management and Educational Perspective

Abstract The aim of the current research is to identify, categorize, and rank school performance indicators using the Q method. The research method is applicable in terms of purpose, mixed (qualitative-quantitative) in terms of implementation, and interpretive paradigm. This research was done in two steps; the research strategy in the first step is qualitative content analysis, and the statistical population of the research includes 24 people from four groups of parents, managers and employees of girls' high school, teachers and also the officials of the Yazd City Education Organization, by and targeted sampling; and the sample size continued up to theoretical saturation, and performance indicators were extracted from the semi-structured interview. In the second step of this research, using Q-axis factor analysis, the mental typology of 33 parents was investigated using a questionnaire and in-the-field. Content analysis was used in qualitative data analysis, and SPSS software and Q method were used in quantitative data analysis. The findings of the research have led to the achievement of seven different types of minds, indicating heterogeneity and significant differences in the views of parents regarding performance criteria; a difference that invited the managers of this school to segment the market based on understanding the desires and needs of the target audience with different mentalities. and improves the insight of managers of this industry regarding capital allocation by looking from outside to inside and emphasizing on audience-oriented evaluation system. Extended abstract Introduction The Education is the main educational institution and one of the largest social organizations, which plays a fundamental and important role in realizing the cultural, educational, social and economic goals of society, and the level of development of any society depends on its quality level (Andam & Taheri, 2019). The goal of every educational system is the all-round development and improvement of students in cognitive, emotional, psychological, and motor fields. As the future makers of every country, students form an important group of people in society (Vrghese & Rathnasabapathy, 2020) and therefore one of the most important concerns of educational institutions today is success in education, which means that every educational institution is efficient when its audience are academically in a favorable condition (Muhonen & et al, 2018) and their subjective assessment of the institution's performance should be positive and lead to continued communication and receiving services from it (Karababa, 2020). Considering the significant importance of school in the growth and upbringing of children and adolescents, it is important to study their interest and enthusiasm for school and the factors related to enthusiasm for school (Nouri, 2017). One of the important factors affecting student enthusiasm is school performance, which can have a significant impact on the selection process of students and parents (Tuominen et al, 2020.( The main question that this research was conducted in order to answer is: what are the evaluation criteria of parents of students of Sama Girls' High School in Yazd in the second grade of education, which provides a good basis for judging the performance of the school in terms of more than one year of contact with the school? And how is the mental typology of mothers as one of the most important effective roles in the decision-making process for the school of female students?  Theoretical Framework School performance Due to the intensification of competition between schools in recent years, schools are inclined towards improving their performance and have taken into consideration the identifying performance indicators and improving them from the path of competent and scientific measurement as an important part of the measures underlying the growth and excellence of the education system of the country. In fact, the main philosophy of performance measurement is to be aware of the level of performance that the organization is in, and to determine in which departments and steps things are going well, so that the foundations of success can be strengthened based on that. In addition, by revealing the parts and steps in which things are not well done and followed up, appropriate corrective measures can be designed and implemented (Ghaffari et al, 2013). Oladhamzehzadeh et al, (2024) investigated the design of an educational model based on identity development with an Iranian-Islamic approach for elementary school students. 20 experts from the academic community and specialists have been questioned to investigate the performance of students in the qualitative section; in the quantitative part of this research, 217 principals, assistants, teachers, and staff of elementary schools in five cities of Tehran have been considered, and the identified factors have been extracted in the form of 9 dimensions and 61 components. In this research, the dimensions of the educational model based on the development of students' identity with the Iranian-Islamic approach includes educational goals and content, teaching and learning methods, instructors and teachers, cultural factors, social factors, psychological factors, media, educational factors, and family factors. Based on the results of quantitative analysis, all dimensions and components of the model were valid and the model has a good fit. Ong et al, (2021) investigated performance evaluation in one of the public secondary schools in Malaysia. The results showed that performance evaluation is important for tracking teachers' productivity, their career development, providing a clearer career path, and helping teachers to improve job quality, provided that performance evaluation is carried on based on accurate criteria and fair evaluation and with cooperation and mutual communication between the manager and the teacher. Research methodology The research method is applicable in terms of purpose, mixed (qualitative-quantitative) in terms of implementation, and interpretive paradigm. This research was done in two steps; the research strategy in the first step is qualitative content analysis, and the statistical population of the research includes 24 people from four groups of parents, managers and employees of girls' high school, teachers and also the officials of the Yazd City Education Organization, by and targeted sampling; and the sample size continued up to theoretical saturation, and performance indicators were extracted from the semi-structured interview. In the second step of this research, using Q-axis factor analysis, the mental typology of 33 parents was investigated using a questionnaire and in-the-field. Research findings Content analysis was used in qualitative data analysis, and SPSS software and Q method were used in quantitative data analysis. The findings of the research have led to the achievement of seven different types of minds, indicating heterogeneity and significant differences in the views of parents regarding performance criteria; a difference that invited the managers of this school to segment the market based on understanding the desires and needs of the target audience with different mentalities. and improves the insight of managers of this industry regarding capital allocation by looking from outside to inside and emphasizing on audience-oriented evaluation system. Conclusion The current research was conducted with the aim of identifying, categorizing, and ranking school performance indicators using the Q method. The results of this research are in agreement with the results of Oladhamzehzadeh et al, (2024), Sori et al, (2023), Narimani et al, (2021), Ong et al, (2021), Taghipur Zahir et al, (2019), Moradi & AminBidakhti (2018), Ghaffari & Shirvani (2016), Khan et al, (2014), Whitford (2013), Nazari et al, (2013), and Abdulahi (2007). Ong et al, (2021) showed that performance evaluation is important for tracking teachers' productivity, their career development, providing a clearer career path, and helping teachers to improve job quality, provided that performance evaluation is based on accurate criteria and fair and collaborative evaluation and mutual communication between the manager and the teacher. According to the results of the research, it is suggested to take into account the difference in the mothers' views on a continuous and periodic basis to receive the views of the parents regarding the performance of the school, and not only to focus on improving the performance indicators identified from the perspective of the four groups in this research, but also they also consider the students' views and opinions and provide the basis for achieving a comprehensive performance evaluation system that can provide the appropriate tool and ruler for the school in order to achieve the high goals.

Abstract The purpose of this study is to design and validate a conceptual model of factors influencing competitive advantage in applied education centers. This research is applied in nature and adopts a descriptive–survey design with a quantitative approach. In the model development stage, the expert opinions of 15 specialists in higher education and management were utilized to identify an initial set of variables. Following consensus, 32 final variables were extracted and categorized into four main groups: process-related, service-related, customer-oriented, and support-related factors. To validate the proposed conceptual model, confirmatory factor analysis (CFA) was conducted using AMOS software. Data were collected through 188 completed questionnaires from managers, instructors, and experts working in applied higher education centers offering MBA and DBA programs. The analysis results indicated that the final model demonstrated an acceptable level of goodness-of-fit, and the identified variables play a significant role in enhancing competitive advantage. The proposed model can serve as a scientific framework to guide strategic decision-making in applied education centers.

