Intellectual capital and market value: evidence from Jordan

  • Received September 7, 2019;
    Accepted October 21, 2019;
    Published November 12, 2019
  • Author(s)
  • DOI
  • Article Info
    Volume 16 2019, Issue #4, pp. 37-45
  • Cited by
    7 articles
  • Funding data
    Funder name: Amman Arab University, Jordan
    Funder identifier:
    Award numbers:

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

This research aims to apply the value-added intellectual coefficient (VAIC) model to test the impact of intellectual capital (IC) on market value of the Jordanian industrial firms. The research increases the awareness of the need for firms of all sizes to communicate and value their business beyond capturing numbers alone. The sample for this study is 73 Jordanian manufacturing shareholders companies during the period 2005–2017. The sample employed consists of 648 firm-year observations. Market value is measured using the market capitalization over the total assets. Valuation approaches are a challenging area created to enable the stakeholders, or outside parties, to put an economic value on a firm.
The IC and its components: capital employed (CEE), structural capital (SCE), and human capital (HCE) of industrial firms have been analyzed, and their impact on market value has been estimated using regression models. The results show that there is no relationship between IC and the market value; HCE is associated with the market value, and SCE and CEE are not associated with the market value. This could be explained by the increase in employees’ training, as a regular training program is an essential factor in managers’ and employees’ performance. Practically, investors have a positive view of a firm that has higher employee expenditure than its investment in physical capital. Future research should be made on the empirical analysis of other sectors to determine whether different results and explanations can be obtained.

view full abstract hide full abstract
    • Table 1. Descriptive measures
    • Table 2. Correlation matrix
    • Table 3. Hypothesis 1
    • Table 4. Hypothesis 2
    • Table 5. Hypothesis 3
    • Table 6. Hypothesis 4