Management and Educational Perspective

Abstract Abstract The purpose of this research is the model of recruitment and selection of human resources of the public sector with the approach of the entrepreneurial government. This research is applicable in terms of its purpose, qualitative in terms of the data collection method, and a meta-composite approach in terms of the research implementation method. The statistical population of the research includes all elites and university professors and relevant experts in the field of human resources and entrepreneurial government in Sistan and Baluchistan province, whose number in 2020 is about 200 people, who were selected non-randomly in a purposeful way. Meta-composition method was used for data analysis. Extraction of components and indicators was obtained from background investigation and theoretical foundations, and then coding was used for analysis. According to the data analysis, 545 final codes were extracted through open coding and then, using axial coding were categorized in the form of 121 concepts and 16 categories; contextual (environmental) factors, structural (organizational) factors, behavioral (content) factors, Recruiting human resources, selecting human resources, identifying entrepreneurial opportunities in the public sector, exploiting entrepreneurial opportunities in the public sector, executive and managerial obstacles, political and legal obstacles, economic obstacles, cultural and social obstacles, combined selection with domination of internal resources in experience-based jobs, combined selection with the domination of external resources in knowledge-based jobs, individual consequences, organizational consequences, social consequences, and finally, in the stage of selective coding, the process and multidimensional model has been presented for the first time at the level of government organizations of Sistan and Baluchistan province. Extended abstract Introduction Human resource management is often considered as an effective approach in managing people, and its function has evolved from being only responsible for keeping records to being a strategic partner. One of the new roles of human resource management is the development of innovation and entrepreneurship. In this regard, many governments around the world believe that entrepreneurship is the key to economic development; therefore, many governments have started to offer entrepreneurship development programs. Since the theories of "entrepreneurial government" or "government entrepreneurship" were proposed by Osborn & Geabler (1992), government organizations have been keenly seeking to make changes in their management patterns, so that they can benefit from hidden advantages within these theories, which are partly derived from the management of the private sector. One of the necessary changes in this field is related to the field of human resources management as one of the internal components of government organizations (Narula & Chaudhary, 2018). Today, organizational entrepreneurship improves productivity at various individual and organizational levels, which requires a suitable foundation in the organization, therefore, flourishing in today's evolving world belongs to the societies and organizations that establish a meaningful relationship between their management capabilities and entrepreneurship of their human resources and organization; in the other word, any society or organization can move forward in the direction of development provided that by creating suitable platforms, manage the valuable human resource management system of other resources of society and organization so as to create value and acquire growth and development. The results of the studies of Hasanzade et al, (2019, 2020) and Hasani Sadrabadi (2017) in Iran's government organizations show that the establishment and use of entrepreneurial initiatives in Iran's government organizations have not been successful, and the improvement of management characteristics and entrepreneurship in these organizations requires structural and sustainable changes in public administration. Unfortunately, the current situation cannot be improved, unless efforts are made to develop human resource management development policies that can cause real and entrepreneurial changes in the state and behavior of managers and employees. Despite the mentioned issues, it seems that the human resource management system can help to promote organizational entrepreneurship in government organizations (Ahmadi et al, 2012). Therefore, according to the issues raised, the current research intends to answer the question; what is the model of recruitment and selection of human resources in the public sector with the entrepreneurial government approach? Theoretical Framework Recruitment and selection of human resources Recruitment and selection includes actions and activities carried out by an organization in order to identify and attract people in the organization who have the ability to help the organization to achieve its strategic goals. Specifically, such activities should create a pool of desirable candidates; increase their interest and attraction to the organization as an employer, and increase the likelihood that they will accept a job offer (Teimouri et al, 2018). Entrepreneurial government According to Morris & Jonse (1999), entrepreneurship in the public or government sector is the process of creating value for citizens by bringing together a unique combination of public or private resources in order to take advantage of social opportunities. In the other word; Government entrepreneurship is an organizational process that includes the improvement and development of current and new services and programs, technological innovation (techniques, procedures and laws and new administrative and selection processes) and organizational innovation (new management processes and new structuring methods) in the public sector, through the application of a new model of resource allocation to create value for citizens (Hasanzade et al, 2019). Gheiravani et al, (2023) investigated the purpose of providing a smart governance model focusing on the development and training of human resources in the public sector. The results showed that all the components of smart governance in the public sector were identified and their significance was confirmed in the research, and by examining themes, concepts and quantitative and qualitative research, the smart governance model was explained with a focus on the development and training of human resources in the public sector.  Fazlali & Moazzami (2022) investigated the effect of organizational leadership style on human resource management while emphasizing the mediating role of competitive work environment. The results of the research show that organizational leadership style has no significant effect on human resource management and has been rejected; however, the significant effect of organizational leadership style on the competitive work environment, the competitive work environment on human resource management, and the organizational leadership style on human resource management with the mediating role of the competitive work environment has been confirmed. Research methodology This research is applicable in terms of its purpose, qualitative in terms of the method of data collection, and a meta-composite approach in terms of the research implementation method. The statistical population of the research includes all elites and university professors and relevant experts in the field of human resources and entrepreneurial government in Sistan and Baluchistan province, whose number in 2020 is about 200 people, who were selected non-randomly in a purposeful way. Research findings For data analysis, meta-combination method was used. Extraction of components and indicators was obtained from background investigation and theoretical foundations, and then coding was used for analysis. According to the data analysis, 545 final codes were extracted through open coding and then, using axial coding, were categorized in the form of 121 concepts and 16 categories; contextual (environmental) factors, structural (organizational) factors, behavioral (content) factors, Recruiting human resources, selecting human resources, identifying entrepreneurial opportunities in the public sector, exploiting entrepreneurial opportunities in the public sector, executive and managerial obstacles, political and legal obstacles, economic obstacles, cultural and social obstacles, combined selection with domination of internal resources in experience-based jobs, combined selection with the domination of external resources in knowledge-based jobs, individual consequences, organizational consequences, social consequences, and finally, in the stage of selective coding, the process and multidimensional model has been presented for the first time at the level of government organizations of Sistan and Baluchistan province. Conclusion The current research was conducted with the aim of recruiting and selecting human resources of the public sector with the entrepreneurial government approach. According to the obtained results, the present research is aligned with the results of Gheiravani et al, (2023), Fazlali & Moazzami (2022), Munaty et al, (2022), Fitri et al, (2021), Orakwue & Iguisi (2020), Moustaghfir (2020), Barani et al, (2022), Hasanzade et al, (2020), Noroozian et al, (2020), Tabatabai Mazdabadi et al, (2020), Sajadi Parsa et al, (2020), Narula & Chaudhary (2018). Narula & Chaudhary (2018) showed that entrepreneurship is a commitment in which a certain person goes through favorable opportunities by successfully exploiting available resources. Entrepreneurs cannot run a business alone. An entrepreneur must hire, assign people to various tasks, influence employees and retain and prepare them for the growth and development of the company. Since there are many things that need to be done from the establishment to the growth of a company, the entrepreneurial process facilitates the needs of human resource management practices. This research highlighted the relationship between human resource management methods and context and entrepreneurship. According to the obtained results, the following suggestions are provided: All recruitment processes in government organizations should have scientific and legal processes, so that in addition to reducing administrative corruption, employment justice is also established in the government departments of the province. Double emphasis on the necessity of meritocracy in the appointment of public sector managers of Sistan and Baluchistan province and paying attention to their entrepreneurial personality traits at the time of selection Revision of the job description in the public sector jobs of Sistan and Baluchistan province, emphasizing the entrepreneurial aspects of the jobs.

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