Abstract The purpose of the research is to design a model of resilience in government organizations of Kermanshah province. The research method is qualitative-quantitative. This research is developmental-applicative in terms of purpose, and descriptive-survey in terms of data collection. The statistical population of the research includes government managers who have at least 10 years of experience. In order to sample; purposeful sampling method in the qualitative part was used, and in the quantitative part based on the number of extracted components, the number of samples was determined to be 396 people and the cluster method was used for sampling. In order to analyze the data, the coding method was used in the qualitative part and the structural equation model method was used in the quantitative part by PLS software. The results indicate that the causal factors include; individual, group and organizational factors. Also, the environmental platform includes; the role of the government, political and economic factors, and electronic platforms. The consequences also indicate the continuity and progress of the organization. The results of the structural equations also showed that the resilience model has a favorable fit in government organizations. Extended abstract Introduction Organizational resilience is a subcategory of positive organization and today, despite the attention to the term resilience and its vastly uses in various fields, there is a limited theoretical and practical understanding of this concept in its evaluation and measurement in relation to organizational resilience (Rafiyan et al, 2011). Resilience has been studied in many different fields, including management, security, ecology, psychology, disaster management, organization management, engineering, etc., but there is no definition acceptable in any field (Bergström et al., 2015). Resilience in the organizational field is not an exception to this rule. Researchers such as Alblas & Jayaram (2015), Alblas & Jayaram (2015), Chand & Loosemore (2016), and Hu et al, (2018) have considered organizational resilience as the ability to deal with changes, internal and external risks and impulses, and some such as Linnenluecke & Griffiths (2010), Alexiou (2014), and Ortiz & Bansal (2015) have defined resilience as a capacity to deal with changes, risk and impulses, and some others have considered it as an essential asset for the organization when it faces a risk (Ruiz-Martin et al, 2018). Rai et al, (2021) have considered organizational resilience as a way to deal with successive environmental crises. Organizations try to prepare themselves for all kinds of crises in advance and without fear by planning and making necessary preparations against all kinds of crises. Resilience means developing new competencies and broad capabilities to sustain momentum by creating new opportunities. Also, in this definition, resilience is considered as success due to the ability to invest in challenges and unexpected changes. According to another definition, resilience is the ability of a person or organization to quickly design and implement positive adaptive movements coordinated with an emergency situation, in such a way as to bear little pressure. Others define it as an essential asset for the organization when it faces a risk (Ruiz-Martin et al, 2018). Therefore, the concept of organizational resilience includes resistance, compromise and adaptation as the main assets and refers to shock absorption, reorganization and learning, etc. as the main capacities. Resilient organizations are also organizations that are able to deal with unforeseen shocks such as financial crises and globalization of competition, etc., and in some cases, it makes the organization prosper. In fact, resilience can be considered a necessity in recent centuries. Today's societies are increasingly facing emergency and crisis situations that challenge social and economic stability, and they rely on the services and employment provided by organizations to achieve resilience, because organizational resilience and societies' resilience are two sides of the same coin, and this means that if organizations are not ready to respond to emergencies and crises, then societies will not be ready either (Stephenson, 2010). Resilient organizations are organizations that can overcome crises with low costs due to their preparation and planning and high flexibility. All organizations should consider resilience against threats and environmental changes as one of their important and strategic goals and consider achieving this important goal. Therefore, the present research, with regard to the earthquake that happened in some cities of Kermanshah province in 2016 and caused a lot of money and lives (about 2,000 dead and 10,000 injured), seeks to, by designing a model of resilience in the government organizations of this province, help to increase their strength and readiness, which will reduce the amount of damages to the lowest possible level in the repetition of such events. The main question of the current research is: what are the characteristics of the model of resilience in government organizations of Kermanshah province? Theoretical framework The word resilience was presented for the first time by Holling in 1973 in a study titled resilience and stability of ecological systems. Then this term has been used for various other forms of resilience such as individual, organizational, supply chain, etc. If we consider people as a part of societies, resilience depends on societies and is not limited to social relations and social structure. One way to create resilience is to support people to create favorable social relations through technical resilience interventions. These interventions can include the development of services, the use of resources in communities, and the creation of new social and operational values for them. The origins and meaning of resilience as a scientific concept is ambiguous (Friend & Moench, 2013). The concept of resilience is used everywhere in both physical and social sciences. However, it has different meanings at different times. In recent years, resilience has become more common in both scientific and political discourses (Meerow & Newell, 2015). In fact, there are many meanings and concepts for resilience, there are wide definitions and interpretations of resilience, many of which are taken from the academic community of ecological systems. Originally, resilience is related to hazard studies, materials science and environmental studies, and also is a concept that has been used freely and enthusiastically by a wide range of policy makers, practitioners and academic researchers in the last decade. Although the actual meaning associated with this term varies accordingly, there are actually a large number of conceptual frameworks for resilience that are increasing day by day, reflecting its complex and multidimensional nature (Breetzke & Pearson, 2017). In relation to organizational resilience, it can be acknowledged that this category is a subcategory of positive organization, but today, even with attention to the term resilience and its many uses in various fields, especially in relation to organizational resilience, there is a limited theoretical and practical understanding of this concept in its evaluation, measurement or creation (Rafiyan, 2011). In general, two views have been mentioned in relation to organizational resilience, one of which defines resilience as the ability to return to the normal situation in the face of adversity, conflict, failure or positive events, and the other includes the development of new capabilities and the ability to expand simultaneously or even create new opportunities. Vogest also acknowledges that a resilient organization is considered to be able to maintain positive settings under challenging conditions (Hillmann & Guenther, 2021). Therefore, resilience is a necessity to respond to threats and also to adapt positively in the face of challenging conditions, accept opportunities, and provide sustainable performance (Duchek, 2020). Methodology The current research is developmental-applicative in terms of its purpose, and descriptive-survey in terms of data collection according to the nature of the research. In order to collect data in the qualitative part, in-depth semi-structured interviews were used, which were selected through purposeful and snowball sampling. The statistical population of the research included managers who are in government organizations of Kermanshah province and had at least 10 years of experience. The statistical population of quantitative section is a small part of the employees of the government organizations of Kermanshah province, whose number is 5641 people. Sampling in the quantitative section is 5 to 15 times the number of observed or apparent components (Homan, 2017). Considering that 33 primary components have been extracted, at least 190 questionnaires should have been distributed. The sampling method in this research was cluster. As a result, 400 questionnaires were distributed, of which 396 were usable. Discussion and Results The researcher analyzes the data in depth and presents them in the form of Strauss & Corbin theory (2008), and the main purpose of the research is to understand the situation. In this research process, after determining the core category, other categories are drawn in the form of a paradigmatic pattern around the core category. By using selective coding, the relationship between the categories has been identified and the conceptual model has been presented in an integrated manner. After making sure that there is a reasonable correlation and relationship between the observations and the related local variables, it is time for another important analysis, through which the statistical hypotheses of the current research will be investigated. In this part, the overview of the research model was examined first, and then each hypothesis was investigated separately. As it is clear in the previous figures, the value of t among the causal conditions, the central phenomenon, the consequences and the environmental background is higher than 1.96, which indicates the significance of the relationships between them. This shows that the model extracted in government organizations of Kermanshah is a suitable model. Also, the coefficients between the mentioned factors are all positive. Therefore, it is positive and direct between them. Also, in Table 13, the estimated coefficients and explained variance of the research variables are reported.   Conclusion The purpose of this research is to study resilience in government organizations of Kermanshah province. Therefore, while examining the concept of resilience; antecedents, environmental background and consequences of organizational resilience have been identified and a model of resilience has been presented in government organizations of Kermanshah province. After designing the research model, a number of interviewees were asked to express their opinions regarding the presented model so that required corrections can be made if necessary. The findings of the research were evaluated and reviewed by five university professors and three PhD. students, and necessary corrections were made while receiving opinions. In the following, explanations are given about each of the factors of the model. Based on the research model, "organizational resilience" as a central category based on the causal conditions of "individual, group and organizational factors" and taking into account the role of the government, economic and political factors and electronic platforms as a pattern is realized and leads to the creation of continuity in the organization and the progress of the organization. The research findings are in line with the results of Ruiz-Martin et al, (2018) and Lengnick-Hall et al, (2011).

