dissertation in pdf-format - Aalto-yliopisto

Patents, trademarks, utility models and registered designs (IPRs) were used as proxies for
innovations. The IPRs are used as proxies because they are available for many countries and the
information is broken down into very detailed technological sectors. This information represents the
most precise and reliable classification in diverse branches of industry and the most precise and
reliable classification of commercial and technological output of innovation and R&D activities
(Archibugi, 1988). The prior innovation investigations that use IPRs include mainly patents in
focused branches of industries. This study uses broader definitions of IPRs. The possible problems
in the comparison of innovation and R&D activities in different branches of industry may be
decreased by following the practise of the OECD (2005) Oslo Manual and using broader definitions
of IPRs. The exclusion of micro and large firms clearly decreases the amount of firms with IPRs.
However, the inclusion of these categories would probably have increased the skewness of the
distribution towards micro firms since relative figures were used in measuring the growth of
turnover.
The definition of rural and urban areas follows the Nomenclature of Territorial Units for
Statistics (NUTS) criteria (http://ec.europa.eu/eurostat/ ramon/nuts/introduction_regions_en.html),
which are based on the population density. Eastern Finland is clearly a rural area since the
population density is 7.8 inhabitants/km². In the Helsinki region the population density counts 222.6
inhabitants/km² and thus it is considered an urban area.
Because the objective was to determine the factors differentiating innovative SMEs from other
firms, logistic regression analysis (LRA) was used to analyse the data. LRA-analysis instead of log
linear modelling is recommended when explanatory independent variables are not only nominal or
ordinal but also scale variables (Tansey et al., 1996). The dependent variable in the LRA model will
have a value 0 if the firm does not have IPRs and 1 if the firm has produced some IPRs. The
explanatory (independent) variables include location, age and size, success index and the amount of
public R&D funding received. In the LRA model it is always possible to achieve at least 50%
accuracy by simply setting the prediction for each observation to respond the most frequent outcome
(Hoetker, 2007). Thus, the model is not appropriate if it does not reach the level of a priori
probability. SPSS Inc.’s Predictive Analytics Software package was used in the statistical analysis.
Coad and Rao (2008) emphasized that in research sets that are related to the growth of a firm
and patent data the use of basic regression analysis (RA) may be misleading. This could be
explained through the assumption of regression analysis. In regression analysis normality of the
distribution and equality of the variance are the basic assumptions. However, the distributions of the
returns to innovation (IPRs) are highly skewed and the growth rates often heavily-tailed, too (Coad