Valuation of Human Resources with a Human Capability Development Approach: A Structural-Interpretive Model for the National Iranian Gas Company
Keywords:
Human resource valuation, human resource capabilities, human capital, National Iranian Gas CompanyAbstract
Modeling human resource valuation with a focus on the development of human capabilities offers a scientific solution for assessing and enhancing employee competencies to create sustainable value. This study aims to design a model for valuing human resources with an emphasis on the development of human capabilities in the National Iranian Gas Company. The research was conducted using a mixed-methods approach that combines qualitative and quantitative methods. In the qualitative phase, the statistical population consisted of 266 articles related to human resources in the oil industry, from which 46 articles were selected using the Sandelowski and Barroso method. In the quantitative phase, the statistical population included 40 individuals comprising professors, experts, managers, and specialists from the National Iranian Gas Company. From this population, 20 individuals were purposively selected, and 15 were chosen for a focus group. The validity of the discovered dimensions was confirmed using the Glyn tool and the CESP method, while reliability was assessed using Cronbach's alpha coefficient. Data analysis was conducted using both descriptive and inferential statistics. In this study, 240 indicators were identified within 54 components. After screening through the Delphi method, 238 indicators were retained. The human resource valuation model developed for the National Iranian Gas Company comprises 16 dimensions related to the development of human capabilities, which were delineated and categorized through focus group analysis. For the model design and its hierarchical structuring, the MICMAC software and the Structural-Interpretive Modeling (ISM) method were employed, resulting in a six-level output. This tool played a significant role in analyzing the relationships among variables and determining their hierarchical levels.
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