Abstract Abstract The purpose of this research is to design a model for facilitating experimental sciences based on the reverse approach in elementary school. According to its purpose, the research method is a mixed exploratory research (qualitative-quantitative) based on thematic analysis and structural equation modeling. The statistical population of the research in the qualitative part includes 14 participants of the interview part (including experts in the field of curriculum, experts in the field of experimental sciences and teachers with high experience in elementary school) and participants in the texts content part (including documents published in reliable scientific databases in recent 5 years leading to the time of analysis and adjustment of the fourth season (2017 to 2022). The sampling method in the interview section is purposeful snowball type; and in the text section it is purposeful based on the purpose of the research. The statistical population in the quantitative section includes all primary school teachers in Khuzestan province with a sample of 193 people and was selected by multi-stage cluster random sampling method. The tool of the qualitative part is an interview, and that of the quantitative part is a questionnaire. The analysis of the qualitative part is using the theme analysis method proposed by Atride-Sterling (2001), and the quantitative part is SPSS and Amos software. The results showed that the facilitation model includes six organizing themes: objective and visual teaching, teacher activity in the reverse method, teacher-student interaction, teaching for learning, teacher motivation and ability, and active participation of families, which has a total of 31 basic themes. In the validation of the designed model, the examination of the structural model showed that the above model has a good fit. Extended abstract Introduction One of the important and practical lessons during education is experimental science, which is taught to students from the moment they enter school until the end of the academic year (Raisi Ardali, 2020). The purpose of teaching experimental sciences in the primary school curriculum is to educate people with scientific literacy, who must be equipped with knowledge, skills and scientific insight of behavior based on values ​​and competences, and who must understand the phenomena related to experimental sciences and are familiar with science and technology (Simsek, 2020). Reviewing the results of the Tims test, which is the most important international adaptation study in the field of teaching experimental science and mathematics in the fourth grades of primary along with eighth grades of secondary school, and is held every four years under the supervision of the International Association for the Evaluation of Academic Progress (Zianejad Shirazi et al, 2022), shows Iran's poor performance in mathematics and experimental science lessons in the fourth grade of elementary school compared to other countries (Kasyani N, Zarei, 2019). The 21st century educational systems emphasize the importance of paying attention to student-centered learning in which students participate and direct their own learning. In student-centered learning environments, students must actively participate in learning. Participation should be organized through learning activities such as discussion, problem solving, and peer learning. These types of activities are known in a new method called the reverse learning method or the reverse class (Kazu, 2020). But despite the fact that in recent years, the reverse learning method has been proposed as an alternative model of education to improve students' knowledge and skills, interaction and self-efficacy in learning; mostly this method has been neglected in the field of teaching experimental sciences (Ahmed & Indurkhya, 2020). Reverse learning is a type of blended learning that reverses traditional perceptions of pre-class and in-class activities. This teaching method gives students the opportunity to have more control over their learning. However, teachers should check whether students' activities in the classroom lead to more pre-learning or not (Nuhoglu Kibar, 2020). Based on this, the current research is looking for an answer to this question: What is the pattern of facilitating experimental sciences based on the reverse approach in the elementary school? Theoretical Framework Reverse learning Reverse learning is an approach that has aroused the interest of researchers and educators today. Reverse learning, as the name suggests, is exactly the reverse of the traditional teaching method. If we want, this learning style can be summed up in one sentence: doing school work at home and doing homework at school. It is an educational model in which students learn educational content at home through online or offline educational videos, and the teacher in the classroom solves the problems and answers the students' questions and talks with them (Khorshidi & Ghaidi, F, 2022). Aybirdi & Atasoy sal (2023) investigated the effect of reverse learning on the achievements of L2 learners. They showed that reverse learning had significant effects on students' scientific achievements compared to the traditional method. Dong (2021) in a research entitled the effectiveness of the flipped classroom in collaborative learning showed that the flipped classroom was effective in improving academic performance and promoting higher level thinking abilities, such as critical thinking and self-recognition and evaluation. Research methodology According to its purpose, the research method is a mixed exploratory research (qualitative-quantitative) based on thematic analysis and structural equation modeling. The statistical population of the research in the qualitative part includes 14 participants of the interview part (including experts in the field of curriculum, experts in the field of experimental sciences and teachers with high experience in elementary school) and participants in the content part of the texts (including documents published in reliable scientific databases in last 5 years lead to the time of analysis and adjustment of the fourth chapter (2017 to 2022). The sampling method in the interview section is purposeful snowball type; and in the texts section it is purposeful based on the purpose of the research. Statistical society in the quantitative part includes all primary school teachers in Khuzestan province with a sample of 193 people, which were selected by multi-stage cluster random sampling method. The tool of qualitative part is interview, and of quantitative part is questionnaire. Research findings To analyze the qualitative part, the method proposed by Atride-Sterling (2001) was used through the theme analysis method; and SPSS and Amos software were used to analyze the quantitative part. The results showed that the facilitation model includes six organizing themes: objective and visual teaching, teacher activity in the reverse method, teacher-student interaction, teaching for learning, teacher motivation and ability, and active participation of families; which has a total of 31 basic themes. In the validation of the designed model, the examination of the structural model showed that the above model has a good fit. Conclusion The current research was conducted with the aim of designing a model for facilitating experimental sciences based on the reverse approach in elementary school. The results of this research are in agreement with the results of Aybirdi & Atasoy sal (2023), Dong (2021), Sahebyar et al, (2021), Jafarkhani et al, (2020), Tajari & Bayani (2019), Niro & Hajian (2020), Smallhorn (2017). Based on the study of Jafarkhani et al, (2020), the implementation of the reverse learning method has a positive effect on the learning and motivation of multi-grade students and helps the multi-grade teacher in the learning process and classroom management. Erkan & Duran (2023) in the context of the fourth elementary science lesson show that basic activities using reverse teaching have a positive effect on students' scientific creativity and their basic activity; and with this method, most students find the activities useful, structured and entertaining. According to the results of the research, the following suggestions are presented: It is necessary for educational systems in different educational levels, especially in the primary period, to use a creative reverse approach and use the opportunities and facilities of educational films and videos, internet spaces, and educational podcasts. In this regard, teachers should provide elementary students with new subjects of experimental sciences in the form of video, podcast or audio recording, and ask them to observe and take notes and bring them to class as homework.

Abstract Abstract The present study aims to design a model of the dimensions of merit-based managerial governance with an intelligentization approach to the administrative system. In the qualitative phase, the research method was descriptive–exploratory, employing the meta-synthesis method. In the quantitative phase, a descriptive-survey approach was adopted using Interpretive Structural Modeling (ISM). The statistical population in the qualitative phase (meta-synthesis section) consisted of 415 articles in the fields of merit-based managerial governance and intelligent organizations. Using the Sandelowski and Barroso method, 45 articles were selected as the sample. Among 20 experts, 15 were purposively selected to participate in a focus group discussion regarding the dimensions of the model. In the quantitative phase, 15 experts were engaged to respond to the CASP questionnaire and complete self-interaction matrices within the framework of Interpretive Structural Modeling. The results of the meta-synthesis identified 38 components within 125 indicators. Through the focus group process, 20 components were categorized into 10 dimensions, including process governance, structural and organizational governance, managerial governance, ethics-oriented governance, economic and social governance, legal and regulatory governance, benevolent behavior governance, technology- and expertise-oriented governance, and political governance. Finally, using the Interpretive Structural Modeling method, the proposed model was designed and the types of variables were determined. Introduction After World War II, governance emerged as a new concept in the development literature. In 1989, the World Bank first used the term governance to describe the need for institutional reforms in African countries. Subsequently, the concept of good governance was used by the President of the World Bank to refer to efficient public services, a reliable judicial system, and an accountable government (Kamrani et al., 2023). On the other hand, in the knowledge era, intelligence is one of the undeniable requirements for organizational survival and represents the total knowledge that an organization possesses about the environment in which it competes. In fact, an intelligent organization should function as a system in which participatory decision-making is adopted, employee collaboration and team building are considered fundamental principles, and decentralization is encouraged in order to facilitate organizational learning and process integration (Mehdibeigi & Yaghoubi, 2021). In the contemporary era, attention to the characteristics of merit-based governance based on religious teachings and adopting them as a model is of considerable importance, as it can provide a guiding framework for Islamic governments (Tahmasbi Baldaji, 2023). Nahj al-Balagha, like a shining sun, has continuously illuminated the course of history, guiding sincere seekers of truth and satisfying the spiritual thirst of those in search of knowledge. Therefore, this study seeks to explore the concept of merit-based governance based on the teachings of Amir al-Mu’minin (Imam Ali, peace be upon him). Accordingly, the research question addressed in this study is: What is the model of merit-based governance for an intelligent organization based on the teachings of Nahj al-Balagha? To clarify the theoretical gap in the literature, previous studies such as Newman et al. (2022), Ismail and Al‑Assa’ad (2020), Dovali et al. (2023), Yaghoubi et al. (2022), Zohrabi et al. (2022), and Saberifar (2020) have examined factors related to intelligent governance, including the strength of democracy, smart businesses, social cohesion, policies and regulations, and intelligent economic competition. These studies have also discussed challenges of intelligent governance such as the lack of digital infrastructure development, gaps in data and statistics within governance systems, digital security issues, challenges related to digital literacy, weaknesses in inter-organizational information integration, and limitations in big data visualization and dashboarding in governance systems. Moreover, some studies have addressed elements of intelligent organizations, including intelligent mobility, intelligent living, intelligent people, intelligent economy, and intelligent environment. Furthermore, studies on governance within Nahj al-Balagha, such as those by Montazeri et al. (2018) and Rastegar and Mousavi Davoudi (2022), have identified factors such as religious transparency, behavioral transparency, structural transparency, economic transparency, rule of law, public participation, openness to criticism and accountability, responsibility and responsiveness, effectiveness and efficiency, and meritocracy as components of merit-based governance. However, a review of previous research indicates that most studies have examined good governance or merit-based governance separately. Few studies have attempted to design a comprehensive model of merit-based governance specifically for organizations. Therefore, considering the above, the main research question of this study is: How can a model of the dimensions of merit-based managerial governance be designed with an intelligentization approach to the administrative system? Theoretical Framework Merit-Based Governance Merit-based governance encompasses the mechanisms and processes through which citizens, groups, and civil institutions pursue their interests, exercise their legal rights, and fulfill their obligations. In general, governance emphasizes the concept of partnership rather than mere participation (Bagherzadeh Khodashahri et al., 2023). Intelligent Organization Intelligent organizations need to adapt to their environment in order to perform smart and timely actions (Hamidzadeh et al., 2023). In the global knowledge economy, the task of an intelligent organization is to address the problem of knowledge obsolescence by providing timely access to learning content and identifying the best ways to obtain knowledge resources that are increasingly distributed (Gope et al., 2018). The development of this concept toward becoming a society reflects the emergence of intelligent organizations (Mehdibeigi & Yaghoubi, 2021). Dovali et al. (2023), in a study entitled Designing an Organizational Intelligentization Model, stated that the categories related to the concept of intelligentization include tools for adapting to environmental changes, transformation into a learning organization, and the use of technology. The causal subcategories include organizational factors, human factors, technical factors, and economic factors. The contextual conditions include organizational culture, decision-making infrastructures, legal infrastructures, and business skills, while the strategic categories include decision-making strategies, economic strategies, cultural development, and the provision of infrastructures. Tahmasbi Baldaji (2023), in a study entitled Analyzing the Characteristics of Merit-Based Governance Based on Nahj al-Balagha, examined the key indicators of merit-based governance and analyzed them within cognitive, political, social, and economic dimensions. Intelligent Organization Intelligent organizations need to adapt to their environment in order to carry out intelligent