dissertation in pdf-format - Aalto-yliopisto

In the best statistical model the explanatory variables are success index, public R&D funding
and turnover/employee ratio (Table 2; Model 2). Of these variables, success index and public R&D
funding were statistically significant at the level of p .1 and p < .01. Thus these results support
acceptance of the fourth and fifth hypotheses. The turnover/employee ratio was not statistically
significant but it improved the overall classification rate.
All the explanatory variables in the best model (Table 2; Model 2) were continuous, meaning
that the signs of these coefficients may be used to interpret the direction of the impact of the variable
(Hoetker, 2007). It was found out that the worse the success index, the larger the probability of
being classified as an innovative growth SME. This means that these firms are probably less
successful than non-innovative SMEs. The direction of the impact is unexpected but this results
responds to the expected preconception of high variance in short-term income and profitability
(Bhide, 2000; Freel and Robson, 2004). The sign of turnover/employee ratio is positive, implying
that a higher turnover/employee ratio increases the odds ratio of being classified as an innovative
SME. This result is consistent with former results that fast growth businesses with higher
productivity are more IPR intensive (Coad and Rao, 2008).
Public R&D funding is a statistically significant variable in both the models (Table 2). This
means that a firm that receives public R&D funding will be more likely to generate IPRs. The result
is natural since precondition for granting public R&D funding to firms is the requirement of overall
innovativeness in R&D projects. One reason for the lower probability of successful businesses
belonging to the group of innovative firms may be a lag between R&D funding and the successful
commercialization and exploitation of IPRs. Thus, the benefits of R&D seem to ripen slowly. What
could explain the lower success of those firms that are IPR intensive? In the basic production
function an increase in capital intensity (inputs in IPR) decreases the need for employment. This
may increase the productivity of labour but decrease the productivity of capital. In this study, the
productivity of labour is measured by the turnover/employee ratio and the productivity of capital by
the return on investment, which is included in the success index. It could be deduced that higher
productivity may lead to lower success because success is a function of the productivity of capital.
However, this conclusion may be biased because of the measures of success and the indirect impact
of the productivity of capital.
In Model 2 the overall classification rate of the selected as well as the unselected cases is about
78%. In the group of selected cases around two-thirds of SMEs with IPRs were classified correctly.
The value of Nagelkerke R Square (0.297) and Hosmer and Lemeshow’s test statistics (0.192)
indicate that the model is applicable for finding out which factors differentiate innovative growth