درخت حوزه‌های تخصصی

Purpose: The present study aims to investigate the relationship between smart talent management and meritocracy in order to provide the expressive characteristics of the selection, appointment and maintenance of managers. Methodology: The research method is descriptive survey and the required information has been collected using a researcher-made questionnaire that its validity has been determined from the available resources and through expert opinion and its reliability has been determined by calculating the Cronbach's alpha coefficient (.882). The statistical population of the present study includes the associate professor faculty members and higher rankings and staffs having MA and higher degrees, i.e. a total of 1,200 people. Sampling method is stratified random sampling through which 291 people are selected as the statistical sample. Findings: Findings of exploratory factor analysis show that strategy, attraction, evaluation and discovery of talents are the main components of talent management, and organizational learning, knowledge expansion and sharing are the main components of knowledge management. Among the 13 dimensions of talent management, the development of talent with a standard coefficient (.90) and preservation of talent with a standard coefficient (.88) are the most effective ones, respectively. Discussion: Among the 13 dimensions of knowledge management, expansion and sharing with a standard coefficient (.95) and organizational learning with a standard coefficient (.88) are the most effective ones, respectively. Also, confirmatory factor analysis confirms the relationship between smart talent and meritocracy with a standard coefficient (1.06), and test statistics has evaluated the severity (correlation coefficient) of this relationship (.299).

Purpose: The aim of this study was to identify the main components of school performance evaluation in desirable condition. Methodology: The present study was a descriptive cross-sectional study. The statistical population of the study consisted of 1980 high school principals who were selected based on Cochran's formula of 322 students using cluster-cluster sampling method. Educational management (including 15 university professors, 20 administrators and 15 school administrators) selected through purposive sampling, semi-structured interviews were conducted, primary components identified and then identified by the researcher to determine the final components. It had 172 items and its face validity and content validation by Tidopa experts for it was 75/0 using Cronbach's alpha, was used. Data were analyzed using exploratory factor analysis and structural equations. Findings: The findings indicated that the following components could be considered for evaluating the performance of 12 components and 172 sub-components: 1- Management and leadership 2- Administrative and financial affairs 3- Teaching processes- Learning and planning 4-Axis programs 5-Information technology and intelligence 6-Physical space and equipment 7-Empower teachers and staff 8-Supplemental, extracurricular activities 9-Organizational climate and organizational culture10-Interaction with school environment and Gaining Experiences11- Physical Education, Health and Well-being 12- Staff Involvement, Parents and Students in School Affairs Conclusion: School management has a very important role in school performance. Therefore, school management and leadership as the main pillar of schools should play a scientific role and with respect to good human relations which will play an important role in enhancing school performance. Keywords: Evaluation, Performance, Schools, Desire.

Purpose: The main purpose of this study was to identify the components of SMEs' knowledge rewards, and based on other components, learning and development strategies were developed based on the obtained components. Methodology: The present study was a descriptive survey in terms of purpose and method of data gathering and was mixed in terms of research method. Therefore, in the qualitative part, using semi-structured interviewing tool with 15 human resources experts and stakeholders Interviews were conducted and the data were analyzed using qualitative content analysis and a model was designed by combining them. Then, the model was tested by a researcher-made questionnaire and sample of 162 SMEs in Tehran, using quantitative structural equation research method with LISREL software and then to formulate strategies and strategies related to learning and learning. The paired t-test and SPSS software were used for development. Reliability of qualitative part of Holstein method was 0.83% and Cronbach's alpha coefficient was 0.81. Findings: The findings showed that 22 sub-components of comprehensive reward factors were identified, all of which had appropriate factor loadings and the model was confirmed. Among the non-material dimensions of learning and development, it was the most important factor and in explaining learning and development strategies, the highest gap was observed in education, which was the first and foremost strategies in this field. Conclusion: The findings of the study confirm the relationship of each of the material and non-material components to the concept of comprehensive reward, but the most important is the importance of the material components to the comprehensive reward. In the payment component, cash awards and in benefits, the benefits of flexibility had the highest impact, but among the non-material components that were secondary to the overall reward, the highest impact was on the learning and development component, which included performance evaluation. It matters the most. Non-material factors were expected to be of high priority for knowledge workers, but this was not favorable to the statistical population.