and timely actions (Hamidzadeh et al., 2023). In the global knowledge economy, the role of an intelligent organization is to address the problem of knowledge obsolescence by ensuring timely access to learning content and identifying the most effective ways to obtain knowledge resources that are increasingly distributed (Gope et al., 2018). The development of this concept toward becoming a knowledge-based society reflects the emergence and expansion of intelligent organizations (Mehdibeigi & Yaghoubi, 2021). Dovali et al. (2023), in a study entitled Designing an Organizational Intelligentization Model, stated that the categories related to the concept of intelligentization include tools for adapting to environmental changes, transformation into a learning organization, and the use of technology. The causal subcategories include organizational factors, human factors, technical factors, and economic factors. The contextual conditions include organizational culture, decision-making infrastructures, legal infrastructures, and business skills, while the strategic categories consist of decision-making strategies, economic strategies, cultural development, and the provision of infrastructures. Tahmasbi Baldaji (2023), in a study entitled Analyzing the Characteristics of Merit-Based Governance Based on Nahj al-Balagha, examined the key indicators of merit-based governance and analyzed these indicators within the cognitive, political, social, and economic dimensions. Research Findings To analyze the research findings, the meta-synthesis method and Interpretive Structural Modeling (ISM) were employed. The results of the meta-synthesis indicated that 38 components were identified within 125 indicators. Through the use of a focus group, these components were consolidated into 20 components across 10 dimensions, including: process governance, structural and organizational governance, managerial governance, ethics-oriented governance, economic and social governance, legal governance, benevolent-behavior governance, technology- and expertise-oriented governance, and political governance. Finally, using the Interpretive Structural Modeling method, the proposed model was designed and the types of variables and their relationships were determined. Conclusion The present study aimed to design a model of merit-based managerial governance dimensions with an approach toward the intelligentization of the administrative system. The results of this study are consistent with the findings of previous studies, including those by Dovali et al. (2023), Tahmasbi Baldaji (2023), Manouchehri and Karimi (2023), Nasuri (2023), Alavian et al. (2023), Nohadi and Siahkali Moradi (2021), Montazeri et al. (2018), Falletta and Combs (2019), Pazireh et al. (2019), and Istudor et al. (2016). Nohadi and Siahkali Moradi (2021) indicated that the greatest difference between the two types of governance lies in accountability. There are also major differences in combating corruption; however, there is less divergence between the two governance types in terms of transparency and rule of law. Governance in the Islamic Republic of Iran differs from other governance systems in the world. The primary distinction lies in the fact that governance within an Islamic system must be grounded in Sharia principles. Another important issue concerning good governance is legislation. Lawmaking requires thoughtful consideration and research; therefore, since institutions such as the Guardian Council and some other bodies operate at the final stage of the legislative process, intermediary institutions should play a more active role in supporting the legislative process so that public issues can be addressed more accurately and effectively.

Abstract Abstract This research was done with the aim of providing a systematic model of employee training using artificial intelligence. The research method is a combination of qualitative and quantitative methods. The statistical community in the qualitative section includes an unlimited number of experts familiar with the subject, and the statistical sample in this section is 20 people selected by the snowball method. The statistical population in the quantitative section includes all the specialists and experts related to the research subject in an unlimited number, of which 384 people were selected as a sample by a simple random method. Researcher-made questionnaires with confirmed validity and reliability were used to collect the data of this research. Data analysis in this research was done at two levels of descriptive statistics and inferential statistics. To complete the Delphi process in this research, Kendall's coefficient was used with the help of SPSS software. In order to test the research model, structural equation technique was used through Smart-PLS statistical software, and the results show the appropriate fit of the model. The findings of the research show that the inputs of the model include 1- educational data, 2- personal information, 3- educational needs, 4- user feedback, and 5- data of the work environment. The model process also includes 1- determining the needs and goals, 2- collecting data, 3- pre-processing the data, 4- training the artificial intelligence model, 5- evaluating and improving the model, 6- implementing and deploying, and 7- monitoring and updating. Finally, the outputs of the model include 1- individual feedback, 2- educational suggestions, 3- monitoring and follow-up, and 4- support and guidance. Extended abstract Introduction In 1950, Alan Turing, an English mathematician, wrote an article entitled "Computing Machines and Intelligence" and posed the question: "Can machines think?" This led to further exploration of the use of machines to support human decision-making, with the formation of a workshop in 1956. The workshop was organized by John McCarthy, an American mathematician, who focused on the "study of artificial intelligence" (Frehywot, 2023). As science and technology advance, since 2013, when Frye and Osborne estimated that nearly half of US jobs are at risk of high automation, AI has been at the top of policymakers' agendas, and now the consensus is that AI creates fundamental changes in the labor market. With the use of artificial intelligence, many skills that were important in the past become automated; many jobs are also obsolete or transformed; and artificial intelligence is increasingly used (Tuomi, 2018). Artificial intelligence includes various related technologies, often supported by machine learning algorithms, whereby it achieves set goals through supervision (human-guided) or unsupervised (autonomous machine) (Walsh et al., 2019). Today, most experts believe that the implementation of smart technology will dynamically transform work environments. There are applications of artificial intelligence in all industries and professions, and human resource management is no exception to this rule. Therefore, for the organization to remain relevant and maintain its competitive advantage, it is necessary to adopt new technological developments (Kaushal et al., 2023). Today, the surprising speed of innovation in business processes and technology requires that the employees of organizations continuously acquire new skills and be able to adapt to changing practices. Thus, educational needs become more personalized (Ford et al., 2017). Employee skill development was once done entirely by on-the-job supervisors; but now with the increasing demand for new skills, technological advances have enabled training and development on mobile platforms, such as smartphones and laptops (Maity, 2019). Artificial intelligence should be applied to organizational learning to help companies solve their training challenges. When recruiting and hiring new employees, the main challenge is to quickly and effectively make them fully aware of and understand the organization's internal policies and procedures. Machine learning features have been incorporated into various HR software systems to facilitate greater efficiency (Iqbal, 2018). Even more comprehensively, the use of various artificial intelligence technologies can help companies develop a learning organizational culture and avoid the common training design model based on traditional competency model analysis (Chen, 2023). According to the above, considering the development of digital technology, especially artificial intelligence, and the increasing demand for personalized training, the past training methods are no longer able to meet personal needs, and the adoption of artificial intelligence-based training can effectively fill the shortage of personalized training. Therefore, the main question that the researcher seeks to answer in this research is: "What is the system model of employee training using artificial intelligence?" Theoretical foundations Artificial intelligence is often defined as a computer system with the ability to perform tasks normally associated with intelligent beings. The first explicit definition of artificial intelligence came in a funding proposal to the Rockefeller Foundation in 1955. This definition states that "any aspect of learning or any other characteristic of intelligence can originally be described so sufficiently precise that a machine can be built to simulate it." This initial definition quickly led to deep discussions. In practice, the early developers of artificial intelligence interpreted intelligence and thinking as the mechanical processing of logical statements (Tuomi, 2018). In another definition, artificial intelligence is defined as "making a machine behave in a way that would be called intelligent if a human behaved." Although artificial intelligence was defined in 1955, it has recently gained worldwide recognition due to the technological revolution. Artificial intelligence is discussed as non-human intelligence designed to perform specific activities and tasks (Kaushal et al., 2023). McCarthy describes artificial intelligence as the science and engineering of building intelligent machines through algorithms or sets of rules, that the machine follows to mimic human cognitive functions, such as learning and problem-solving (Frehywot, 2023). This definition provides a combination of AI capabilities and what it is. Human resource management has undergone an early revolution with the help of artificial intelligence, which has gradually affected human resource operations. These functions, which were already performed entirely by humans, are being recreated with the help of a computer assistant. HR functions such as performance appraisal, learning and development, and talent acquisition are some of the areas where artificial intelligence has been introduced (Kaushal et al., 2023). By means of a variety of artificial intelligence technologies, it can be more comprehensive to help companies to form a learning organization culture that uses the traditional instructional design model based on traditional gap analysis. A customized curriculum can comprehensively test and locate staff levels through technical tools, and intelligently promote customized courses (Jia et al., 2018). In the process of education, AI technology can help learners automatically record learning data. Employees can simply enter learning objectives, archives and learn key points, and the course is automatically completed by the AI teacher (Jia et al., 2018). Methodology The current research is applicable in terms of its purpose. In terms of the method of data collection, it is descriptive-correlation in nature. The statistical population of this research in the qualitative part is made up of experts related to management issues (human resource management and system design) with a master's degree or higher in Sistan and Baluchistan province. In the quantitative part, all the employees of the departments of Sistan and Baluchistan province who have a bachelor's degree or higher, formed the statistical population of this research. The statistical population is considered unlimited in both qualitative and quantitative sections. Therefore, the sample size in the qualitative section is considered to be 20 people, selected by snowball method. In the quantitative part, Cochran's formula was used for unlimited communities with an error level of five percent, and the sample size of 384 people was determined and selected by simple random method. The tool of data collection in this research was researcher-made questionnaires. SPSS and Smart-PLS statistical software were used to analyze the data of this research. Findings The value of Cronbach's alpha for all variables of the research questionnaire is more than 0.7. Therefore, the research questionnaire has acceptable reliability. Also, the composite reliability value of all variables is higher than 0.7, which indicates the internal consistency of the reflective measurement model. All values related to convergent validity are also above 0.5, which indicates the similarity or internal validity of the reflective measurement model. Also, the reliability of the data collection tool and the quality of the reflective measurement model have also been confirmed. Conclusion Considering the importance and necessity of organizations to use artificial intelligence in the process of human resources management, a model for training employees using artificial intelligence was presented in this research, which has three parts according to the system mode: input, process and output. Model inputs include 1- educational data, 2- personal information, 3- educational needs, 4- user feedback, and 5- workplace data. The process of the model includes 1- determining the needs and goals, 2- collecting data, 3- pre-processing the data, 4- training the artificial intelligence model, 5- evaluating and improving the model, 6- implementing and deploying, and 7- monitoring and updating. The outputs of the model include 1- individual feedback, 2- educational suggestions, 3- monitoring and follow-up, and 4- support and guidance.  

