Intelligent Management of Human Capital

Objective: This study investigates the causal mechanisms linking future-oriented career behaviors to career capital, specifically examining the mediating role of talent management within the Department of Environmental Protection of Tehran Province. Methodology: Utilizing an applied, descriptive-survey design with an explanatory-causal approach, data were gathered from 242 employees through stratified random sampling. The research employed standardized instruments to measure future-oriented career behaviors, talent management, and career capital. Psychometric robustness was established via Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE). Data were analyzed using structural equation modeling (SEM) through SPSS and SmartPLS. Findings: The results indicate that future-oriented career behaviors exert a significant positive direct effect on career capital. Moreover, talent management serves as a significant mediator in the relationship between future-oriented career behaviors and career capital. These findings suggest that individual proactive behaviors are effectively converted into career capital when facilitated by structured talent management processes. Conclusion: Talent management functions as a strategic nexus that translates proactive career behaviors into tangible career-related outcomes. To foster career capital and sustainable organizational growth, institutions should integrate effective talent management practices into their human resource development frameworks.

Background and Objective: Artificial intelligence-based work measurement and time-tracking systems hold significant potential for optimizing nurse shift scheduling and more efficiently managing their workload. Methodology: This research aimed to investigate the experiences of nurses and managers regarding the implementation and utilization of such a system within a hospital setting, seeking to identify its challenges, opportunities, and impacts on operational and managerial processes. A qualitative approach using semi-structured interviews was employed. Participants included nurses and managers (with expertise in human resources management, operations, planning, and AI development) at various levels within a hospital. Data collected from the interviews were analyzed using the thematic analysis method and Maxqda software to identify the main and overarching themes related to users’ experiences with the AI system. Findings: The data analysis revealed eight primary overarching themes: 1) Enhancing organizational efficiency through intelligent work measurement, 2) Increasing perceived fairness and job satisfaction, 3) Organizational agility and dynamic decision-making, 4) Gradual trust in AI systems, 5) Financial optimization and cost-effectiveness, 6) Managerial agility and empowerment with AI, 7) Balancing machine logic with human considerations, and 8) Intelligent shift optimization with a human-centered approach. Conclusion: Therefore, although AI systems for work measurement and time measurement of nurses increase efficiency, they also face challenges related to adoption, training, and maintaining a balance between technical and human aspects; thus, their success requires a comprehensive approach and active staff participation.

Background and Objective: With the advent of the era of digital transformation and the penetration of artificial intelligence in management structures, organizations need new models that, in addition to technological efficiency, are aligned with value and indigenous principles. This article aims to design a model of artificial intelligence applications in the country's state organization for registering documents and real estate. Methodology: The present study is developmental-applied in terms of its purpose and mixed (qualitative-quantitative) in terms of the nature of the data. In the qualitative step, using content analysis and semi-structured interviews with 17 experts Based on the rule of "theoretical saturation", seven key areas of artificial intelligence capability in the organization were identified. In the quantitative step, using a researcher-made questionnaire and structural equation modeling (PLS-SEM), prioritization and validation of the areas were carried out. Findings: The findings show that the most important applications of artificial intelligence in the Iranian Organization for Registration of Deeds and Properties are, respectively: (1) a system for detecting and preventing document forgery, (2) an intelligent signature and fingerprint matching engine, (3) an automatic information extraction system from image documents using natural language processing and advanced optical character recognition (OCR), (4) a transactions risk rating system, (5) an intelligent chatbot for registration services, (6) a staff violation monitoring and warning system, and (7) a tool for predicting services workload and optimal resource allocation. Conclusion: Finally, the proposed technical architecture and implementation challenges (including privacy, data integrity, and user acceptance) are presented. This research shows that the gradual implementing of AI-based systems can increase accuracy by 94%, reduce document processing time by 76%, and reduce human errors by 89%.

Background and Objective: In the attention economy era, employees' attention has become the scarcest organizational capital, and digital fatigue resulting from excessive use of technology threatens their cognitive health and productivity. This study aims to design a human resource management model in the attention economy era, focusing on strategies to sustain focus and reduce digital fatigue. Methodology: This is an applied qualitative study using grounded theory methodology. Data were collected through semi-structured interviews with 20 experts selected through purposive sampling. Findings: The findings revealed a comprehensive model including causal conditions (technological pressure, always-on culture, poor process design, and lack of digital literacy), contextual conditions (centralized structure, cultural norms, and rigid regulations), and intervening conditions (managerial support, technological infrastructure, and reward system). The core phenomenon, "attention erosion and digital mental fatigue," manifested through six components, and multi-level strategies were proposed including macro-level (protective policies, job redesign), meso-level (aware leadership development, communication re-engineering), and micro-level (individual empowerment, focused environment design). Consequences emerged at individual level (burnout, work-life conflict), organizational level (elite exit, operational dependency), and institutional level (social capital erosion, injustice normalization). Conclusion: The proposed model demonstrates that effective employee attention management requires transformation in governance policies, cognitive load-based job redesign, and development of attention-conscious leadership.

