Intelligent Management of Human Capital

Background and Objective: One of the factors for the success of the cultural-educational centers of the Islamic Republic of Iran Army is the competence of the instructors. The aim of this research is to design a competency model for the instructors of the cultural-educational centers of the Islamic Republic of Iran Army Ground Forces. Methodology: The type of the present research is applied, the research method is descriptive and its approach is mixed. The statistical population in the qualitative section included 14 experts who were selected purposefully and judgmentally, and in the quantitative section, 95 people, including a group of experts and experts familiar with the research topic in the cultural centers of Nezaja, professors, trainers, and graduates of higher education who were selected randomly. The method and tool of data collection was fieldwork and semi-structured interviews. Descriptive methods were used to analyze the data. Findings: The statistical population in the qualitative section included 14 experts who were selected purposefully and judgmentally, and in the quantitative section, 95 people, including a group of experts and experts familiar with the research topic in the cultural centers of Nezaja, professors, trainers, and graduates of higher education who were selected randomly. The method and tool of data collection was fieldwork and semi-structured interviews. Descriptive methods were used to analyze the data. Conclusion: The research findings indicate that the identification of educators' competencies includes knowledge competencies (including 18 sub-competencies), skills competencies (including 20 sub-competencies), attitudes competencies (including 17 sub-competencies), and personality competencies (including 24 sub-competencies). It was suggested that the competency model identified in this study be sent to the Nizaja Assessment Centers and the Paydan Center so that, while selecting instructors based on the specified competencies, they can be evaluated and measured accordingly. It should also be sent to the Deputy Ministers of Education and Training as well as the Nizaja Inspectorate to hold empowerment courses in this regard.

Background and Objective: The present study was conducted with the aim of Predicting the mental health of elementary school principals using artificial intelligence. Methodology: The present study design was descriptive and correlational and predictive. The statistical population of the study included all primary school principals who were working in 1403-04. Accordingly, 128 people were selected from the aforementioned population by stratified random sampling and the necessary tools were administered to them. The sampling method was as follows: from among the schools, the primary school was selected and from among the districts, District 2 was selected. Due to the incompleteness of the questionnaires related to 4 of the respondents, the final analysis was conducted on 124 of them. To collect the information required for the study, the Keyes Mental Health Questionnaire (2002) and the Faraji et al. Management Artificial Intelligence Questionnaire (1402) were used. The collected data were analyzed in the SPSS-26 software using the multiple regression method. Findings: The results showed that the significance level for the variables AI management, AI-based decision-making, AI infrastructure, AI skills, and AI orientation is less than 0.05, so with 95% confidence, the existence of these five independent variables is significant for predicting mental health (β ≠0). Conclusion: This study deals with predicting the mental health of managers who use AI and shows that with the correct and ethical use of AI, healthier and more productive work environments can be created in which employees feel more satisfied and relaxed.

Background and Objective: The necessity of managerial competence is emphasized in various sections of the Seventh Development Plan of the country. The aim of the present study is to provide a competency model for managers based on the Seventh Development Plan of the country. Methodology: This research is applied in terms of purpose and mixed (qualitative-quantitative) in terms of data type. The participants in the qualitative section included 15 faculty members from universities and municipal officials, while the quantitative section included all managers of the Tehran municipality, totaling 115 individuals. Data collection tools in the qualitative section were semi-structured interviews, and in the quantitative section, a researcher-made questionnaire was used. Data analysis methods included thematic analysis for qualitative data and confirmatory factor analysis along with structural equation modeling for quantitative data. Findings: The findings of the study indicated that the competency model for managers based on the Seventh Development Plan of the country, prioritized as follows: specialized knowledge with a weight of 0.458; attitude with a weight of 0.327; and skills with a weight of 0.278. Conclusion: The findings of this research can lead to a more precise understanding of the factors related to managerial competence based on the Seventh Development Plan of the country within organizations. These outcomes are highly significant for stakeholders, including managers, policymakers, and investors, as they can lead to optimal decision-making and improved management and supervisory strategies.