Abstract Abstract The aim of this study is to present a model for teachers' lived experiences of happy ecosystem educational opportunities in the post-corona era. The research method is applicable in terms of its purpose, and mixed (qualitative-quantitative) in terms of its implementation method, and is phenomenological in nature in the qualitative part and descriptive in the quantitative part. The statistical population of the research in the qualitative part consisted of 15 experts, including teachers with more than 10 years of experience and expertise in the field of educational management and educational sciences, selected using the purposive sampling method, and the statistical population in the quantitative part consisted of 890 teachers from all e-learning schools in Khorasan Razavi Province, of whom 268 were selected through the Cochran formula. Coding and content analysis were used to analyze the data, and PLS was used in the quantitative part. The results of the study included twelve components and four central dimensions of expanding the learning situation, expanding and disseminating education, the point of transformation and quality, and the rotation of the role of teachers. The findings showed that benefiting from the educational opportunities of the happy ecosystem in the post-corona era requires a change in teachers' views on virtual education and there is a need to implement coherent policies in this regard. If the current practice regarding the happy ecosystem continues and its management process is neglected, it will lead to the loss of the capacity of virtual education and a return to the pre-corona era. In the quantitative section, the results showed that all the relationships between the variables are significant. The GOF results also showed that the model has a strong fit. Introduction Today, human life has undergone rapid global changes and is tied to technological advances (Nouri et al. 2021). The education system is no exception to this rule and is always seeking to use new technologies to improve the teaching and learning process (Ebrahimi et al. 2022). With the rapid spread of the coronavirus in the second decade of the 21st century, many aspects of human life have changed, and the social and individual lives of people in all parts of the world have been affected in various dimensions, such as economic, social, and political (Ajand & Farazandepour, 2022). The need for health for students and teachers to prevent disease has led to many schools being closed and out of their current form. Such an event has led to many government policies and actions using cyberspace to find a solution to the school closure crisis (Bagherzadeh, 2020). In developed countries around the world, the field of virtual education is expanding for all segments of society, and with the coronavirus pandemic, opportunities for using virtual platforms have immediately expanded and spread (Abdelhafez, 2021). The COVID-19 pandemic provides an opportunity to identify weaknesses, deficiencies, and shortcomings, and to highlight the imbalance in the distribution of infrastructure needed in the cyberspace sector, especially in remote and deprived areas, and to identify these deficiencies and work to address them (Nissim & Simon, 2020). This is seen as growth and excellence in this field and a positive aspect of the situation that has arisen, and a starting point for continuing and improving educational quality in the future (O’Brien et al., 2020). In Iran, in line with educational developments around the world, a student education network called SHAD was launched (Kaveh Nooshabadi & Lotfi Mofrad, 2021). The new SHAD platform and the provision of education in cyberspace require a change in the orientation of teachers and their familiarity with the elements of teaching and learning, such as teaching, content, and evaluation in cyberspace (Karimi et al., 2022). Therefore, such a change and the move from face-to-face classes to virtual classes was the starting point of the transformation in the Iranian education system (Kondori et al., 2022). Hence, the main question of the present study is: what is the model of teachers' lived experiences of educational opportunities in the happy ecosystem in the post-Corona era? Theoretical framework Virtual education Distance education via the Internet (e-learning) or local networks in a way in which a teacher as an educator and a learner are separated is called virtual education (Soleimani & Asghari, 2021). Allavi Gharabat et al. (2025) investigated the identification of the dimensions and components of virtual Arabic language education in Iraq. The findings of this study showed that virtual Arabic language education in Iraq had 66 basic codes, 22 organizing codes, and 8 comprehensive codes. In this study, the overarching codes include teacher professional development, student engagement, global trends and technological advancements, curriculum and instruction development, stakeholder engagement, continuous improvement and evidence-based practice, digital skills preparation and career readiness, and online learning environment considerations. Finally, a model of dimensions and components of virtual Arabic language education in Iraq was designed. Yarahmadi et al. (2024) investigated the process of learning physical education in education through virtual education. The results obtained from the analysis showed that the educational limitations of movements and practical lessons, lack of supervision and concentration in virtual education, weak communication interaction between teachers and students, coverage of virtual classes, economic issues, weak software and hardware technology and infrastructure, use of information technology in virtual education, insufficient knowledge of the space and capabilities of virtual education, lack of supervision and limitations on physical ability and implementation of lessons and capabilities of virtual education were categorized. Finally, using the final access matrix, using the structural-interpretive method, the sub-themes of the design of virtual education for physical education in Iranian education were classified, which is at the highest level of coverage of virtual classes and at the lowest level of teaching capabilities of virtual education. It should be noted that factors that have less influence at a high level.  Research Methodology The research method is applicable in terms of its purpose, mixed (qualitative-quantitative) in terms of its implementation method, and phenomenological in nature in the qualitative part and descriptive in the quantitative part. The statistical population of the research in the qualitative part consisted of 15 experts, including teachers with over 10 years of experience and expertise in the field of educational management and educational sciences, who were selected using the purposive sampling method, and the statistical population in the quantitative part consisted of 890 teachers from all e-learning schools in Khorasan Razavi Province, of whom 268 were selected using the Cochran formula. Research findings Coding and content analysis were used to analyze the data, and PLS was used in the quantitative part. The results of the research included twelve component and four axial dimensions of expanding the learning situation, educational expansion and dissemination, the point of transformation and quality, and the rotation of the role of teachers. Based on the findings of this study, it can be concluded that benefiting from educational opportunities in the post-corona era requires a change in teachers' views on virtual education and there is a need to implement coherent policies in this regard. If the current practice regarding the happy ecosystem continues and its management process is neglected, it will lead to a loss of virtual education capacity and a return to the pre-corona era. In the quantitative section, the results showed that all the relationships between the variables are significant. Also, the GOF results showed that the model has a strong fit. Conclusion The present study was conducted with the aim of providing a model for teachers' lived experiences of educational opportunities in the post-corona era. The results of this study are consistent with the results of Qasem Tabar & Qasem Tabar (2021), bakouei et al. (2021); Shams & Rezvanyan (2021), Jafari et al. (2021); Faraji et al. (2022); Babazadeh et al. (2022), Torkashvand et al. (2021), Hatami et al. (2021); Jafari et al. (2021); Saberi & sharifzade (2019); Mirzaei (2022); hamidizade et al. (2022); faraji & Fekri (2022); Davaji et al. (2021); and Namdari et al. (2017). The results of the present study indicate that the process of continuing to improve the quality of virtual education and paying attention to a happy environment in the post-corona era has been neglected and its most important implementation element, namely teachers, has been forgotten. Dispersed policies have led to sectional and minimal decisions being put on the agenda and the capacity of virtual education and a happy environment in schools has practically diminished. Based on the results of the research, it is suggested that teachers' virtual actions and activities in the form of technological components be considered in their ranking discussion in order to benefit from its financial benefits.

Abstract Abstract
The present study was conducted with the aim of designing a model for applying artificial intelligence in elementary school students' learning. This study was applicable in terms of its purpose, and mixed and exploratory in terms of the type of data; in such a way that the qualitative part was conducted using the paradigmatic grounded theory method and the quantitative part was conducted using a survey method. Participants in the qualitative part included academic experts and scholars from the Ministry of Education selected through conscious sampling and the snowball method; and in the quantitative part, 319 education managers and elementary school principals in Tehran were determined based on the Cochran formula. Data collection in both parts was conducted using the field method; semi-structured interviews were used in the qualitative part and a researcher-made questionnaire was used in the quantitative part. The validity and reliability of the instruments were confirmed in both parts. Qualitative data were analyzed by theoretical coding, and quantitative data by confirmatory factor analysis. The findings showed that the AI ​​application model consists of 19 components and 106 indicators in the form of six dimensions of the paradigm model. In this model, the central phenomenon includes learning enrichment and learning individualization; causal factors include technical infrastructure, intelligent support, and technology growth in the family; contextual factors include learning management, learning content, and teacher capabilities; intervening factors include ethics, information security, and educational technology; strategies include personalization of education, feedback, intelligent evaluation, content quality, and learning assistant; and outcomes include deep learning, improving teacher efficiency, high-quality evaluation, and educational innovation. Finally, the obtained model was quantitatively evaluated with confirmatory factor analysis, and the fit indices showed that the model was confirmed.
