Presenting the Knowledge Development Model of Future Managers Based on Talent Management

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

This research aims to present a knowledge development model for future managers based on talent management with a grounded theory approach in the Iranian Social Security Organization (headquarters). This research is applied in terms of purpose and exploratory in terms of method. It uses a mixed methods approach for data collection and analysis, qualitative and quantitative data analysis techniques-   grounded theory and structural equation. The research tool in the qualitative part was a semi-structured interview. In the qualitative part, using the grounded theory method, data obtained from the interviews with 12 elites and qualified specialists of the Social Security Organization, which were analyzed manually and by using Atlas TI 8 software during three stages of open, central, and selective coding that resulted in generation of 19 categories. The results were presented in the form of a paradigm model that includes causal conditions (individual factors, organizational factors, lack of proper selection and knowledge and skills of employees), central phenomenon (future managers based on talent management and personality types), underlying conditions (organizational platform, selection of talents, use of talent) Intervening conditions (psychological factors, individual factors, managerial factors) and strategies (talent sourcing, empowering managers and employees, job and employee fit, succession planning, foresight, and cognitive strategy) and outcomes (organizational results, public satisfaction, futurization). In the quantitative part, the data obtained from the structural equation analysis questionnaire were analyzed using AMOS statistical software. Based on the outputs, the factor loadings of all the items of the standard model were higher than 0.3, and all the significant coefficients of the model were higher than 1.96.

Language:
English
Published:
International Journal of Knowledge Processing Studies, Volume:3 Issue: 2, Spring 2023
Pages:
26 to 38
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