Background and Objective: The present study was conducted with the aim of investigating the effective drivers in the path of teaching-learning to human resource well-being among Tehran Municipality employees. Methodology: The research method is descriptive, the objective is applied and the implementation is survey. The data collection tool is a modified standard questionnaire. The statistical population of the study included one hundred employees of the Deputy for Planning and Human Capital Development in one of the 22 districts of Tehran Municipality. 112 were selected as a sample by simple random method and based on the Cochran formula. The validity of the questionnaire was confirmed using content and construct validity, and its reliability was confirmed using Cronbach's alpha coefficient. SPSS version 25 software was used to analyze the data, and the smart-PLS method was used to test the quality of the structural model. Findings: While reviewing the literature and research background, after identifying the operational concepts of learning and well-being from the perspective of employees in the municipality above and examining the results of the completed questionnaires in the organization under study, the effectiveness of training, learning, and employee well-being was revealed as a complex and multifaceted process, because even though the impact of training on learning and, consequently, employee well-being was conceivable, the results indicated tangible and intangible effects of intervening and mediating factors in this area; so that the research hypotheses were not confirmed. Conclusion: The results showed that in the statistical population under study, teaching-learning processes do not guarantee well-being components, including psychological well-being, occupational well-being, and individual well-being. Therefore, it can be acknowledged that in organizational empowerment training, in addition to psychological, personality, family issues, and lived experiences of employees, factors such as level of education, level of expertise, skills, job description, responsibility, and organizational position, content, time, and place of training should be personalized and considered in the form of comprehensive training programs. Finally, appropriate scientific and practical suggestions were also presented.

Background and Objective: This research aims to identify, analyze, and synthesize strategies for addressing ethical challenges in the application of artificial intelligence in human resource management. Methodology: A meta-synthesis method was employed to conduct a systematic study on credible international scientific sources. From an initial pool of 113 articles, 27 high-quality articles were ultimately selected and deeply analyzed. Thematic analysis was conducted to identify main themes, and Shannon entropy technique was applied to rank and determine the relative importance of themes. Findings: Three main themes were identified: "Systematic Digital Ethics Infrastructure" (including AI ethical governance mechanisms, technical infrastructure for algorithmic quality assurance, and integrated systems for sensitive data protection), "Human-Centered Capability Development Mechanisms" (including approaches to empowerment and digital capability development, and human-centered participatory decision-making framework), and "Mechanisms for Ensuring Justice and Systemic Accountability" (including mechanisms for promoting algorithmic justice and inclusivity, and systematic transparency and accountability mechanisms). The calculated total entropy (0.9156) confirmed a balanced distribution and appropriate dispersion of themes. Conclusion: The research presents a comprehensive framework for ethical challenges of artificial intelligence in human resource management, emphasizing human-centered participatory decision-making framework, technical infrastructure for algorithmic quality assurance, and mechanisms for promoting algorithmic justice as the most important solutions. This framework, focusing on intelligent human-machine collaboration, enables ethical utilization of artificial intelligence in human resources.

Background and Objective: Student admission in universities is a complex process that significantly impacts educational quality and scientific development. Traditional student selection methods are often inefficient and require enhancement through modern technologies. This study aims to optimize the student admission process at Imam Hossein Comprehensive University by proposing a competency-based recommender model. Methodology: This applied and descriptive-analytical research employs a combination of data mining techniques, natural language processing, and similarity measurement algorithms. First, student competency criteria were extracted, and then a recommender system was designed and evaluated using language models and natural language processing techniques. Findings: The proposed recommender model analyzes applicants' individual characteristics and provides personalized suggestions, thereby increasing the accuracy and efficiency of the admission process and enhancing decision-making in selecting qualified students. Conclusion: The proposed model can serve as an effective tool for university administrators to optimize the student admission process. In addition to improving the precision of candidate selection, this approach contributes to the academic and research development of the university.

Background and Objective: The purpose of this article is to identify human resource risks in Sepah Bank. Research Method: This research is a mixed method and the theme analysis method was used to identify human resource risks. For validation, a quantitative research method was used and based on an applied descriptive purpose. The statistical population is Sepah Bank. In order to receive and collect information, the opinions of managers and experts in the fields related to human resources were used. In this regard, three steps were taken to identify human resource risks. In the first step, the theoretical literature of the research was studied. In the second step, non-probability judgmental and snowball sampling methods were used and experts were interviewed until saturation. In this step, theme analysis was performed using a qualitative method. After identifying the risks, the validity of the identified structures was confirmed. Data were collected from a questionnaire and for data analysis, the structural equation model measurement section and Lisrel SPSS and QSR Nurive software were used. Findings: In the qualitative part, using the theme analysis method, dimensions, components, and indicators of human resources risk were identified. In the quantitative part, using confirmatory factor analysis, while examining and confirming the indicators and dimensions of the human resources risk model, their priority in the West Azerbaijan Tax Affairs Organization was examined so that the organization's managers, while identifying the very important human resources risks in their organization, could plan to control them. The presented model consists of 5 dimensions and 14 components. Conclusion: In this research, after identifying the dimensions, components, and indicators of human resources risk, a model was designed; this assessment helps the managers of the organizations to adjust the model indicators in a way that leads to the control of existing risks by identifying and ranking the status of the aforementioned risks.

