dissertation in pdf-format - Aalto-yliopisto

2 Literature review and conceptual framework
characteristics of growing and successful firms and analysing the data.
Notably, Davidsson and Wiklund (2000), Delmar (1997) and Janssen
(2009) have pointed out that prior performance research lacks justification
of applied performance concepts, theory-driven choice of models and the
use of single variables, modification of dependent variables and chosen
time periods. Thus, methodological issues are likely to be major causes of
the limited theoretical development in performance studies (Janssen, 2009;
Shepherd and Wiklund, 2009), and (probably) the uncertainties or
conflicting findings regarding some important relationships.
At the beginning of the first decade of the 21st century considerable
methodological debate focused on operationalization and definitions of the
applied concepts, sampling strategies and data (Achtenhagen et al., 2010;
Almus, 2002; Birley et al., 1995; Bygrave, 2006; Delmar 1997; Janssen,
2009; Kiviluoto et al., 2011; Sexton et al., 2000; Davidsson and Wiklund
2000; Parker et al., 2010; Shepherd and Wiklund, 2009). However, despite
the extensive research efforts very few prior studies empirically analysed
whether success and growth are different or the same concepts. If these
concepts are different, what kinds of factors may affect both of them? Very
little is known about the applicability and possible problems of the most
frequently used statistical methods. These problems include difficulties in
gathering pertinent, high-quality data and the use of different, sometimes
inappropriate, analytical methods (Almus, 2002; Chandler and Lyon, 2001;
Delmar, 1997). The problems are particularly acute in multidimensional
analyses of performance, for example growth and success studies
(Davidsson et al., 2009).
Several factors may cause problems when using statistical methods,
especially in high growth and high success (HGS) studies. In some studies
exceptionally high values of the variables have been excluded (e.g. Siegel et
al., 1993), while in others these “super-high” values have been the focus of
the examination (Markman and Gartner, 2002). In addition, results
obtained through basic regression analyses may be distorted because the
basic assumptions regarding distributions and error terms may not be
valid. The basic assumptions made in multiple linear regression analysis are
that the relationships among the variables are linear, error terms are
normally distributed, estimates are unbiased, variances of the error terms
are equal (homoscedastic) and predictor variables are not correlated (i.e.,
there is no multicollinearity). These characteristics may not be valid for
data related to the growth and success of firms operating in entrepreneurial
markets. As Bygrave (2006) states, these markets continuously change and
are characterised by substantial uncertainty, instability and discontinuity.
In practice, distributions of variables related to high growth and high
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