Introduction
The rapid advancement of computing and information processing methods has accelerated the expansion of AI applications, with the aim of enabling computers to perform their tasks by simulating intelligent human behaviors such as inference, analysis, and decision-making (Duan, Edwards & Dwivedi et al., 2019).
AI in education has great potential to enhance learning, teaching, and assessment by suggesting or providing personalized or tailored learning to learners, developing teachers’ understanding of the learning process, and providing anywhere-anytime search engines and immediate feedback (Xia et al., 2022). AI in learning helps learners actively construct their own knowledge by exploring and manipulating elements of the learning environment (Randhawa et al., 2020).
AI helps to increase the quality of education and not only assists in the learning process but also provides facilities such as tutoring, grading, lesson planning, and feedback to students (Mondal et al., 2019). Timms stated that collaborative robots are used in collaboration with teachers to teach everyday tasks such as spelling, pronunciation, and adjusting students’ abilities (Timms et al., 2016). Artificial intelligence helps personalize content to meet the demand for education, so it plays a significant role in the organization of course materials (Mondal et al., 2019). Therefore, it can be said that it is one of the most important phenomena in education that will be used more in the future (Rezaei et al., 2024). According to the US Department of Education, it is estimated that artificial intelligence in education in this country will increase by 47.5% from 2017 to 2021, which is expected to see a significant growth in companies developing artificial intelligence in education (Liang et al., 2020). During the emergence of Corona, educational technologies, especially artificial intelligence, were used in many countries, but in Iran, very little of artificial intelligence has entered the field of education. Despite the fact that the use of modern educational and training equipment and technologies in line with the goals of education has been considered in the document on the fundamental transformation of education, artificial intelligence has been taken for granted; and only education and training, teachers, and students use virtual spaces as modern educational technologies; while artificial intelligence has spread around the world and provides significant help to teachers and students. Accordingly, the researcher in this study seeks to answer the question: What is the model for using artificial intelligence in learning for elementary school students in Tehran province?
Theoretical foundations
Infrastructure and technological background
Educational developments based on artificial intelligence require appropriate technical infrastructure and access to modern technologies for families. Infrastructure includes servers and network storage, educational software and applications, appropriate bandwidth, multimedia tools, and information processing equipment that enable smart education and active student interaction (Li et al., 2020). In addition, parental mental preparedness, family economic and cultural status, and the use of artificial intelligence tools in daily life are effective contexts for the acceptance and use of smart systems (Chen et al., 2020).
Smart support enables access to content at any time and place, personalized services, and active student interaction with content, complementing the teacher's role in the education process, which is in line with the active learning theory (Rezavan et al., 2022).
Learning Management and Educational Content
Learning management involves identifying student needs, continuously monitoring the learning process, providing feedback and continuous evaluation; and improves the quality of education (Tsai et al., 2019). Learning content should be in line with educational objectives, utilize interactive tools such as games and videos, and enhance students’ academic achievement. Teachers’ ability to use artificial intelligence for teaching and content production has a direct impact on the effectiveness of education (Zhu et al., 2020).
Content personalization strategies, providing immediate feedback, and intelligent assessment facilitate individual and active learning and take into account individual student differences (Popenici & Kerr, 2017).
Intervenors and Ethics in Smart Education
The use of smart technologies in education is associated with ethical and security considerations. Respecting privacy, trustworthiness in the use of content, and developing security infrastructures are critical components (Livingstone, 2012). Educational technology, including artificial intelligence tools, media, and virtual reality technology, acts as an intermediary between the student and the learning process and enables continuous and personalized learning through timely intervention and resource management.
Implications of Smart Learning
The use of artificial intelligence leads to deep learning, improved teacher efficiency, accurate evaluation, and educational innovation. Deep learning is possible by strengthening cognitive skills, increasing the ability to analyze and solve problems, active student participation, and repeating material until complete learning. By continuously monitoring student progress and utilizing smart evaluation, teachers can provide timely corrections and feedback, and improve learning outcomes by combining face-to-face and virtual methods and producing multimedia content (Li et al., 2020).
Sun (2024) examined various uses of artificial intelligence in education in a study titled "Applications of Artificial Intelligence in Education." The research method of this study was a review and analysis. The results showed that AI can improve personalized learning, adaptive learning, virtual reality technology, and teaching assessment, and change the traditional learning system by providing new teaching methods and assessment systems.
Alam et al. (2024) analyzed the future role of AI in education in a study titled "Future Applications and Prospects of AI in Education". The research method of this study was analytical and descriptive. The results showed that AI, with active participation in data analysis, virtual classrooms, adaptive assessments, and personalized learning, can create more effective and efficient educational experiences and requires the participation of educators and legislators to fully realize this potential.
Research Methodology
This research is applicable in terms of purpose, and mixed exploratory (qualitative and quantitative) in terms of data. The qualitative stage was conducted based on systematic data, and the statistical population included 19 academic experts, educational technology specialists, and education managers, of which 16 interviews were used as the basis for the analysis. The quantitative phase was conducted as a cross-sectional survey with a statistical population of 1880 school principals and educational districts in Tehran, and 319 people were selected by stratified random sampling. Qualitative data were collected through semi-structured interviews and library studies, and quantitative data were collected with a researcher-made questionnaire consisting of 19 components and 106 indicators. The validity and reliability of the tools were confirmed by test-retest methods, model reliability test, and inter-coder test, respectively, at 80–86%
Research findings
The research findings showed that the application of artificial intelligence in elementary school students' learning has several basic dimensions that interact with each other in a systematic way. On one hand, technical infrastructure and intelligent support enable active, personalized, and continuous learning, and on the other hand, teacher empowerment and content management ensure that the education process proceeds with quality and effectiveness. Ethical considerations and information security, along with educational technologies, as intervening factors, ensure the safe and sustainable use of systems. Content personalization strategies, feedback, smart assessment, and learning assistants enhance students' individual and collaborative learning, leading to outcomes such as deep learning, educational innovation, improved teacher effectiveness, and quality assessment. 
Discussion and Conclusion
The research findings show that implementing smart learning in Tehran elementary schools requires appropriate technical infrastructure and families’ access to modern technologies. Servers, bandwidth, software, and multimedia tools enable smart learning and active student interaction with content (Chen et al., 2020; Rezavan et al., 2022). The readiness and support of teachers and families, especially a positive attitude towards educational technologies, play a key role in the success of smart learning (Valeri et al., 2024; Alam et al., 2024).
In learning and educational content management, identifying student needs, providing continuous feedback, and regular evaluation improve the quality of education (Tsai et al., 2019). The use of interactive tools and multimedia content enhances students’ active participation and academic achievement (Zhu et al., 2020; Sun, 2024). Teachers’ ability to utilize AI for teaching and content production also has a direct impact on learning effectiveness (Chen et al., 2022).
Ethical and security considerations, including protecting students’ privacy and developing security infrastructure, are essential for the successful implementation of smart technologies (Wang et al., 2024; Omar et al., 2024). Educational technologies, through media, content production tools, and virtual reality, enable interactive and personalized learning and reduce teachers’ workload (Sun, 2024).
The implications of using AI in learning include enrichment, individualization, and active learning. Providing diverse examples, simulating real-world situations, and purposeful exercises facilitates deep and active learning (Valeri et al., 2024; Sun, 2024). Content personalization strategies, immediate feedback, and intelligent assessment enable effective learning that is responsive to individual differences (Chen et al., 2022; Zhu et al., 2020).
Finally, AI can help improve the quality of learning, increase student engagement, improve teacher efficiency, and innovate in education (Alam et al., 2024; Wang et al., 2024). This technology enables a more effective, efficient, and personalized learning experience for students.

Abstract Abstract
The current research aims to investigate a model for enhancing knowledge management based on intellectual and specialized capital in the banking industry. The research methodology, considering its objective, is developmental-applicable; and in terms of execution, it is mixed (qualitative-quantitative). The statistical population for the qualitative phase includes 18 university professors, managers, and experts from Bank e-Mehr Iran, selected through purposive sampling. The statistical population for the quantitative phase consists of managers and experts from Bank e-Mehr Iran in Tehran. According to Morgan and Gracey’s table, the maximum sample size was considered to be 235 individuals, selected using simple random sampling. Data collection in the qualitative phase was conducted through semi-structured interviews; and in the quantitative phase, through questionnaires. Content analysis was applied for analyzing qualitative data, and SPSS and PLS software were utilized for quantitative data analysis. Qualitative analysis revealed that knowledge management in organizations encompasses six main dimensions: intelligent knowledge infrastructures, knowledge empowerment, internal organizational value creation, external organizational value creation, comprehensive knowledge support, and knowledge culture. Confirmatory factor analysis indicated that the proposed model possesses adequate construct validity and convergent validity for all dimensions, with the items exhibiting high and significant factor loadings. As a practical suggestion, it is recommended that organizations enhance flexible knowledge infrastructures and foster a culture of continuous learning. This will enable them to practically leverage employees’ experiences and knowledge, thereby sustainably developing the organization’s intellectual capital while improving employee knowledge management.