Background and Objective: This study aimed to rank the factors influencing the performance of artificial intelligence in improving in-service teacher training by utilizing the SECA decision-making method in a qualitative and applied manner. Methodology: The study is considered applied in terms of its objective and descriptive-survey in nature. The statistical population included all faculty members of Nasibeh Campus, Farhangian University in Tehran. The sampling method was purposive and adhered to the principle of theoretical saturation, encompassing 20 experts. Data collection was conducted through interviews with experts. After extracting expert-defined criteria influencing AI performance in in-service teacher training, a decision matrix was developed based on the criteria and alternatives. Finally, the SEC method was employed to determine the ranking of influencing factors, and the LINGO software was used to solve the nonlinear equations. Findings: Educational data analysis, personalized training, and virtual interaction and collaboration were identified as the most significant factors influencing AI performance in improving in-service teacher training. Conclusion: All the criteria identified by the experts were considered positive, indicating that an increase in any criterion would lead to an improvement in the performance of AI in enhancing in-service teacher training.

Background and Objective: This study aimed to Examining the dynamics of the role of artificial intelligence in marketing strategies with a focus on ethical and cultural issues such as privacy of data, algorithmic fanaticism, etc. and its effect on consumer behavior. Methodology: This applied research was conducted using a qualitative approach. In this research, the concepts of artificial intelligence, marketing strategy and consumer behavior, and the relationship between artificial intelligence and ethics, culture and consumer behavior were investigated through the dynamic system method, and the connections. Findings: Artificial intelligence, marketing strategies, consumer behavior, cultural challenges and issues, and ethical issues are the five main concepts of this research, which were identified by extracting and examining the variables that influence each relationship and their implications. AI-powered personalization involves the collection and analysis of customer data, so the lack of legal and compliance frameworks in this area raises privacy concerns, which negatively impacts consumer behavior. Cultural aspects include customs, traditions and social norms, which in turn affect the use of AI in this society and among consumers. Conclusion:The use of artificial intelligence introduces inconsistencies into the culture of society and creates ethical concerns such as data protection, skills shortages and implementation difficulties, problems arising from the lack of legal frameworks and the digital divide. To reduce the negative effects of AI, solutions such as creating ethical frameworks for AI development, applied laws, the creation of regulatory and development institutions with respect for cultures have been proposed.

Background and bjective: The purpose of this article was to design and present a model for the selection and recruitment of elite university graduates in the Islamic Republic of Iran Army. Methodology: In terms of its practical purpose, this research is an exploratory research with a mixed approach (qualitative-quantitative). The statistical population is the number of 320 elites (elite students and elite graduates) who have received at least one of the Foundation's awards (including Shahid Shahriari, Shahid Kazemi, Shahid Chamran and Shahid Sayad Shirazi) during 3 received last year, are The size of the sample population in the interview stage is 13 people from the deputies (education and training, plan and budget, human resources, science, research and technology, AMA and Pesh and AJA industrial research - self-sufficiency research and jihad of the four forces - Education and research of Dafos Aja), the managers of the elite foundation of the armed forces and Aja, and for the questionnaire, the number of 175 people was calculated using Cochran's formula and sampling was done by stratified random method. Interviews, questionnaires and library studies were used as data collection tools. The validity of the questionnaire was confirmed using the content validity ratio (CVR). Also, its reliability was obtained using Cronbach's alpha coefficient of 842%. Findings: In this research, using the exploratory approach and using data base theorizing, the model of selection and recruitment of elite graduates of the country's universities in Aja was presented based on three stages of open, central and selective coding. In the quantitative part of the research, the evaluation and validation of the causal relationships expressed in each of the judgment cases was done with the methodology of structural equation modeling with the partial least squares approach and using the Smart-PLS software, and each of the explained hypotheses of the case was measured. Originality: The results obtained in the validation of the research hypotheses, from the total of 15 tested hypotheses, all the research hypotheses have been confirmed, except for the hypotheses number 5, 8, 9, 10, and 14, and it has had a significant effect on the selection and recruitment of elite graduates of the country's universities in Aja.

