Explanation and Evaluation of the Intellectual Capital Model in the Tose’e Ta’avon Bank
Keywords:
Intellectual Capital, Human Capital, Structural Capital, Relational Capital, Innovation CapitalAbstract
Intellectual capital is one of the most important organizational assets, through which organizations can strengthen their position in today’s competitive environment and achieve leadership in innovation and value creation. Accordingly, the present study aims to design an optimal model of intellectual capital in the Tose’e Ta’avon Bank. This research is exploratory in purpose and survey-based in nature. In the qualitative phase, interviews were conducted with 25 experts to identify the main and sub-themes that define intellectual capital. In the quantitative phase, using random sampling and Cochran’s formula, 75 individuals were selected as the statistical sample. In this phase, various statistical tests were used to measure and examine the relationships between the main and sub-themes. Data analysis in the qualitative section was performed through thematic analysis, while in the quantitative section, it was carried out using structural equation modeling (SEM) with the aid of SPSS and LISREL software. Based on the results of the thematic analysis, intellectual capital consists of four main themes and forty-eight sub-themes. The findings of the structural equation modeling revealed that the impact coefficients of all questions and latent variables—corresponding to the four dimensions of the intellectual capital evaluation model: human capital, structural capital, relational capital, and innovation capital—were significant and exceeded 0.50. Moreover, the significance levels of these coefficients were greater than 1.96, indicating statistical validity. The intellectual capital evaluation model, as a reliable and sustainable instrument, can be effectively applied to enhance management performance in the Tose’e Ta’avon Bank. The results of the study demonstrate that the structural parameters of the intellectual capital model are statistically significant.
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Copyright (c) 2026 Shirzad Khezri , Jafar Beikzad, Gholamreza Rahimi, Nader Behlooli (Author)
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