Document Type : Research Paper
Authors
1
PhD Student Department of Sports Management, Shoushtar Branch, Islamic Azad University, Shoushtar Branch, Iran
2
Assistant Professor, Department of Physical Education, Izeh Branch, Islamic Azad University, Izeh Branch, Iran
3
Assistant Professor of Physical Education, Shoushtar Branch, Islamic Azad University, Shoushtar Branch, Iran
10.22084/smms.2022.25578.2996
Abstract
The purpose of this study was to present a model of prerequisites for establishing a talent management process in football in Khuzestan province. The population studied in this study were the officials of the talent identification committee of the football board of Khuzestan province and cities and the coaches were experts. The sampling method was targeted and in the form of snowballs. The research method was based on data theory and the tool was to collect the findings through semi-organized interviews. The interview was semi-structured with open-ended questions and up to 20 in-depth interviews and systematic analysis of the required data. The data were collected and analyzed through three steps of open, centralized and selective coding. In the second part of the research, AHP technique was used to prioritize the findings of Yenbad data theory. According to the findings, there are a total of 33 categories and 56 components in football talent identification in Khuzestan province. The findings also showed that among the causal factors, attention to basic football among the bedrock factors, the championship and professional characteristics of football, among the intervening factors, investment of other provinces and neighboring countries in football, from Among the driving factors, the development of grassroots football was the first priority. It can be said that the implementation of the strategies of establishing a football academy and talent identification center, financial support for talent identification, determining football talent identification indicators and developing interactions and interactions,
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