dissertation in pdf-format - Aalto-yliopisto

Characteristics of successful gazelles 23
deflator. Using this classification criterion we identified 500 growing firms – 412 from
urban (Helsinki region) and 88 from rural (Eastern Finland) areas.
In Heimonen and Virtanen (2007) the criterion for fast growth was more than 30%
growth in average annual sales for the period 2002–2005. This means that firms more
than doubled their turnover. Firms classified as successful businesses should be allocated
at least 80 points out of the maximum 100 points in the index used. We used the same
growth and success classification but combined the “Successful businesses in provinces
in Finland” data published in 2007 with the original data. Altogether we identified
466 and 101 growing businesses in urban and rural areas, respectively; thus the total size
of the sample was 567 businesses.
The data used in this study consist of 348 growing SMEs. The data were collected for
the period 2002–2005. As in our previous paper we selected growth businesses by using
10% annual growth in turnover to define growing firms and at least 30% annual growth
for a company to be considered a gazelle. The construction of the success index is
explained in Appendix 2. Balance Consulting Oy (2007) considers that those businesses
which exceed 70 points belong to the A-category. These firms were considered to be high
success businesses. Using this classification criterion we identified 273 non-high growth
(NHG) and/or non-highly successful (NHS) and 75 HG and HS businesses.
The definition of urban and rural areas follows the NUTS 1 criteria. We use the
density of population in relation to the density of population within the country as a
criterion to classify areas as urban or rural. We selected Eastern Finland as a rural area
and the Helsinki region as an urban area.
The basic assumptions, evaluation criteria and challenges in interpretation of different
methods used in the analysis are presented in Appendix 1. RA, logistic regression
analysis (LRA) and DA were used to examine the data. Growth and success of the firm
were used as dependent variables in the RA models. In the discriminant and logistic
regression analyses the classification of simultaneous growth and success of firms was
used as a dependent variable (1 = HG and HS; 0 = other combinations). First, both
discriminant and LRA are estimated ignoring the a priori probability (Model 1) and then
matched comparisons (cross validation) are introduced (Model 2). Explanatory
(independent) variables include age and size of the firm, branch of industry,
innovativeness (IPRs), and public R&D funding (Appendix 3). Moreover we use region
as an independent dummy variable in the growth and success estimations.
5 Models and results
5.1 Regression analysis
Even though regression analysis is frequently used in growth and success studies, its use
is problematic because of the nature of the growth and success as a dynamic change
phenomenon and its basic assumptions (Appendix 1). The basic assumptions of multiple
linear regression analysis, the approach most often used in such studies, are linearity,
normality of the distribution of the error term, unbiased estimates, equality of variances
of the error term (homoscedasticity) and an absence of correlation between predictor
variables (no multicollinearity).
Since the operationalisation of the combined continuous variable measuring both
growth and success would be difficult in a RA model, we decided to test the variables