Management and Educational Perspective

Abstract The present study aims to develop a qualitative model of the interactions and outcomes of artificial intelligence-based human resource management (AI-HRM). In terms of purpose, the research is exploratory, and in terms of implementation, it adopts a qualitative approach. The study population consisted of 18 university scholars, managers, and human resource management experts who were selected through purposive sampling. Data were collected using semi-structured interviews and analyzed through thematic analysis. The findings led to the identification of 183 codes, categorized into 28 basic themes and 27 organizing themes. The results indicated that AI-based human resource management comprises four principal dimensions: interactive algorithms in the interview screening process, including the interactive enhancement of interviews, intelligent matching of applicants with organizational competencies, and interactive analysis of résumé data; dynamic performance appraisal, including interactive evaluation of work quality, interactive prediction of long-term performance, and automated continuous feedback; employee interaction with the learning and development system, including interactive career path recommendation, adaptive learning content, and soft-skills simulation; and interactive human resource planning systems, including workforce estimation, turnover intention prediction, and improvement of HR processes. These four dimensions directly affect three major outcomes: operational productivity, with emphasis on automating administrative decrees and reducing leave-processing time; improvement in HR decision-making, with emphasis on the unbiased standardization of interviews and monitoring of behavioral data; and human resource value creation, with emphasis on shared value creation and alignment with strategic objectives.

Abstract The current research aims to validate the digital talent management model using artificial intelligence. The research methodology, considering its objective, is applicable; and in terms of execution, it is mixed (qualitative-quantitative). The statistical population of the research in the qualitative section includes 14 university professors in the field of public management and managers and experts of mining companies, selected by purposive sampling. The statistical population in the quantitative section includes active employees of mining companies, of which 215 individuals were considered based on Cochran’s formula, using a random sampling method. Data collection in the qualitative section was derived from semi-structured interviews based on theoretical foundations. The validity of the codes in the qualitative section was confirmed by two independent researchers, and its reliability was confirmed by the Cohen’s Kappa coefficient. In the quantitative section, a questionnaire was used. For data analysis, thematic analysis was used in the qualitative section, and SPSS and Lisrel software were used in the quantitative section. As a result of this process, the dimensions of the process functions (automatic screening and decision support tools), job matching (precise matching of jobs and skills, improvement of qualitative assessments), personality trait evaluation (analysis of psychological tests, identification of personality types), educational recommendations (personalized training, intelligent and dynamic training, triple synergy, and educational justice), and prediction of job turnover (job turnover analytical system and preventive diagnosis) were identified. The results of confirmatory factor analysis also indicate the validity of the final conceptual model of the research.
Introduction
Nowadays, with the advancement of technology, traditional talent management processes have become manual, time-consuming, and inefficient. Artificial intelligence (AI) has demonstrated its potential significantly across various sectors and has currently become a strategic tool for improving organizational efficiency (Abraham et al., 2025). With the rapid development of AI, big data, cloud computing, and other tools, traditional talent management methods can no longer meet the development needs of modern organizations. These tools may provide opportunities and challenges for reforming the human resource management system (Sithambaram et al., 2023). Traditional methods cannot objectively and reasonably collect in-depth data on organizational talent. These tools are incapable of accurately managing the analysis and processing of data resources such as the alignment status between talents and positions, and the matching status between talents and their superiors and subordinates. Talent selection, utilization, training and development, and retention, within digital talent pools using traditional methods, are not only unfavorable to the long-term development of organizations and talents but also, to some extent, reduce the enthusiasm of organizational talents (Saini & Tarkar, 2022). Conversely, many researchers have emphasized the transformative potential of AI in reshaping traditional approaches to talent acquisition, development, and retention, highlighting its benefits in improving recruitment processes, such as reducing bias in candidate selection and enhancing predictive analytics for identifying outstanding potential talents. Accordingly, AI simplifies administrative tasks, allowing HR professionals to focus on the strategic aspects of talent management. This is because talents are considered highly valuable resources for organizations to achieve long-term development (Weber et al., 2024). On the other hand, studies show that very few digital talent management models have been presented in this area. For example, the talent attraction and selection model proposed by Kathiravan et al. (2023) can be mentioned. This model optimizes the recruitment and selection process of employees using AI and machine learning algorithms. Talent management systems can automatically analyze resumes and identify the best candidates based on predefined criteria. This approach not only reduces recruitment time and costs but also increases selection accuracy. The AI-based talent development and training model proposed by Kaliannan et al. (2023) also aids in personalizing training and development processes. Given the aforementioned points, the main research question of the current study is: How is the digital talent management model based on artificial intelligence validated?
Theoretical Framework
Talent Management
Talent management is an integrated strategy or system designed to develop processes for recruiting, selecting, and developing individuals, and retaining those with the necessary skills, often examined with a long-term, comprehensive plan in mind (Almaaitah et al., 2020).
Artificial Intelligence
As a branch of computer science, artificial intelligence develops an intelligent machine product that, based on understanding the nature of intelligence, reacts in a manner similar to human intelligence. Research in this field primarily includes image recognition, speech recognition, language processing, expert systems, and robotics (Yanling Shi, 2020).