Introduction
In today’s world, where organizational competitive advantage is more than ever based on knowledge, creativity, and innovation; banks, as leading financial institutions, are inevitably compelled to recreate their structures in line with the knowledge age. In such a context, knowledge management is not merely a tool for storing and transferring information, but a strategic approach that can enhance the flow of knowledge creation, sharing, and utilization across all organizational levels, paving the way for accurate decision-making, service innovation, and improved customer experience (Pisoni et al., 2024).
Intellectual capital in banking encompasses a collection of employees’ professional knowledge, financial analysis skills, effective customer relationships, accumulated experiences in operational processes, as well as structural systems and IT that empower the organization in optimal financial service management (Quezada et al., 2025). Knowledge management is a process that an organization uses to collect, organize, share, and analyze its knowledge in a way easily accessible to employees. This knowledge can include technical resources, FAQs, training documents, and other information (Afshari et al., 2020).
A knowledge management system harnesses the collective intelligence of an organization, leading to better operational efficiency. These systems are supported by a knowledge base. They are typically crucial for successful knowledge management, providing a centralized location for storing information and ensuring easy access to it. Companies with a knowledge management strategy achieve business results faster because increased organizational learning and collaboration among team members facilitate quicker decision-making across the business (Asayesh & Mehdikhani, 2025). It also simplifies more organizational processes such as training and board presence, leading to reports of increased employee satisfaction and retention (Faizolahy et al., 2023). Knowledge management is not a new concept; human civilizations have historically preserved and transferred knowledge from one generation to another to understand the past and anticipate the future. In today’s complex and dynamic business environments, the thirst for up-to-date knowledge continues to expand in scope and depth (Mehdikhani & Valmohammadi, 2020). Therefore, this research seeks to answer the question: What is the model for enhancing knowledge management based on intellectual and specialized capital in the banking industry?
Theoretical Framework
Knowledge Management
Knowledge management is a process that refers to creating new knowledge and using it for the organization when needed. It also facilitates the learning process, increases the amount of knowledge required by members of the organization, and supports the rapid transfer of knowledge within the organization (Zamanifard et al., 2025).
Intellectual Capital
Intellectual capital is a collection of knowledge, information, intellectual property, experience, competence, and organizational learning that can be utilized to create wealth. In fact, intellectual capital encompasses all of an organization’s employees’ knowledge and its capabilities in generating added value, thereby leading to sustainable competitive advantages (Bananuka, 2020).
Wang et al. (2025) investigated the relationships between intellectual capital and strategic decision-making, as well as the moderating-mediating effect between these two variables in small and medium-sized enterprises (SMEs). Their findings indicate that the positive impact of intellectual capital on strategic decision-making through resource integration capability is contingent upon top management’s participative behavior, highlighting the role of resource integration capability and top management’s engagement in intellectual capital. The results also demonstrate that intellectual capital and resource integration capability strengthen positive decision-making relationships. Furthermore, top management’s participative behavior enhances the positive interactive effect of intellectual capital with resource integration capability. Intellectual capital can be utilized by SME management teams to formulate and implement relevant strategic decisions and increase decision-making effectiveness, which are critical stages for success in decision-making processes.
Shafaei et al. (2024) examined the impact of knowledge management on organizational performance, considering business process management as a mediating variable. The research findings revealed that, based on the significance levels of the hypotheses, all three hypotheses were supported. The impact coefficient of knowledge management on business process management was found to be 0.742, the impact of knowledge management on organizational performance was 0.422, and the impact of business process management on organizational performance was 0.652. Therefore, the primary recommendation of this research is to focus on the retention, sharing, and application of knowledge, which is effective in both managing business processes and enhancing the level of organizational performance.
Research Methodology
In terms of its objective, this research is categorized as applicable-developmental; and regarding its execution method, it employs a mixed-methods approach (qualitative-quantitative). The statistical population in the qualitative phase consists of 18 university professors, managers, and experts of Mehr Iran Credit Bank, who were selected by purposive sampling. The statistical population in the quantitative phase includes managers and experts of Mehr Iran Credit Bank in Tehran, with a maximum sample size of 235 individuals determined based on the Morgan and Krejcie table, utilizing simple random sampling. Data collection was conducted through semi-structured interviews in the qualitative phase and via a questionnaire in the quantitative phase.
Research Findings
The analysis of data from the qualitative section utilized the thematic analysis method, while the quantitative section employed SPSS and PLS software. The qualitative analysis revealed that knowledge management within organizations comprises six main dimensions: intelligent knowledge infrastructure, knowledge empowerment, internal value creation, external value creation, comprehensive knowledge support, and knowledge culture. Confirmatory Factor Analysis (CFA) demonstrated that the proposed model possesses adequate construct validity and convergent validity for all dimensions, with the indicators exhibiting high and significant factor loadings. As a practical recommendation, it is advised that organizations enhance flexible knowledge infrastructures and foster a culture of continuous learning. This, in turn, will promote employee knowledge management, enabling the practical utilization of employee experiences and knowledge, thereby ensuring the sustainable development of the organization’s intellectual capital.
Conclusion
The present research was conducted with the aim of examining the enhancement model of knowledge management based on intellectual and specialized capital in the banking industry. These findings align with those of previous studies, including Attar & Bitaraf (2024), Shafaei et al. (2024), Limsangpetch et al. (2022), Areed et al. (2020), Farooq (2018), Migdadi (2021), Nejad Afshar et al. (2025), Bocoya-Maline et al. (2024), and Ardalan et al. (2022). Mohammadzadeh et al. (2023) indicated that the causal drivers of knowledge management include knowledge updating, facilitating infrastructure, and meritocracy; intervening conditions involve interactive and motivational leadership; contextual conditions include organizational culture and employee ranking; strategies encompass healthcare training and receptiveness to criticism; and outcomes include cost reduction, satisfaction, and increased employee productivity. Therefore, it can be concluded that knowledge management is related to numerous factors; and to enhance it, attention must be paid to all the indicators and factors identified in the present study.
Based on the research results, the following suggestion is proposed:
It is recommended that banks establish AI-based digital platforms for the analysis and processing of knowledge data. The utilization of AI systems can assist in identifying existing knowledge patterns and predicting future needs. These platforms can empower organizations to intelligently classify and categorize their data and information, thereby facilitating access to relevant and high-quality information.

Abstract Abstract
This study was conducted with the aim of identifying the dimensions and validating a model of organizational capacity‑building to enhance human resource productivity. The research adopted an applicable purpose and employed a mixed‑method design. In the qualitative phase, semi‑structured interviews were conducted with 19 academic and executive experts in the fields of organizational capacity‑building and human resource productivity, selected through purposive sampling until theoretical saturation was reached. The data obtained from these interviews were analyzed by thematic analysis, leading to the extraction of basic and organizing themes.
In the quantitative phase, the statistical population consisted of all employees of Karaj Municipality in 2024. Stratified random sampling was employed, and data were collected using a researcher‑developed questionnaire designed based on the qualitative findings. Instrument validity was assessed through internal consistency, and reliability was evaluated by Cronbach’s alpha coefficient. Data analysis was performed at both descriptive and inferential levels, including confirmatory factor analysis, by SmartPLS version 3.
The findings revealed that the organizational capacity‑building model for enhancing human resource productivity is grounded in five fundamental dimensions: individual capacity‑building, process capacity‑building, institutional capacity‑building, cultural and organizational learning capacity, and governance capacity. These dimensions encompass components such as the development of job‑related knowledge and skills, enhanced motivation and job satisfaction, innovation and continuous improvement, participatory leadership, technological infrastructure development, sustainable financing, inter‑organizational collaborations, promotion of a learning culture, strengthening ethical values and organizational trust, and alignment with legal frameworks and macro‑level policies. Overall, the results indicate that applying this model can facilitate the improvement of human resource productivity within Karaj Municipality.
Introduction
In the contemporary era, where non‑governmental institutions increasingly play a significant role in addressing social and economic challenges, organizational capacity‑building is considered an inevitable necessity. Given that more than 70% of the world’s population is influenced by non‑governmental institutions, empowering these organizations can contribute to improving the quality of life for millions of people (Molaei & Bandeali, 2020). Furthermore, in circumstances where global crises such as climate change and pandemics require rapid and effective responses, organizations with stronger capacities can emerge as key actors in addressing these challenges (Marzek, 2022).
