dissertation in pdf-format - Aalto-yliopisto

20 T. Heimonen and M. Virtanen
3.2.3 Maximum likelihood models
Littunen and Tohmo (2003) and Littunen and Virtanen (2006, 2009) used logistic RA
when investigating longitudinal data from 200 start-ups from the manufacturing and
business services sectors in 1990. These firms were followed up to 1997 and after seven
year development 86 respondents were found and responded in 1998. In Littunen and
Virtanen (2006, 2009), a similar definition as that given by Smallbone et al. (1995) was
applied, stating that growing firms should double their turnover during the period of
the study (>10% annual growth). The distribution of the data was even, since it included
43 observations in both growth and non-growth classes, which meant that
cross-validation was unnecessary. The majority of those factors that differentiated
growing ventures from non-growth companies were dependent on the strategic and
operative choices of the entrepreneur. Littunen and Virtanen’s (2009) model did not
include any statistically significant entrepreneur-specific variables. They concluded that
fit of the entrepreneur is necessary but not a sufficient condition for successful growth
performance. The classification rate of growth businesses was 92.1% and 86.1% for
non-growth firms, indicating that nine of the ten growing firms were correctly classified.
Littunen and Virtanen (2006, 2009) focused on growth issues and no direct link to
success was shown in their study.
In those studies which investigate growth and success simultaneously, the usual
measure of growth is related to financial performance. However, some success studies
use survival or growth of the firm as an indicator of successful performance. Veronique
et al. (2000) used growth in the number of employees as their success indicator. They
conducted stepwise logistic RA in order to identify the factors that had an impact on their
growth indicator. Veronique et al. (2000) drew conclusions about the size of the impact
of independent variables on the likelihood of hiring new employees. However, they did
not give any statistical data about classification, i.e., how well the predictor variables
classified the dependent variable in different categories. Moreover, the success indicator
used by Veronique et al. (2000) may have been biased. For example, Lussier and Corman
(1995) found that failed businesses had higher workforce education and did not have
problems in acquiring staff. This could be interpreted to mean that owners of failed
ventures take greater risks and are not cautious enough in recruiting personnel.
The resource-based view was used as a theoretical framework by Davidsson et al.
(2009); they examined the connection between profitability and growth using parametric
testing and logistic RA. Swedish data collected from the years 1997–2000 consisted of
1,470–1,482 SMEs and Australian data from the periods 1994–1995, 1995–1996 and
1997–1998 consisted of 3,488–3,717 observations. Davidsson et al. (2009) used sales
growth and return on assets (ROA) as measures of variables. Following the RBV, they
calculated both growth and profitability relative to other firms in the industry.
Steffens et al. (2009) studied performance configurations over time, seeking the
growth – profit relationship. Their longitudinal survey data included 2662 firms. Industry
medians were subtracted from growth and profit figures in order to adjust them for
industry variations. Steffens et al. (2009) used probit and ordered probit analysis to
examine the differences between growth and profit orientation of young and old firms.
Unlike many other empirical studies, including our own research, Steffens et al. (2009)
succeeded in using the potential of longitudinal data in probit analysis. The usual practice
is that even if the data has been gathered from a longer period, the time series properties
cannot be used in the analysis. In their dynamic analysis, Steffens et al. (2009) compared