Proceedings of the 3rd European Conference on Intellectual Capital

Table 7: Goodness <strong>of</strong> fit test α =20%
Lidia García-Zambrano et al.
Model Summary
Model R R squared R squared corrected Standard error <strong>of</strong> <strong>the</strong> estimate
0,462 0,213 0,184 0,54213
Predictor variables: (Constant, training costs accumulated log
The resulting model is as follows:
3
t t i
ε t
t
i 1
t
~
Log VI
⎛ LogGF ⎞
−
= 7,214 + 0,
171
(1 0,2i) +
LogINCV
⎜
⎜∑
−
LogINCV ⎟
⎝ =
⎠
Table 8: Impact <strong>of</strong> <strong>the</strong> investment in training on <strong>the</strong> future value <strong>of</strong> <strong>the</strong> intangibles, descriptive
statistics: α = 30%
Descriptive statistics
N Minimum Maximum Average Standard deviation
Intangible Value Log 09 35 8,41 10,78 9,4534 0,60011
Training costs accumulated log 35 6,71 11,23 8,8071 1,01161
Table 9: Impact <strong>of</strong> <strong>the</strong> investment in training on <strong>the</strong> future value <strong>of</strong> <strong>the</strong> intangibles, linear regression: α
= 30%
Coefficients
Model
Non-standardised
Coefficients
Standardised
coefficients
B Standard error Beta
t Sig.
Linear regression (Constant) 7,118 0,846 8,412 0,000
Training costs
accumulated log
Dependent variable: Intangible Value Log 09
Table 10: Goodness <strong>of</strong> fit test α =30%
0,267 0,096 0,472 2,779 0,010
Model Summary
Model R R squared R squared corrected Standard error <strong>of</strong> <strong>the</strong> estimate
0,466 0,217 0,188 0,54072
Predictor variables: (Constant, training costs accumulated log
The regression model resulting from <strong>the</strong> analysis is as follows:
3
t t i
ε t
t
i 1
t
~
Log VI
⎛ LogGF
⎞
−
= 7,118 + 0,
267
(1 0,30i) +
LogINCV
⎜
⎜∑
−
LogINCV ⎟
⎝ =
⎠
Except to case α =30%, total value <strong>of</strong> intangibles is less than accumulated expenses on training (table
2, 5 and 8). That seems mean a lack <strong>of</strong> efficiency <strong>of</strong> training investments. But we have to consider <strong>the</strong>
crisis period when stock market capitalization was low. We understand <strong>the</strong> comparative between
<strong>the</strong>se two variables is not suitable.
The beta <strong>of</strong> model for <strong>the</strong> three cases studied is positive and, at <strong>the</strong> same time, statistically significant
at 5%. This means that <strong>the</strong> investment in training positively affects <strong>the</strong> value <strong>of</strong> <strong>the</strong> intangibles <strong>of</strong> <strong>the</strong>
company. This result, set out in tables 3, 6 and 9, includes how investing in Human Capital has a
positive impact on <strong>the</strong> intangible value <strong>of</strong> <strong>the</strong> company, and, <strong>the</strong>refore means that <strong>the</strong> value <strong>of</strong> <strong>the</strong>
company increases. We consider also in tables 4, 7 and 10 <strong>the</strong> R 2 determination coefficient that
shows <strong>the</strong> goodness <strong>of</strong> <strong>the</strong> adjustment, according to <strong>the</strong> depreciation rate applied. In <strong>the</strong> three cases,
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