Background and Objective: The main purpose of the current research is to identify the impact of algorithmic management for human resource management in the gig economy with a meta-synthesis approach. Methodology: This study follows a developmental, qualitative, and documentary design. Using an analytical-descriptive approach, the research implemented Sandelowski and Barroso’s seven-stage meta-synthesis for systematic data collection and analysis. In order to identify the consequences, researchers systematically search for research related to these consequences in 6 scientific databases: Science Direct, Emerald, Wiley, Sage, Taylor & Francis, and the Scopus citation database between 2010 and 2024. In the initial investigations, 120 sources were found, and after screening and validation and using the CASP tool, finally 24 articles were analyzed. Findings: The findings of the present study show that a total of 2 main consequences of algorithmic management in the field of human resource management in the gig economy have been identified, including negative consequences and positive consequences, and 36 sub-consequences. Conclusion: The findings show that algorithmic management in the field of human resource management has both positive and negative aspects. On the positive side, organizations have improved and enhanced some of these activities by using algorithmic processes to automate human resource management activities in relation to the organization, employees, and customers. On the negative side, the data-driven approach dehumanizes work, and algorithms take over roles that have traditionally been the preserve of human resource professionals.

Background and Objective: In the knowledge-based economy, organizations rely on knowledge to achieve competitive advantage. Big data analytics capability (BDAC) provides valuable insights from complex datasets, while innovation ambidexterity supports the simultaneous pursuit of exploratory and exploitative innovations. This study aims to investigate the impact of BDAC on innovation ambidexterity and competitive advantage in knowledge-based firms, with a particular focus on the mediating role of innovation ambidexterity. Methodology: This study employed a quantitative, descriptive-correlational research design. Data were collected through a questionnaire administered to a sample of 360 knowledge-based firms located in Tehran Province. A systematic random sampling technique was used based on a sampling frame and calculated sampling interval. Structural equation modeling was applied to analyze the data and test the proposed hypotheses. Findings: The results revealed that BDAC has a significant positive effect on both innovation ambidexterity and competitive advantage in knowledge-based firms. Innovation ambidexterity also exerts a significant positive influence on competitive advantage. Furthermore, innovation ambidexterity partially mediates the relationship between BDAC and competitive advantage. Conclusion: BDAC is a key driver of competitive advantage in knowledge-based firms, exerting both direct and indirect effects through the enhancement of innovation ambidexterity. The partial mediating role of innovation ambidexterity suggests that competitive advantage stems from the combined effect of data-driven analytical potential and the innovative exploitation of organizational knowledge. Therefore, managers should integrate investments in big data analytics infrastructures with the simultaneous development of exploratory and exploitative innovations strategies to identify new opportunities, improve products, and strengthen competitive advantage.

Background and Objective: The purpose of this study is to conduct a comparative study of various online teaching methods and their impact on the learning of students at Imam Ali (AS) Military University. Methodology: The research method was descriptive and the study design was correlational. The statistical population of the research included 181 professors of Imam Ali (AS) Military University in 1404. The sample size based on Morgan's table was 118 professors of Imam Ali (AS) Military University. In the present study, a multi-stage cluster random sampling method was used, and the research collection tool was a researcher-made questionnaire. The correlation coefficient method was used to analyze the data of the research hypotheses and the results were analyzed using SPSS 20 software. Findings: The results of the research indicate that according to the results, the variable of online teaching without a teacher with beta (BETA=0.75), the variable of online teaching with the presence of a teacher and students (BETA=0.78), the variable of combined face-to-face and non-face-to-face online teaching (BETA=0.42), and the variable of online teaching without students (BETA=0.80) predict the variable of student learning. Conclusion: According to the results, the variable of online teaching with the presence of the professor and students predicts the student learning variable with the highest degree (BETA=0.58), combined online teaching with face-to-face and non-face-to-face teaching with the lowest degree (BETA=0.28), and online teaching without students (BETA=0.45).

