dissertation in pdf-format - Aalto-yliopisto

Characteristics of successful gazelles 21
the kind of growth-profit development that different age categories exhibited during the
research period. Their conclusion was that both young and old firms were more likely to
be classified into a ‘star’ category as a result, first, of above-average profitability and then
because of growth, rather than by achieving growth first and expecting profitability later
(c.f. Davidsson et. al., 2009). One deficiency of the paper is that the evaluation of the
model and its statistical characteristics could be more comprehensive in order to provide
the reader more information about the reliability aspects.
Even though Parker et al. (2010) examined high-growth firms, i.e., gazelles, their
study also addressed aspects of success, since in their multinomial logit model they
compared the status of the firm in 2001 with the situation in 1996 when the interviews
about management strategies took place. They found that, in order to become or remain
large, the firms needed to follow two key strategies. Gazelles should invest in marketing
(via a marketing department) and have focused product strategies. This means that a main
product is a major contributor to sales. This confirms results showing that strategy can act
as a differentiator of growth firms (Littunen and Virtanen, 2009) and that focused
strategy is important in order to achieve fast growth (Virtanen and Heimonen, 2011).
Moreover, continuously growing gazelles are less likely to sell their shares to other
stakeholders (workforce, directors, venture capitalists). The analysis and construction of
Parker et al.’s (2010) model is thoroughly justified, including an evaluation of its
statistical performance. However, the small number of failed businesses and the number
of observations in different categories compared to the number of independent variables
were fairly low and thus the interpretation of coefficients is not straightforward.
Fabling and Grimes (2007) investigated which business practices set successful firms
apart from others. Their cross sectional data consisted of a survey of 3,000 firms from
New Zealand. Most questions in the survey were qualitative and thus they used mainly
binary data in their probit estimation. A self-reported measure of performance (business
results higher/lower than competitors) was used as a dependent variable and different
practices and characteristics connected with profitability, productivity and market share
as explanatory variables. They discovered that both internal and external features of the
firms were associated with business success. Statistically significant explanatory
variables were:
1 investment in up-to-date capital equipment
2 labour augmenting practices (incentives and HR practices)
3 R&D activities
4 market research.
Fabling and Grimes (2007) suggested factors that differentiate high performers from the
other groups. Their analysis was thoroughly justified including a careful explanation of
categorisation and estimation of results and a Kernel density function comparison of the
potential outcomes. However, they ignored the growth perspective; this constitutes the
other dimension of our study.
In this paper we follow quite closely the definitions of Parker et al. (2010). For fast
growth businesses we expect consistent HG throughout the whole study period. The
difference between our data and that of Parker et al. (2010) is that we are unable to