Background and Objectives: In recent decades, Artificial Intelligence (AI) has emerged as a pivotal technology in enhancing Human Resource Management (HRM) processes. However, many organizations have yet to fully exploit this technology's potential, which may be due to low levels of organizational and informational maturity. This study examines the impact of organizational, informational, and human resource maturity levels on managers' and experts' perceptions of AI efficiency in HRM. The innovation of this research lies in combining three maturity models organizational, informational, and human resource and examining their combined effects on AI productivity in HRM. Methodology: To address this gap, this research employs a descriptive-survey approach, collecting data from 120 HR managers and experts in knowledge-based companies at the Science and Technology Park. The sample size was determined using the Morgan table, and the sampling method was judgmental. Data analysis methods include multiple regression and Structural Equation Modeling (SEM). Findings: The results indicate that human resource maturity, with an impact coefficient of 0.361, has the most significant effect on the perception of AI efficiency, while informational and organizational maturity follow with coefficients of 0.296 and 0.240, respectively. Conclusion: This study recommends that organizations should pay special attention to the simultaneous development of organizational, informational, and human resource maturity to effectively leverage AI.

Background and Objective: The relationship between artificial intelligence and the safety of employees in their workplace is one of the vital issues in organizations. The present study aimed to investigate the effects of artificial intelligence on the health and safety of employees in the workplace. Methodology: The present study was conducted by reviewing selected research literature and interviewing experts. Among 30 included articles, 22 review articles, 6 original articles, 1 cohort article, and 1 case article were found. After reviewing the articles related to the subject, an interview was conducted with 15 experts and specialists in the field of marketing and business management, artificial intelligence, and the Internet of Things in the field section. FindingsThe results showed that applying artificial intelligence in workplaces enhances accuracy, speed, process optimization, time savings, error reduction, employee health, safety systems, high-risk identification, pandemic response, stress reduction, job confidence, and workplace security measures. Conclusion: The undeniable role of AI in employee health and safety is highlighted, along with technologies like IoT, big data, VR, and biosensors for the Fourth Industrial Revolution. Future research could explore AI models' effectiveness in enhancing health and safety.

Background and Objective: Equipping and preparing human resources as the most critical organizational pillar to cope with rapid changes is of great importance, and its knowledge-based development ensures organizational stability. The present study aims to identify knowledge-based human resource development strategies for sustainable management. Method: This study is applied in terms of purpose and qualitative in terms of approach, employing the thematic analysis method. Participants included human resource managers, professors, and experts in this field, with a purposive sample of 15 individuals. In this research, "trustworthiness" was evaluated as a criterion for replacing validity and reliability. Findings: Through semi-structured face-to-face interviews and employing the thematic analysis strategy, 234 basic themes, 15 organizing themes, and 5 global themes were identified. Conclusion: The identified organizing themes include "Information and Communication Technology," "Internal Structure," "Training," "Knowledge-Based Recruitment," "Knowledge-Oriented Performance Evaluation," "Job Design," "Personality," "Competency and Skills," "Motivation," "Sustainable Development Thinking," "Organizational Culture," "Social Value System," "Rules and Procedures," "Environmental," and "Economic" factors. Ultimately, a model was proposed consisting of five global themes: Knowledge-Based Human Resource Systems, Knowledge-Oriented Information and Bureaucratic Structure, Human Capital, Knowledge-Based Thinking and Culture, and Sustainability and Extra-Organizational Factors.

Background and Objective: This study aims to design a framework for empowering students at Imam Ali Military University (AS), focusing on mentoring. By examining the university's structure and the interactions between experienced faculty members and novice students, the research seeks to develop a locally adapted model that bridges these domains to enhance student empowerment. Methodology: This developmental, applied research employs a qualitative approach. The study population comprises academic experts and senior commanders from Imam Ali Military University (AS). Using purposive snowball sampling, 14 individuals were selected for semi-structured interviews. Data analysis utilized Strauss and Corbin's three-stage coding process within grounded theory methodology. Findings: Data analysis revealed six conceptual categories forming the research model: Causal factors: educational aspects, students' academic rights, quality of educational responsiveness, significance of practical training, and confidence in education. Contextual factors: educational dynamics, organizational excellence, technical knowledge, motivational systems, and managerial transformation. Intervening factors: macro and micro conditions. Strategies: categorized into short-term, medium-term, and long-term. Outcomes: educational, social, expertise-related, and individual. Conclusion: The results suggest that internal excellence and managerial transformation significantly contribute to student empowerment through mentoring. These factors facilitate individual and group development and create an appropriate managerial environment conducive to student growth.