Background and Objective: The teacher ranking system, through creating job incentives and enhancing professional competencies, leads to improved performance and increased educational quality. However, designing a coherent mechanism for human resource governance in this arena is essential. The aim of this study was to design a human resource governance model with an emphasis on the teacher ranking system in education. Methodology: This applied research with a qualitative approach was based on the systematic grounded theory of Strauss and Corbin. Data were collected through purposive interviews with 11 experts and specialists and analyzed using three-stage open, axial, and selective coding in MAXQDA software. Findings: Data analysis showed that causal conditions include external pressures, systemic deficiencies, and managerial inefficiency. Contextual conditions encompass macro infrastructures, prevailing laws, and the dominant culture, while intervening conditions consist of financial and bureaucratic constraints, influential groups, and cultural challenges. The identified strategies are: smart and sustainable regulation, establishment of a multidimensional evaluation system, a participation and accountability structure, international collaboration, regulation of transfer and placement, and strategic regulation of livelihood. The consequences of the model include the strengthening of teachers' social capital, transformation of educational quality, and equitable organization of human resources in education. Conclusion: The resulting model provides a systematic, coherent framework for understanding and improving human resource governance in the educational system. This model can serve as a basis for precise policymaking in the effective implementation of the teacher ranking system and, consequently, the sustainable enhancement of educational quality in the country.

Background and Objective: This study aims to identify the dimensions and components of developing critical thinking among administrators of military universities and to determine the causal relationships among them. As a core administrative competency in an era of complexity and uncertainty, critical thinking influences decision-making quality, strategic problem-solving, and organizational effectiveness. Its importance is heightened in military universities due to sensitive missions, hierarchical structures, and academic–defense requirements. Despite broad scholarly emphasis on critical thinking, systematic identification of its developmental dimensions for administrators in military academic settings remains limited. Method: This applied research used a mixed-method sequential exploratory design. In the qualitative phase, data were gathered through document analysis and semi-structured interviews with nine senior administrators selected purposively. Interviews continued until theoretical saturation. Qualitative data were coded and analyzed using MAXQDA. In the quantitative phase, the DEMATEL multi-criteria decision-making method was used to assess causal relationships among the extracted components. The same nine administrators participated in this stage. Findings: Four main dimensions and fourteen components were identified across intellectual–ethical, cognitive, metacognitive, and affective–behavioral domains. DEMATEL results showed that intellectual courage, fairness and intellectual humility, independence, and intellectual maturity function as causal and driving components, while emotions, persuasion, emotional intelligence, continuous learning, and inquiry operate as effect components in developing administrators’ critical thinking. Conclusion: The identified dimensions and components offer a scientific basis for designing policies and professional development programs aimed at strengthening critical thinking among administrators in military universities.

Background and Objective: In the era of rapid digital transformation, Electronic Human Resource Management (E-HRM) has emerged as a strategic driver for human capital development in public sector organizations. The main objective of this study was to investigate the direct and indirect effects of E-HRM on the development of human resource competencies, with the mediating role of organizational learning culture, within the Foundation of Martyrs and Veterans Affairs of Razavi Khorasan Province. Methodology: This applied research employed a descriptive-causal design using Structural Equation Modeling (SEM). The statistical population comprised 400 employees, from which a sample of 197 individuals was selected through simple random sampling. Data were collected using standardized questionnaires. Validity was established through content validity and convergent validity using confirmatory factor analysis (CFA), while reliability was confirmed using Cronbach's alpha and composite reliability (CR) coefficients. Findings: The findings revealed that E-HRM has a positive and significant effect on both the human resources competency development and organizational learning culture. Furthermore, the effect of organizational learning culture on human resources competency development was confirmed. The mediating role of organizational learning culture in this relationship was also proven. Conclusion: The implementation of E-HRM leads to the human resources competency development, both directly and through strengthening the organizational learning culture. These results underscore the necessity of simultaneous attention to technology and cultural foundations for the effectiveness of digital investments in the human resource domain.

Background and Objective: The aim of the present study is to conceptualize compensation for services based on gamification. Methodology: This study was conducted in two qualitative phases: the first phase was conducted with a data-based approach and with the aim of explaining compensation for services, through semi-structured interviews with 10 experts. The second phase was conducted with the aim of expanding the resulting model, using the cognitive mapping method of gamification (based on the Cho model) and the participation of 8 experts in this field. Sampling in both phases was carried out using the snowball method and based on the principle of theoretical saturation. Findings: The findings of the study showed that in the first phase, a paradigmatic model of compensation for services was developed and the components of "emotional and communicative", "executive and compensatory", and "informational and procedural" were identified as central phenomena. In the second phase, the implementation of this phenomenon on the basis of gamification was carried out using 8 motivational drives: "epic meaning and inner voice", "progress and achievement", "ability to express creativity and feedback", "ownership and appropriation", "social influence and dependence", "scarcity and impatience", "unpredictability and curiosity", and "harm and prevention" and in the form of 4 design dimensions (aesthetics, story context, technology and elements). Conclusion: Finally, the results indicate that the executive bodies of Kermanshah province are facing challenges that have caused the inefficiency of existing compensation systems; therefore, gamification was introduced as the main solution to improve and promote employee compensation.

