The nexus between corporate governance, asset structure, and value of listed firms: evidence from Kenya

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Shareholders of listed firms are guaranteed reasonable security prices due to enhanced firm value, which translates to global wealth creation. However, firms’ value has declined globally. Therefore, this paper uses a causal-comparative design and panel data regression model to explore the nexus between corporate governance, asset structure, and value of Kenyan-listed firms from 2010 to 2019. Secondary data were extricated from audited financial reports of 51 firms. As hypothesized, the results show a positive relationship between board composition and firm value with a regression coefficient (0.17, p < .05). The composition of the audit committee is positively associated with firm value with a regression coefficient of (0.629, p < .05). A tangible and notable correlation exists between protecting shareholders’ rights and firm value with a regression coefficient of (0.28, p < .05), while financial disclosure was significant with a regression coefficient of (1.15, p < .05). Plant, property and equipment positively and significantly affect firm value with a regression coefficient of (2.10, p < .05), while financial assets had (0.28, p < .05), which was significant. Current assets positively and significantly affect firm value with a regression coefficient of (1.87, p < .05). Finally, the results reveal a positive but insignificant correlation between firm size and value with a regression coefficient of (0.22, p < .05), while the relationship between firm age and value is negative but insignificant with a regression coefficient of (–0.003, p < .05). The study recommends that sufficient managerial effort be directed towards corporate governance and asset structure to maximize shareholder value.

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    • Table 1. Stationarity test results
    • Table 2. Normality test results
    • Table 3. Heteroscedasticity test results
    • Table 4. Variance inflation factor test results
    • Table 5. Autocorrelation test results
    • Table 6. Model specification test results
    • Table 7. Data characteristics
    • Table 8. Correction matrix
    • Table 9. Goodness of fit of the model
    • Table 10. Independent variables and dependent variables: Individual significance level of the variables
    • Table 11. Goodness of fit of the model
    • Conceptualization
      Barine Nkonge Habakkuk
    • Data curation
      Barine Nkonge Habakkuk
    • Formal Analysis
      Barine Nkonge Habakkuk
    • Funding acquisition
      Barine Nkonge Habakkuk
    • Investigation
      Barine Nkonge Habakkuk
    • Methodology
      Barine Nkonge Habakkuk
    • Project administration
      Barine Nkonge Habakkuk
    • Resources
      Barine Nkonge Habakkuk
    • Software
      Barine Nkonge Habakkuk, Kariuki Samuel Nduati, Kariuki Peter Wang’ombe
    • Validation
      Barine Nkonge Habakkuk, Kariuki Samuel Nduati, Kariuki Peter Wang’ombe
    • Visualization
      Barine Nkonge Habakkuk, Kariuki Samuel Nduati, Kariuki Peter Wang’ombe
    • Writing – original draft
      Barine Nkonge Habakkuk
    • Writing – review & editing
      Barine Nkonge Habakkuk, Kariuki Samuel Nduati, Kariuki Peter Wang’ombe
    • Supervision
      Kariuki Samuel Nduati, Kariuki Peter Wang’ombe