El Hajal et al. (2025) explored artificial intelligence and the future of talent management in tourism and hospitality. Using scenario planning, they outlined four alternative futures for AI-driven talent management, emphasizing the importance of ethics, continuous learning, and the complementary role of AI.
Kumar & Hariharasudhan (2025) investigated the role of artificial intelligence in recruitment and talent management. Through qualitative interviews, they found that AI can automate resume screening and interview scheduling stages, thereby reducing human biases.
Research Methodology
The research method, considering its objective, is applicable; and in terms of execution, it is a mixed-methods (qualitative-quantitative) approach. The statistical population of the research in the qualitative section includes 14 university professors in the field of public management and managers and experts in mining companies, selected using purposive sampling. The statistical population in the quantitative section includes active employees of mining companies, with 215 individuals determined based on Cochran’s formula, using random sampling. Data collection in the qualitative section was based on semi-structured interviews derived from theoretical foundations. The validity of the codes in the qualitative section was confirmed by two independent researchers, and its reliability was confirmed by Cohen’s Kappa coefficient. In the quantitative section, data was collected through questionnaires.
Research Findings
Thematic analysis was used to analyze the data in the qualitative section, while SPSS and Lisrel software were used for the quantitative section. As a result of this process, the identified dimensions include dimensions of process functions (automatic screening and decision support tool), job matching (precise job and skill matching, improvement of qualitative assessments), personality trait evaluation (analysis of psychological tests, identification of personality types), educational recommendations (personalized training, intelligent and dynamic training, triple synergy, and educational justice), and prediction of job turnover (job turnover analytical system and preventive detection). The results of the confirmatory factor analysis also indicate the validity of the final conceptual model of the research.
Conclusion
The present research aimed to validate the digital talent management model utilizing artificial intelligence. The results of this study align with findings from Kumar & Hariharasudhan (2025), Kambur & Akar (2022), and others such as Faqihi & Miah (2023), Yazdani & Ahmadi Qajari (2024), Heidary et al. (2023), and El Hajal et al. (2025). Faqihi & Miah (2023) demonstrated that AI-based talent management, employing machine learning and moderating technology theories, leads to better job matching and targeted training.
Based on the results obtained from this research, the following recommendations are proposed:
Other researchers are encouraged to investigate the relationship between the identified dimensions in the current study and other variables, such as attrition intention, digital emotional exhaustion, human capital management, and digital organizational commitment. To enhance the external reliability and generalizability of the proposed model, it is recommended that this model be applied in other private and public companies across various industries, with results presented comparatively. Given that the statistical sample of this study was purposively selected from employees of mining companies, caution and necessary considerations should be taken when generalizing the findings to other organizations, industries, or organizational cultures. Acknowledgments The authors gratefully acknowledge all individuals, institutions, and organizations that provided scientific, technical, administrative, or advisory support throughout this research and the preparation of this manuscript. Conflicts of Interest The authors declare that they have no financial or non-financial, personal, professional, or institutional conflicts of interest relevant to this work. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. AI Use Statement The authors declare that no generative artificial intelligence (AI) or AI-assisted technologies were used in the preparation, writing, or editing of this manuscript. Funding The authors declare that no funds, grants, or other financial support were received during the conduct of this research or the preparation of this manuscript. Ethics Approval All procedures performed in this study were conducted in accordance with the ethical standards of the responsible institutional and national research committee.

Abstract The aim of the present study is to study the knowledge staff narcissism management model with a content analysis approach. The research method is qualitative in terms of implementation method, and is of the content analysis type. The statistical population of this study includes 15 university professors and managers and experts in public administration. The sampling method in this study was purposive. This sampling continued until theoretical saturation was reached. The research collection tool is semi-structured interview. Data analysis was carried out using the content analysis method and MAXQDA software. The results of the study showed that the data were systematically coded and in the first stage, 254 initial codes were extracted and classified in the form of 62 basic themes and 20 basic organizing themes. Finally, five overarching themes were identified as the main dimensions of the research conceptual model, which are: moral guidance, managerial intervention, individual training, professionalism, and moral self-management. In general, managing narcissistic behavior in knowledge workers in organizations requires simultaneous attention to ethical guidance, management intervention, individual training, professionalism, and ethical self-management. These five dimensions provide a coherent framework for promoting ethical actions at the individual and organizational levels.
Introduction
Today, narcissism has become one of the most important topics in the field of organizational behavior (Ren et al., 2024). This behavior is observed more often among knowledge workers than in other occupational groups (Mohd et al., 2023). Narcissism, as a personality trait, refers to an individual's desire to receive praise and exaggeration about themselves (Sarfarazi et al., 2021).