Background and Objective: The digital transformation and the proliferation of Artificial Intelligence (AI) in Human Resource Management (HRM) have necessitated a fundamental rethinking of Assessment Center (AC) design, leading to a gradual transition toward "Smart Assessment Centers." This study aims to elucidate this evolutionary process and develop a conceptual framework for the transformation of ACs within the context of contemporary HRM. Methodology: his research employs a qualitative and exploratory approach using content analysis. Data were gathered from 42 credible scientific sources published between 2020 and 2025, selected from the Wiley and Taylor & Francis databases based on their relevance to digital transformation and HRM. The data were analyzed through three levels of coding: open, axial, and selective. The scientific rigor and trustworthiness of the findings were ensured based on Lincoln and Guba’s (1985) criteria. Findings: The findings indicate that the evolution of ACs can be explained in three distinct waves: the first wave involves the digitization of tools; the second wave focuses on augmented intelligence with a Human-in-the-Loop (HITL) approach; and the third wave centers on the emergence of ecosystems driven by generative models and interactive simulations. While smart ACs enhance operational efficiency and the richness of behavioral evidence, they pose significant challenges regarding validity, fairness, explainability, and legal compliance. Conclusion: The synthesis of the findings suggests that Smart Assessment Centers are more than mere technical tools; they are, in fact, "socio-technical systems" in which technology serves to augment, rather than replace, professional human judgment. Consequently, the future of this field lies not in pure automation, but in responsible, ethical, and human-centric intelligence.

Background and Objective: Not much research has been conducted on the applications of artificial intelligence in human resource development planning. As a result, the main objective of this study is to evaluate the applications of artificial intelligence in human resource development planning. Methodology: Comparisons were made with Solving SEM structural equations by smart pls software. Inputs were through questionnaires, interviews, and library studies. A total of 60 people knowledgeable about the research topic formed the statistical population. Findings: The outputs show that the easiest choice for development through this technology is the component of predicting and managing attrition, retaining and retaining talent with a coefficient of 0.773. In the next stage, facilitating recruitment and hiring based on skills and talent with a coefficient of 0.718, Identifying development needs and analyzing skill gaps with a coefficient of 0.553, succession planning and talent management with a coefficient of 0.483, designing and personalizing development and training programs with a coefficient of 0.450, and finally performance management and continuous feedback with a coefficient of 0.289, Requires help from complementary technologies as they are respectively less effective in this research. Conclusion: To exploit artificial intelligence that drives human resource development, this innovative research presents thematic data mining techniques with the highest relevance.

Background and Objective: This research was conducted with the aim of explaining the role of technology in sports talent identification in Iraq based on the five components of talent identification in Gagné's model. Methodology: The present research was conducted using a qualitative approach and thematic analysis method. The research sample included 15 sports talent identification specialists from three leading cities in the field of sports in Iraq, namely Baghdad, Erbil, and Basra, who were selected through purposive convenience sampling. Data were collected through semi-structured interviews based on Gagné's model and analyzed according to Braun and Clarke's six-phase framework. Research credibility was established through member checking, and dependability was ensured through independent coding with an inter-rater agreement coefficient exceeding 75%. Findings: Data analysis resulted in the extraction of 110 basic themes, 12 organizing themes, and 4 global themes. The global themes included initial assessment tools, development systems, psychological-motivational catalysts, and institutional infrastructure. The findings revealed that in the Iraqi context, technology plays a complementary role to coaches' judgment, and coaches employ simple tools systematically through creativity within constraints. Conclusion: The present research proposed a four-layer interactive framework for understanding the role of technology in talent identification, which demonstrated that in resource-constrained contexts, success lies not in technological complexity but in the systematic and creative application of technology. This framework extends Gagné's model by adding interactive and contextual dimensions.

Background and Objective: Despite the importance of leader conscientiousness, its precise consequences in the organization have received less attention. The main goal of this research is to design and test a structural model of the consequences of "leader conscientiousness" in the organization. Methodology: Considering the quantitative research approach, this research is applied in terms of purpose and descriptive-survey in terms of method. The statistical population consisted of 1,100 employees of Ibn Sina Petrochemical Company, and the sample size was determined as 284 using the Cochran formula. The data collection tool was a standard questionnaire, whose validity (convergent and divergent) and reliability (Cronbach's alpha, composite reliability, and factor loading) were confirmed. Findings: The results of data analysis using SMART-PLS software and t-statistics and path coefficients (β) indicate that all research hypotheses, except for the mediating role of "emotional burnout", were confirmed. Conclusion: The structural model designed in the context of the petrochemical industry shows that the leader's conscientiousness is a key variable that can bring important consequences such as "ethical leadership", "personal moral climate", "decision to leave the service" and "emotional burnout" in the organization. However, given the specific cultural and environmental characteristics of the statistical population under study, direct generalization of these results to other organizations, industries or societies with different cultural backgrounds requires further research and caution in interpretation. Therefore, it is suggested that Ibn Sina Petrochemical pays special attention to strengthening this characteristic in its leaders in order to advance its organizational goals.