Organizational capacity‑building refers to strengthening an institution’s capabilities and resources in order to enhance its performance and productivity. In non‑governmental organizations, this process is particularly important in improving human resource productivity. According to management theories, organizational capacity‑building enables institutions to achieve better outcomes by improving internal processes, enhancing employees’ skills, and creating a positive organizational culture. In this regard, organizations can employ effective capacity‑building strategies to improve service quality and increase their impact on society (Wang et al., 2020).
One of the major challenges in organizational capacity‑building is the shortage of financial resources. Organizations often face financial constraints that limit their ability to invest in capacity‑building initiatives and employee skill development. Reports indicate that more than 60% of such organizations are unable to implement their development programs due to insufficient financial resources. This challenge directly affects human resource productivity and leads to a decline in the quality of services provided (Flink & Chen, 2021).
Another challenge is the lack of managerial and leadership skills among organizational managers. Many institutions, due to insufficient training in management, are unable to establish effective organizational structures. This issue may result in difficulties in attracting and retaining high‑quality human resources and consequently affect the overall productivity of the organization (Jafarzadeh, 2024). Therefore, addressing these challenges and identifying effective solutions to overcome them is of great importance.
To address existing issues related to organizational capacity‑building and the improvement of human resource productivity in Karaj Municipality, comprehensive and multidimensional approaches are required. One effective strategy is the development of continuous and systematic training programs for employees to enhance their technical and managerial skills. Additionally, fostering a positive and participatory organizational culture can increase employees’ motivation and work morale. Alongside these measures, improving managerial processes and increasing transparency in decision‑making are necessary to strengthen public trust in governmental institutions. Considering these issues, the present study seeks to answer the following question: What dimensions constitute organizational capacity‑building for improving human resource productivity in the Karaj metropolitan municipality, and how valid are these dimensions?
Theoretical Framework
Capacity is a multidimensional and dynamic concept that can be examined at different levels and entities within an organization, including the individual, system, and organizational levels. Capacity can be considered both as a process and as an outcome. The concept of capacity‑building is also somewhat intangible. Theoretical information in the literature generally defines capacity‑building in broad terms and emphasizes its importance rather than providing a precise definition. However, practical insights obtained through discussions with practitioners more clearly and accurately explain the concept of capacity‑building through experiential understanding, measurement of different capacity elements, and evaluation of the effects of capacity‑building interventions.
Capacity‑building involves creating an enabling environment with appropriate policies, legal frameworks, and institutional development that includes community participation, human resource development, and the strengthening of management systems (Lafond & Watts, 2004). In general, capacity‑building refers to processes or activities that improve the ability of an individual or entity to perform tasks and achieve objectives (Lafond & Watts, 2008). For example, any activity, project, or organizational change that enhances the ability of an organizational system to produce positive outcomes can be considered a capacity‑building variable.
Capacity‑building is inherently multidimensional. Understanding it requires examining its elements, strategies, dimensions, and intervention mechanisms. Scott and Wolfe (2014) define capacity‑building along a spectrum ranging from broader to more specific interpretations. In a general sense, capacity‑building refers to any activity that enhances the ability of partners to perform their work or assist others in improving the lives of disadvantaged populations. This includes equipping organizational staff with the training necessary to effectively implement their programs (Mohammadi, 2022).
Capacity‑building also encompasses multiple levels, including the community and environmental level (empowering individuals), the organizational level (empowering non‑governmental and civil society organizations), and the network level (empowering networks that function as information and collaboration systems).
Research Methodology
This study is applicable in nature, and employs a descriptive–exploratory design, utilizing a sequential mixed‑methods approach (qualitative followed by quantitative). In the qualitative phase, the phenomenological exploration of experts’ perspectives and experiences was used to identify the dimensions and components of the research subject, ultimately leading to the development of a conceptual model. Participants in this phase consisted of 19 theoretical and practical experts selected through purposive non‑random sampling until theoretical saturation was achieved (interviews 20 and 21 yielded no new insights). The experts included university faculty members and senior officials of Karaj Municipality. Semi‑structured interviews, each lasting up to 40 minutes, were conducted to examine the dimensions, components, indicators, and the current state of capacity‑building for human resource productivity.
In the quantitative phase, all employees of Karaj Municipality involved in capacity‑building and human resource functions were targeted through stratified random sampling. Structural equation modeling was employed to determine the sample size. The data collection instrument was a 65‑item Likert‑scale questionnaire (ranging from 1 = very low importance to 5 = very high importance), developed based on the qualitative findings and divided into demographic and main sections. Validity and reliability in the qualitative phase were evaluated by Guba and Lincoln’s (1982) criteria—credibility, transferability, dependability, and confirmability—through participant feedback, auditing, and diverse expert viewpoints. In the quantitative phase, content validity was confirmed by specialists; discriminant validity was assessed by the Fornell‑Larcker criterion; convergent validity was evaluated through AVE values (> 0.5); and reliability was examined by Cronbach’s alpha.
Qualitative data analysis employed inductive content analysis with three‑stage coding (open, axial, and selective). Quantitative analysis involved descriptive and inferential statistics through SPSS, and structural equation modeling was conducted with SmartPLS at a 5% significance level.
Research Findings
The qualitative interviews reached theoretical saturation with 15 experts (7 academics and 8 managers). As a result, five main dimensions and 18 components of the organizational capacity‑building model were identified, forming a conceptual network framework.
The quantitative sample indicated that 67% of the respondents were male, 78.8% held a master’s degree, and 87.7% had more than 20 years of work experience. Discriminant validity was confirmed through the Fornell–Larcker matrix. Convergent validity was also verified, with AVE values greater than 0.5 and composite reliability (CR) exceeding AVE. Model fit indices (e.g., SRMR < 0.08 and NFI > 0.90) demonstrated an acceptable model fit.
The results of the structural equation modeling confirmed all hypothesized paths as statistically significant. The individual capacity structure—including knowledge development, motivation/job satisfaction, individual evaluation, and productivity—showed strong factor loadings, with the highest loading (0.816) related to motivation. The process capacity structure, consisting of innovation, transformational/participatory leadership, process improvement, and technological infrastructure, demonstrated factor loadings above 0.70, with leadership showing the highest loading (0.821). The institutional capacity dimension, which included formal structures, human resource policies, and inter‑organizational collaboration, had factor loadings ranging from 0.733 to 0.859, with collaboration showing the highest value. The cultural and organizational learning capacity dimension—comprising a culture of continuous learning, ethical values and trust, and productivity through culture and learning—showed factor loadings in the range of approximately 0.63. Finally, the governance capacity dimension—including legal and policy compliance, stakeholder support, and monitoring, evaluation, and learning—demonstrated factor loadings around 0.718 across its components.
Conclusion
This study confirms a five‑dimensional model of organizational capacity‑building for enhancing human resource productivity in Karaj Municipality: individual, process, institutional, cultural/organizational, and governance capacities. Together with its associated components, each dimension contributes to a comprehensive framework supported by significant structural paths and strong model fit indicators.
The findings are consistent with previous research emphasizing the importance of training (Greer et al., 2023), motivation (Jahanbaz et al., 2023), leadership (Apat & Mohapatra, 2025), human resource policies (Djazilan & Arifin, 2022), and cultural learning (Rommerskirch‑Manietta et al.). These results highlight the integrated role of multiple organizational capacities in strengthening human resource productivity within public sector institutions.
However, the study has several limitations, including the restriction of the sample to Karaj Municipality, the potential for respondent bias, and the rapidly evolving technological and managerial context. Future research could expand the model to other municipalities, employ comparative or longitudinal research designs, utilize social network analysis, and conduct cross‑cultural comparisons with other countries.
From a practical perspective, policymakers and managers should prioritize employee training programs, empowerment initiatives, technological infrastructure development, clear policy frameworks, and the promotion of a capacity‑building organizational culture in order to improve productivity and enhance the quality of public services.

Abstract Over the past two decades, Non-Governmental Organizations (NGOs) have become pivotal pillars of welfare systems in many countries; however, there remains a lack of a clear theoretical and empirical formulation of the direct and indirect functions of these institutions. The present study was conducted with the aim of providing a comprehensive model of NGO roles within Iran’s social welfare and social security system. Adopting a qualitative approach within an interpretivist paradigm, data were collected through semi-structured interviews with 12 experts, including NGO managers, legal advisors, and faculty members. Data analysis was carried out using the thematic analysis method based on the Attride-Stirling (2001) model, which ultimately led to the extraction of 29 open codes and 5 basic themes. The findings indicate that NGO roles can be categorized into two levels: first, direct service-providing and empowering roles, including health and welfare service delivery, bridging service gaps, and empowering vulnerable groups; and second, indirect advocacy and supervisory roles, such as monitoring health rights, fostering transparency, needs assessment, policy innovation, and inter-organizational networking. The study concludes that NGOs in Iran have been largely reduced to direct, executive roles and, due to significant structural barriers—such as the lack of supportive legislation, financial dependence on the government, and the absence of a unified database—have failed to perform indirect monitoring and policy-making functions. Ultimately, achieving an efficient hybrid welfare system requires a fundamental redefinition of the relationship between the government and NGOs, alongside the establishment of supportive institutional, legal, and financial frameworks.