Maccoby (2021) defines narcissism in employees as personality traits in which an individual tends to see themselves as superior to others and needs approval and admiration from others. Narcissists tend to seek attention and approval and may be insensitive to the needs of others in their social and professional interactions. This trait can manifest itself in both positive and negative ways in the workplace. While some aspects of narcissism, such as self-confidence and leadership ability, can be beneficial, its negative aspects include a lack of empathy and difficulty in teamworking. Accordingly, narcissists tend to have undesirable traits such as exploitativeness, lack of empathy, aggression towards critics, and disregard for expert advice. Interestingly, narcissistic knowledge workers may initially be positively evaluated and noted for both their popularity and leadership abilities. However, these positive perceptions of these individuals diminish over time as others gradually become aware of their undesirable traits. According to social influence theory, people who are more skilled in influence management tactics will be more successful in conveying a certain self-image to others. For example, an employee with high knowledge skills can convey an image of competence and likability to others through self-promotion. In contrast, an employee with low knowledge skills may present a neutral and passive self-image to his or her supervisors and colleagues (Chen et al., 2024).
Narcissism can become a very important indicator of positive and negative organizational behavior. Isfahan Petrochemical Company (Isfahan Petrorefining Holding) is a public company that was established with the aim of producing aromatic products including benzene, toluene, orthoxylene, paraxylene, and mixed xylene (Hosseini, 2023). One of the most important reasons for choosing the Isfahan petrochemical industry as the context for the study is that this company needs to maximize the potential of its workforce due to the complex and competitive nature of the industry. The number of knowledge workers in this company is 700 people with diverse organizational abilities. Knowledge workers in this industry usually face numerous technical and managerial challenges that require effective cooperation and interaction between team members. Since narcissism can prevent healthy and effective communication between individuals, effective management of narcissism of knowledge workers in the petrochemical industry should be considered as a vital element for maintaining competitiveness and success in the market. According to the contents, the main question of the present research is: What is the management model of employee narcissism in the petrochemical industry?
Theoretical framework
Employee narcissism
Narcissism is a phenomenon that has existed in human societies for a long time; narcissism is an innate and fundamental human personality trait that may be intensified in a way that dominates other personality traits depending on the type of genetic background of individuals, family upbringing, growth conditions, and social and environmental conditions (Tang et al., 2018).
Zheng et al. (2025) examined the relationship between grandiose narcissism and creativity: A study of Chinese high-tech companies. The results showed that participants with grandiose narcissism reported significantly higher levels of creativity and psychological well-being in creative performance than those with vulnerable narcissism.
Bali et al. (2024) examined the design of a narcissism model for sports managers with a qualitative approach. They showed that the narcissism model of sports managers includes positive dimensions (leadership and inspiration ability, high self-confidence, motivation and ambition, creativity and innovation), and negative dimensions (grandiose and excessive pride, self-centeredness and disregard for others, intolerance of criticism and criticism, dominance and control, lack of empathy and understanding of others, refusal to accept failure and mistakes).
Research Methodology
The research method is qualitative in terms of implementation, of the type of content analysis. The statistical population of this study includes 15 university professors, managers and experts in public administration. The sampling method in this study was purposive. This sampling continued until theoretical saturation was reached. The research collection tool is a semi-structured interview.
Research findings
Data analysis was carried out using the content analysis method and MAXQDA software. The research results showed that the data were systematically coded and in the first stage, 254 initial codes were extracted and classified in the form of 62 basic themes and 20 basic organizing themes. Finally, five overarching themes were identified as the main dimensions of the conceptual model of the research, which are: moral guidance, managerial intervention, individual training, professionalism and ethical self-management. In general, managing narcissistic behavior in knowledge workers in organizations requires simultaneous attention to ethical guidance, management intervention, individual training, professionalism, and ethical self-management. These five dimensions provide a coherent framework for promoting ethical actions at the individual and organizational levels.
Conclusion
The present study was conducted with the aim of developing a model of managing narcissistic behavior in knowledge workers using a content analysis approach. The results of this study are consistent with the results of Zheng et al. (2025), Bali et al. (2024), Hassanzadeh & mehdinia (2022), Sayidi (2022), Al Behbahani (2022), Pashazadeh Kahaq et al. (2022), Shariatinejad et al. (2022), Fahim Talebi et al. (2021), and Klimchak et al. (2016). Zheng et al. (2025) showed that participants with high narcissism reported significantly levels of creativity and psychological well-being in creative performance compared to those with low narcissism.
According to the results of the study, the following suggestions were made:
Ethical guidance increases employee trust, reduces conflicts, and promotes empathy and cooperation in the workplace, and plays an important role in preventing narcissistic and harmful behaviors.
Management intervention includes providing feedback, continuous monitoring, and timely intervention in the event of behavioral deviations or decreased performance. It is suggested that unproductive and self-centered employee behaviors are identified and corrected and organizational processes are directed towards common goals. Acknowledgments The authors gratefully acknowledge all individuals, institutions, and organizations that provided scientific, technical, administrative, or advisory support throughout this research and the preparation of this manuscript. Conflicts of Interest The authors declare that they have no financial or non-financial, personal, professional, or institutional conflicts of interest relevant to this work. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. AI Use Statement The authors declare that no generative artificial intelligence (AI) or AI-assisted technologies were used in the preparation, writing, or editing of this manuscript. Funding The authors declare that no funds, grants, or other financial support were received during the conduct of this research or the preparation of this manuscript. Ethics Approval All procedures performed in this study were conducted in accordance with the ethical standards of the responsible institutional and national research committee.