Background and Purpose: The objective of this Research is to explore and explain the role of digital leadership in strengthening artificial intelligence-based strategic alignment in Iranian government organizations. Methodology: The present study was conducted using a qualitative approach and employing the grounded theory strategy. Data were collected through in-depth semi-structured interviews with 22 senior managers and expert specialists from government organizations with experience in implementing artificial intelligence projects, and were simultaneously analyzed using open, axial, and selective coding until theoretical saturation was achieved. Findings: The paradigmatic model of "AI-Driven Digital Leadership" was extracted, with its core phenomenon being "Transformative Digital Leadership Based on Artificial Intelligence." This model encompasses causal conditions (national policy pressures and digital transformation), contextual conditions (bureaucratic structure and infrastructural limitations), intervening conditions (cultural and ethical challenges), strategies (development of digital competencies and culture-building), and consequences (strengthening strategic alignment, innovation in public services, and improvement in organizational performance. Conclusion: This research, employs a grounded theory approach to indigenously examine the role of digital leadership in artificial intelligence-based strategic alignment within the Iranian public sector, presenting a conceptual model that can serve as a framework for policy-making and the development of managerial competencies in government digital transformation programs.

Background and Objective: In the contemporary perspective of economic development and organizational success, the concept of intelligent intellectual capital has been proposed as a determining factor. Therefore, paying attention to intelligent intellectual capital as the foundation of service provision in organizations is considered one of the basic solutions to increase organizational productivity. Therefore, the aim of the present study is to identify and classify the dimensions of the intelligent intellectual capital model in Region One medical universities. Methodology: The present study is applied in terms of purpose and qualitative in terms of method. In the first step, the dimensions of intelligent intellectual capital were identified through interviews with 10 academic and executive experts. Then, in the second step, an interpretive structural method with Excel software was used for classification. Findings: The results showed that the smart intellectual capital model includes 7 dimensions (culture and learning environment, smart processes and systems, human resource empowerment and improvement, effective leadership and management, artificial intelligence technologies, smart knowledge management and smart financing) and has four levels, with human resource empowerment and improvement at the first level, learning culture and environment at the second level, dimensions (smart processes and systems, effective leadership and management, artificial intelligence technologies) at the third level, and dimensions (smart knowledge management and smart financing) at the fourth level.

Purpose: Resilient leadership, as one of the key factors in organizational sustainability and development, is not achieved solely through leaders’ attributes; rather, followers play a significant role in shaping and strengthening organizational resilience. The present study aims to explain and analyze the mapping of followers’ characteristics in achieving resilient leadership. Method: This research is applied in purpose and survey–exploratory in terms of data collection. The research approach combines deductive and inductive methods. The statistical population consists of 20 management scholars and human resource managers selected purposefully using the snowball sampling method. Data collection tools include interviews in the qualitative phase and questionnaires in the quantitative phase. Instrument validity was confirmed through content validity and inter-coder reliability (for interviews), and test–retest reliability (for questionnaires). Data were analyzed using content analysis and coding (in the qualitative section) and Fuzzy Cognitive Mapping (FCM) in the quantitative section. Findings and Results: The findings indicate that resilient leadership results from the interaction between followers’ characteristics, the leader, and the organizational environment. Factors such as followers’ individual resilience, active participation, positive attitude, continuous learning, and trust in the leader play a decisive role. The quantitative and fuzzy analysis further showed that followers’ personal resilience, along with enhanced learning and team development, are the most central components in achieving resilient leadership. It was also found that strengthening organizational resilience requires developing followers’ resilient characteristics and creating a supportive environment by human resource management.

Background and Objective: The present study aimed to develop a model of employee productivity with an emphasis on workplace spirituality in governmental organizations of the Islamic Republic of Iran, specifically military institutions. Methodology: The present study was conducted using a qualitative approach and applied objective using content analysis. The participants included managers, experts, and professors in this field, and a sample of 14 people was selected through a purposive sampling method. Data were collected through exploratory semi-structured interviews. Content validity was ensured through expert review, and the reliability of the analysis was confirmed by an inter-coder agreement coefficient of 0.75. The employee productivity model with an emphasis on workplace spirituality was developed using a thematic analysis approach. Findings: Data analysis was conducted in four stages and resulted in 5 overarching themes, 15 constructive themes, 90 basic themes, and 204 primary codes. Conclusion: The innovative contribution of this study lies in proposing a coherent model that explains the relationship between employee productivity and workplace spirituality in light of the operational and command-related requirements of military organizations.. This model enhances combat readiness, operational effectiveness, and the organizational capability of forces, while strengthening unit cohesion and the efficiency of assigned missions.

