The impact of intellectual capital on company financial performance: Evidence from the Omani industrial sector

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The article aims to investigate, using the VAIC and MVAIC models, the impact of intellectual capital on the financial performance of Omani companies listed on the Muscat Stock Exchange from 2017 to 2021. Regression analysis revealed a significant positive influence of VAIC and MVAIC only on the Asset Turnover Ratio at a 10% significance level. This suggests that an increase in VAIC or MVAIC by one unit could lead to a respective increase in earnings for Omani listed industrial companies by 0.0017 and 0.0016. However, the overall impact of VAIC and MVAIC on financial performance appears limited, necessitating measures for enhanced efficacy. Moreover, company size and leverage were found to significantly influence EBITDA and Return on Assets, suggesting the positive effect of increased activity and resource utilization. Conversely, Return on Customer Equity negatively affected only Asset Turnover Ratio, implying that investments in marketing and advertising may not significantly enhance financial performance. Human Capital Efficiency showed no significant impact on financial performance measures, highlighting the necessity for Omani industrial enterprises to focus on enhancing employee skills and experience for improved value-creation processes. These findings underscore the intricate relationship between intellectual, physical, and financial capital in shaping financial performance, necessitating targeted strategies for enhancement. Further analysis of suggested models indicated the significance of company size on EBITDA, highlighting the importance of scaling activities for performance improvement. VAIC and MVAIC structural elements showed mixed results, while Capital Employed Efficiency negatively affected Return on Equity, Structural Capital Efficiency positively impacted EBITDA and Asset Turnover Ratio.

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    • Figure 1. Conceptual framework of the study
    • Table 1. Summary of all dependent and independent variables, calculation methods, and abbreviations used in the study
    • Table 2. Explanation of the selected variables and descriptive statistics (based on observations: 1:1 – 34:5)
    • Table 3. Correlation coefficients, based on observations 1:1 – 34:5
    • Table 4. Results of panel data estimate parameter selection for each of the models used in the study
    • Table A1. Analyzed PERF models (Pooled OLS, FEM, REM)
    • Conceptualization
      Serhii Lehenchuk, Dmytro Zakharov, Iryna Vyhivska
    • Formal Analysis
      Serhii Lehenchuk, Iryna Vyhivska
    • Investigation
      Serhii Lehenchuk, Dmytro Zakharov, Viktoriia Makarovych, Yaroslav Sheveria
    • Methodology
      Serhii Lehenchuk, Dmytro Zakharov
    • Project administration
      Serhii Lehenchuk, Dmytro Zakharov
    • Supervision
      Serhii Lehenchuk, Dmytro Zakharov
    • Validation
      Serhii Lehenchuk, Viktoriia Makarovych
    • Writing – original draft
      Serhii Lehenchuk, Dmytro Zakharov, Viktoriia Makarovych, Yaroslav Sheveria
    • Writing – review & editing
      Serhii Lehenchuk, Iryna Vyhivska
    • Data curation
      Dmytro Zakharov, Viktoriia Makarovych, Yaroslav Sheveria
    • Resources
      Dmytro Zakharov, Iryna Vyhivska, Viktoriia Makarovych
    • Software
      Dmytro Zakharov, Iryna Vyhivska, Yaroslav Sheveria
    • Visualization
      Dmytro Zakharov, Yaroslav Sheveria
    • Funding acquisition
      Iryna Vyhivska, Viktoriia Makarovych, Yaroslav Sheveria