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16 T. Heimonen and M. Virtanen
sufficient to differentiate between high or low success. In this study we use simultaneous
financial success (success index) as the measure of performance (Appendix 2).
3.1 Time span and the data
One of the most essential factors in analysing growth and success is the time period over
which they are measured. Growth usually implicitly means a change in the state of the
measure and it will be time dependent. Thus the analysis presupposes longitudinal data.
However, quite a large number of studies have been conducted using cross-sectional data.
Even though longitudinal data has been used in several studies, these have mainly
examined data from a three to five-year period (Delmar, 1997). There are a few
exceptions where the time span is longer. Acs et al. (2008) and Smallbone et al. (1995)
used data from a 12-year period. However, both studies relied on descriptive statistics as
the method of analysis. Delmar et al. (2003) used data from ten years when identifying
the typology of HG firms by means of cluster analysis. Littunen and Tohmo (2003) and
Littunen and Virtanen (2006, 2009) followed the same firms over an eight-year period
and used logistic regression when analysing the data.
Davidsson and Wiklund (2000) demanded more longitudinal research, a feature that
has been lacking in entrepreneurship research. Since growth is a dynamic phenomenon
this demand is justified. However, several studies that use longitudinal data – including
our own – finally analyse cross-sections, since the study periods are often too short to
allow time series analysis.
The majority of growth studies use annual data. The analysis of Weinzimmer et al.
(1998) was exceptional in the sense that they solved the problem of time periods that
were too short by using quarterly data. They studied the measurement of organisational
growth by using quarterly data from the years 1987–1991, using time series analysis to
estimate beta, so that size was regressed on time. However, some caveats should be
noted, especially because of the nature of the quarterly data. Even if it is possible to
control seasonality and carefully justify the use of deflators, these selections could be
criticised. It is difficult to find a single indicator to correct seasonality since many firms,
especially larger ones, will be multi-branch businesses. There are also huge differences
within the same industry depending on the markets and products and/or services offered.
Since the data points are the same for every business, deflating the growth measures by
the same deflator does not really improve the power of the analysis if different points of
time are not compared with each other. Moreover, the use of a GDP price deflator does
not reveal the branch-specific effects of cost changes. In a stable economy the price
changes between different quarters are probably small and so the robustness of the
analysis does not inevitably require the use of a deflator, which could even be biased.
Refinement of the data has mostly included using of logarithmic transformations in
order to control the huge variance associated with absolute figures. In some cases
exceptionally high values of the variables have been excluded (e.g., Siegel et al., 1993)
but in some other studies these ‘super-high’ values are the focus of the whole paper
(Markman and Gartner, 2002).
3.2 Methodology and methods of analysis
Chandler and Lyon (2001) reviewed 416 articles from the entrepreneurship literature,
asking whether the methodologies and measurements employed in entrepreneurship