Background and Purpose: Recreating human resources in relief organizations is not simply a change in structure or training, but rather a cultural, professional, and operational transformation that can significantly increase the level of preparedness, responsiveness, and effectiveness of the organization. The present study was conducted with the aim of investigating the consequences of recreating human resources in relief organizations. Methodology: This study is applied in terms of purpose and objective, with an exploratory approach and qualitative content analysis and theoretical sampling, and with the use of purposeful and snowball methods, interviews were conducted with 28 senior experts and experts until theoretical saturation of the data was reached, and focus group sessions were held to validate the data. Findings: The results of the data analysis obtained through the coding process through the Atread-Stirling approach of thematic analysis coding methods based on thematic networks, which results indicate (4) overarching themes and (16) organized themes and (49) basic themes that include individual and human resources consequences, organizational consequences, cultural and behavioral consequences and social consequences, were identified, extracted and confirmed. Conclusion: These consequences not only lead to improving the performance of the organization, but also play a significant role in increasing the quality of relief services in crisis situations. Recreating human resources in relief organizations by creating a change in skills, changing attitudes and improving employee motivation plays an important role in improving the efficiency.

Background and Objective: The present study aimed to compare cognitive emotion regulation, social adjustment, and cognitive distortions between users of depressant and stimulant drugs and normal individuals. Methodology: The statistical population comprised 180 individuals who visited drug rehabilitation centers in Ahvaz during Bahman and Esfand 1403 (2024/2025). Participants were selected from these centers using convenience sampling. All participants were informed about the study’s goals, and their participation was entirely voluntary. Informed consent was obtained from all individuals, and the principles of confidentiality and anonymity were observed throughout the research process. The research design was causal-comparative. Instruments included the Granefsky et al. (2001) Cognitive Emotion Regulation Questionnaire, the Bell Social Adjustment Scale (1997), and a researcher-made questionnaire for Beck’s Cognitive Distortions. Findings: Data analysis was performed using SPSS software. Significant statistical differences were found in cognitive distortions and negative emotion regulation among the measured groups. However, no significant statistical difference was observed in the social adjustment across the three groups. Furthermore, significant differences existed in cognitive distortions among the measured groups..

Background and purpose: With rapid technological advancements, artificial intelligence (AI) is emerging as an effective tool for enhancing organizational efficiency and strengthening public trust. In this context, the Law Enforcement Force of the Islamic Republic of Iran (NAJA), as an organization with unique characteristics, seeks to employ this technology to improve its performance and foster greater public confidence. Accordingly, the present study aims to examine ways of improving efficiency and increasing public trust in NAJA’s management through the application of AI. Research Method: This applied research adopts a descriptive-survey methodology. To analyze the internal relationships among these indicators and evaluate them, Interpretive Structural Modeling (ISM) and MICMAC software were applied. This approach made it possible to identify and prioritize the interrelationships among the indicators. Findings: The results indicate that "rapid and accountable responsiveness," "training and empowerment of human resources," and "improved community engagement" are among the most critical factors contributing to enhanced efficiency and increased public trust in NAJA. Moreover, the use of AI can lead to greater operational transparency, improved information accuracy, and more precise crime prediction. Conclusion: The analyses reveal that the proper utilization of AI can significantly enhance the performance of the Law Enforcement Force and substantially increase public trust.

Background and Purpose: This study was conducted with the aim of designing an architecture for the application of Mixed Reality (MR) technology in the recruitment and selection process of human capital in the Army of the Islamic Republic of Iran (AJA). Methodology: The research is developmental–applied in nature and descriptive in method, carried out through an exploratory sequential mixed-methods design. In the qualitative phase, semi-structured interviews were conducted with 15 experts in information technology and human capital management within AJA, selected through purposive sampling. Qualitative data were analyzed using thematic analysis with NVivo software. In the quantitative phase, the statistical population consisted of 120 senior managers and experts relevant to the research domain; based on Cochran’s formula, a sample of 92 participants was determined through stratified random sampling. Data collection in this phase was conducted using a 20-item researcher-developed questionnaire designed on the basis of the Zachman architecture framework. The reliability of the questionnaire was confirmed using composite reliability and Cronbach’s alpha, while construct validity was verified through convergent and discriminant validity. Quantitative data were analyzed using Smart-PLS through confirmatory factor analysis. Findings: The qualitative results led to the identification of key components of MR application in the recruitment and selection process. The quantitative findings confirmed that the proposed model demonstrated a satisfactory goodness-of-fit. Ultimately, the finalized architecture outlined the deployment of MR technology across multiple levels, ranging from strategic planning (contextual level) to operational implementation (executional level). Conclusion: The findings indicate which the application of Mixed Reality in human capital recruitment and selection can transcend a merely instrumental and technological level and evolve into a strategic and transformative domain. Accordingly, the role of MR is not confined to task simulation or cost reduction; rather, when integrated with organizational architecture, it can provide the foundation for reengineering the recruitment and selection paradigm within the armed forces.