Objective: This research aims to evaluate and analyze the strategic position for improving the productivity of the I.R.I. Army Staff cadre. Method: This applied research employs a mixed-methods descriptive approach. The study's statistical population includes 36 individuals, comprising department heads, middle managers, and human resources personnel within the I.R.I. Army. Due to the alignment of the sample size with the population, a census approach was used. Data collection was conducted using a researcher-developed questionnaire, which assessed internal and external key factors through statistical analysis to determine the strategic position. Findings: The study identified 62 internal and 48 external factors affecting productivity within the I.R.I. Army Staff cadre. These factors were gathered through theoretical research and questionnaire responses from the statistical population. Results: Analysis of the collected data involved calculating status scores for each factor, determining their importance, and computing weighted scores for internal and external factors. The strategic position was plotted on a relevant chart, and the strategic rotation angle was calculated. The findings indicate a gap between the current and optimal productivity levels, placing the organization in a slightly aggressive strategic position. These revisions make the abstract clearer and more concise while maintaining an academic tone. The restructured sentences improve readability, and additional details enhance understanding of the methodology and findings.

Purpose: This study aimed to measure the cognitive readiness of primary education teachers in the 20th district of Tehran, focusing on individual cognitive preparation, workgroup preparation, learning readiness, and social-emotional readiness. Method: This quantitative, descriptive-survey research employed a standard questionnaire to collect data. The questionnaire's reliability was established using Cronbach's alpha (α = 0.89), and content validity was assessed. Descriptive statistics, analysis of variance, single-sample t-tests, and Friedman's test were used to analyze the data and test the research hypotheses. Findings: Significant differences were found in all components of cognitive readiness: individual cognitive preparation (t = 16.12, p < 0.01), workgroup preparation (t = 12.46, p < 0.01), learning readiness (t = 0.4, p < 0.01), and social-emotional readiness (t = 3.99, p < 0.01). Conclusion: The results indicate that individual cognitive preparation is a key indicator of teachers' abilities. Enhanced personal knowledge and awareness in the educational environment may enable teachers to better engage with new ideas and gain a more comprehensive understanding of their surroundings. These findings suggest the importance of focusing on cognitive readiness in teacher development programs. Future research could explore the long-term impact of cognitive readiness on teaching effectiveness and student outcomes.

Objective: This study aimed to develop scenarios addressing the risks posed by the broad application of artificial intelligence (AI) in human capital management processes. Method: This developmental-applied research utilized a descriptive-causal method. The statistical population included managers and professors from a military organization with sufficient expertise in intelligent human capital management. A purposeful sample of 30 individuals was selected for data collection via a questionnaire. The questionnaire's validity was confirmed through content validation, and its reliability was verified using the test-retest method, yielding a correlation coefficient of 0.76. Data analysis was conducted using system dynamics and Vensim software. Findings: Four scenarios were developed and simulated to incorporate all relevant factors. The results indicated that increased AI-related risks in human capital management processes—such as recruitment, development, and performance management—significantly reduce individual productivity. This decline leads to higher dissatisfaction and employee turnover, particularly evident in the fourth scenario where AI risks permeate all management processes. Conclusion: To mitigate the pessimistic scenarios identified in this study, several actions are recommended. These include empowering human capital through soft skills development, enhancing interpersonal interactions, addressing ethical, legal, and security concerns in AI applications, and leveraging AI as an intelligent assistant in human capital management processes. These revisions aim to improve clarity by refining sentence structure and ensuring consistent capitalization while maintaining an academic tone. The restructured sentences enhance readability, and additional details provide a clearer understanding of the methodology and findings.

Objective: Data mining plays a crucial role in optimizing human resource management by uncovering valuable patterns from large datasets. This study aims to predict human resource requirements using a novel approach that combines neural networks with the Whale Optimization Algorithm (WOA) and Simulated Annealing (SA). Method: This applied, quantitative research analyzed a dataset of 30,000 records. The proposed method integrates neural networks' pattern recognition capabilities with WOA and SA's optimization strengths to enhance the accuracy and efficiency of human resource needs prediction. The hybrid model was designed to address limitations in traditional forecasting methods. Findings: The hybrid model demonstrated significant improvements over traditional methods, achieving a 15% increase in prediction accuracy and a 20% reduction in computational time. It also showed enhanced robustness when tested with varied input parameters, maintaining consistent performance across different scenarios. Conclusion: This study underscores the potential of advanced data mining techniques in enhancing human resource planning. The proposed method offers organizations a more reliable tool for forecasting staffing needs and making strategic decisions. Future research should explore its applicability across various industries and investigate its potential for real-time workforce optimization. Additionally, integrating this model with existing HR systems could provide a comprehensive solution for dynamic workforce management.