Background and Objective: Traditional hiring processes are faced with challenges such as human biases and a lack of precision. With the aim of increasing accuracy and fairness in selection, this research introduces an intelligent multimodal system that assesses applicants' personalities by simultaneously analyzing verbal and non-verbal signals based on the six-factor psychological model. Methodology: With its multi-stage architecture, the system first analyzes verbal content using advanced speech-to-text models. Concurrently, it extracts facial states and movements by leveraging facial analysis libraries and the Facial Action Coding System. Finally, a deep learning model with a cross-modal attention mechanism combines these two facets of information to predict personality traits. Findings: Evaluations showed that the multimodal model performs with significantly more accuracy than unimodal models. The system's validity was confirmed by comparing its results with the evaluations of expert psychologists. Additionally, an interpretable analytical dashboard was designed to provide evidence (quotes and clips) to human resources managers. Conclusion: This research provides an innovative, precise, and evidence-based framework for psychological analysis within the hiring process. By relying on the six-factor psychological model and a multimodal architecture, this system has the potential to transform traditional selection processes towards fairer, more objective, and more efficient procedures.

Purpose: This study examines the effect of developing in-service training on the job empowerment of Nezaja employees with the role of motivation as a mediator in the applicability of the Second Step of the Revolution statement. Methodology: This study is applied in terms of purpose, descriptive-analytical in terms of data collection method, and quantitative in nature. The time frame of the study is 1403. The statistical sample of the study was selected from among Nezaja employees according to the Cochran formula in an unlimited population, in the first stage based on cluster sampling and in the second stage, available random sampling. Data were collected through questionnaires, the content validity of which was confirmed by professors and experts, and the construct validity was confirmed by factor analysis and reliability through Cronbach's alpha values. Then, based on the obtained data, the fit of the research model based on confirmatory factor analysis was first examined, and the structural equation modeling method was used to test the hypotheses using Smart PLS software. Finding: Based on the results obtained, the development of in-service training has a positive and significant effect on the motivation and job empowerment of employees, and motivation has a positive and significant effect on the job empowerment of employees in Nzaja. Finally, considering the mediating role of motivation, these three categories should be addressed simultaneously and systematically in order to achieve organizational effectiveness.

Background and Objective: The purpose of this study was to investigate the role of organizational commitment, job involvement, emotional labor, and organizational climate of human resources in the quality of power plant project services. Research Method: This descriptive-analytical research was conducted based on a questionnaire. The statistical population consisted of 183 specialists from MAPNA Company, of whom 125 were selected as samples by simple random method and based on the Cochran formula. The data collection tool was based on a modified standard questionnaire with a five-option Likert scale. The validity of the questionnaire was confirmed using content and construct validity and its reliability using Cronbach's alpha coefficient. SPSS 25 software was used to analyze the data, and the smart-PLS method was used to test the quality of the structural model. Findings: The findings showed that the organizational commitment variable (with a path coefficient of 0.195 and a t-statistic of 3.292) and the job involvement variable (with a path coefficient of 0.375 and a t-statistic of 6.075) have a direct and significant effect on service quality in the electrical installation company. Also, the job involvement variable mediates the effect of organizational commitment on service quality in the electrical installation company (significance of the mediating effect of 6.064), while the organizational climate variable moderates this effect (with a path coefficient of 0.396 and a t-statistic of 5.998). However, the moderating role of the emotional labor variable in the effect of job involvement on service quality in the electrical installation company was not confirmed. Conclusion: Since modern organizational behavior and organizational performance are facing increasing complexities, it cannot be expected that emotional labor will moderate the relationship between organizational commitment and service quality in every situation and in every statistical population.

Objective: Given the extensive technological changes in the country’s banking system and the sudden transition of Bank Sepah to a new comprehensive system in June 2025, frontline employees faced increased psychological and job-related pressure. In such circumstances, managers’ leadership style plays a crucial role in enhancing employees’ resilience. This study aimed to examine the impact of transformational leadership on the resilience of employees in the Tehran Region Four branches of Bank Sepah. Methodology: The research method is applied and descriptive–survey in nature. The statistical population consisted of all employees of Region Four branches (930 individuals). Based on Cochran’s formula, 272 participants were selected through stratified random sampling. Data collection tools included the standard Bass and Riggio (2006) questionnaire to measure transformational leadership, and the Luthans and Youssef (2017) questionnaire to measure employee resilience. The reliability of the instruments was confirmed with Cronbach’s alpha coefficients above 0.7. Data were analyzed using Spearman’s correlation coefficient and multiple regression in SPSS software. Findings: The results showed that all dimensions of transformational leadership (idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration) had a positive and significant effect on employees’ resilience. Among these, inspirational motivation demonstrated the greatest impact on enhancing resilience. Originality: The findings indicate that developing transformational leadership skills can increase employees’ adaptability and recovery ability in crisis situations, ultimately leading to improved service quality and organizational sustainability. The novelty of this study lies in its focus on a real technological crisis in Bank Sepah and its examination of frontline employees as the group experiencing the greatest pressure from organizational changes.

Background and Objective: One of the most important factors for the survival and development of schools is agility. An environment that is always changing lts teachers must be agile. one of the effective factors in the agility of teachers is the leadership style of managers. Quantum leadership is one of the newest and most efficient leadership styles. The aim of this study was to explain how teachers in schools Tehran are motivated by quantum leadership. Methodology: The research method is applied with a mixed approach In the qualitative part, theoretical information related to the research was obtained fram documents. Interviews with experts from among the managers of education districts in Tehran and faculty with subject expertise were conducted through purposive method Toexamine the validity form the expert’s point of view and to determine the reliability of the peer self-review method were used. The quantitative part included 2 categories of school managers and teachers,118 people were determined using the morgan table and quantitative analysis was performed with the SPSS software. Findings: Based on theoretical frameworks,expert interviews, and Based on theoretical frameworks, expert interviews, and questionnaires administered to the study population, the dimensions, components, and indicators affecting teachers' agility through quantum leadership were identified. These include 6 dimensions, 14 components, and 30 indicators. Conclusion: Quantum leadership skills-such as conscious intuitive thinking,creative thinking, situational thinking, and the ability to inspire self-organization -enhance the agility of teachers in Tehran.

Background and Objective: The digital transformation has paved the way for the emergence of the concept of smart governance, which emphasizes the use of modern technologies to enhance governmental efficiency and accountability. However, much of the existing research—particularly within the digital government literature—has been dominated by a technology-centric approach, overlooking the human factor and the qualities of governors. This has created a significant theoretical gap in governance studies. The present study aims to address this gap by highlighting the primacy of the smart governor over tools and structures in the realization of smart governance. Methodology: The study employs a theoretical–analytical and qualitative comparative approach, drawing upon the IntelliGov conceptual model (Vigoda-Gadot, 2024), which examines three levels of intelligence: human, institutional, and artificial. Data were collected through a critical review of scholarly literature, international policy reports (OECD, UNDP, World Bank), and case studies of four selected countries—Taiwan, Estonia, Denmark, and India. Findings: The findings indicate that in countries such as Taiwan and Denmark, the presence of smart governors has strengthened institutional capacity and promoted the ethical use of technology. In contrast, India’s experience demonstrates that the rapid expansion of technology without reinforcing human and institutional dimensions deepens the digital divide and erodes public trust. Conclusion: This study argues that smart governance is fundamentally a human-centered phenomenon, with technology serving only as an enabling factor. Therefore, cultivating and developing smart governors must be prioritized in public policy and governance strategies.

Background and Objectives: This research aimed to predict employees' coping behaviors when faced with job stress, using a combination of structural equation analysis and machine learning methods. This study seeks to provide a scientific framework for better understanding stress coping mechanisms in workplaces. Methodology: The statistical population consisted of 240 employees of a service organization who were selected by simple random sampling method. Data were collected through standardized questionnaires and their reliability was confirmed with a Cronbach's alpha coefficient above 0.85. The data analysis process was carried out in two main stages: in the first stage, structural equation analysis (SEM) was used to examine the relationships between variables, and in the second stage, modeling was carried out with various machine learning algorithms. Findings: The results of structural equation analysis showed that the three variables of job stress, organizational support, and self-efficacy have a significant effect on coping behaviors. In the machine learning section, the multilayer neural network (MLP) algorithm was recognized as the best predictive tool with a significant accuracy of 92.5% and a coefficient of determination of 0.68, which indicates its high predictive power. Conclusion: The hybrid model presented in this study is considered an efficient, accurate and scientific tool for predicting coping behaviors and designing stress management programs in various organizations. This model can help managers and decision-makers to better understand employees' reactions to stress and implement more effective strategies to help improve mental health and increase efficiency in the workplace.