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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 28
29 2 Literature review and conceptual framework success are often very heavily-tailed. Therefore, skewness of the data may result in constructed models having low explanatory power, and difficulties in calculating a priori probabilities of the characteristics of firms in a studied population may complicate the application or validity of diverse regression techniques (Tabachnik and Fidell, 2007). The lengths of time that businesses may exhibit HGS are also uncertain (Parker et al., 2010; Steffens et al., 2009). Therefore, one of the problems in analysing growth and success, in order to identify high growth and high success firms or evaluate the factors involved, is in determining an appropriate time period to consider. Growth and success occur in a constantly changing world (and change the state of the world), thus their rates will inevitably substantially change over time. Many growth and success studies have used cross-sectional (often annual) data sets, but this may not be appropriate for capturing important trends. Furthermore, even in studies that have analysed longitudinal data the timeframes have rarely exceeded 3-5 years (Delmar, 1997), the few exceptions include studies by Acs et al. (2008), Smallbone et al. (1995), Delmar et al. (2003) and Littunen and Virtanen (2009). There have been some attempts to solve the shortcomings of using crosssectional data by using quarterly data and selected deflators (e.g. Weinzimmer et al., 1998). However, this does not solve all the timespan problems. Even if it is possible to control seasonality and justify the deflators, these selections could be biased. The use of a GDP deflator, for instance, does not reveal the branch-specific effects of cost changes, and it is difficult to identify a robust single indicator to correct data for seasonality, since some firms (especially medium and large firms) will be multi-branched businesses. Moreover, there are differences even within the same industry depending on the products offered and accessed markets. In RA analyses it is difficult to operationalize any combined continuous variable intended to measure growth and success. Therefore, one solution for examining HGS by using RA is to construct separate regression functions, then test the dependent variables separately and analyse the possible similarities and differences of independent variables (e.g. Glancey, 1998; Markman and Gartner, 2002). Further, if we use discriminant analyses (DA) and/or other maximum likelihood methods, such as logistic regression analyses (LRA), the problem of explaining simultaneous high growth and high success demands the use of a dichotomous variable as a dependent variable (Tabachnik and Fidell, 2007). In DA and LRA analyses, distributions of the data and their characteristics should be taken into account. We should also acknowledge a priori probabilities in discriminant
Page 3 and 4: Aalto University publication series
Page 5: Author Tomi Heimonen Name of the do
Page 9 and 10: Acknowledgements This dissertation
Page 11 and 12: CONTENTS PART 1: Overview of the di
Page 13: List of Articles This thesis is bas
Page 16 and 17: 1 Introduction Some scholars claim
Page 18 and 19: 1 Introduction In addition, the dur
Page 20 and 21: 1 Introduction Figure 1: Framework
Page 22 and 23: 1 Introduction factors that affect
Page 24 and 25: 1 Introduction 1.4 Key concepts and
Page 26 and 27: 2 Literature review and conceptual
Page 46 and 47: 3 Research methodology Constructivi
Page 48 and 49: 3 Research methodology and the meth
Page 50 and 51: 3 Research methodology The data use
Page 52 and 53: 3 Research methodology 3.4 Models a
Page 54 and 55: 3 Research methodology reliable. Se
Page 56 and 57: 3 Research methodology concluded th
Page 58 and 59: 4 Results, evaluation of the studie
Page 70 and 71: References Abetti, P. A. (2001). Ac
Page 72 and 73: References Bhide, A. (2000). The or
Page 74 and 75: References Creswell, J. W. (2003).
Page 76 and 77: References Golafshani, N. (2003). U
Page 78 and 79: References Leitch, C., Hill, F., an
Page 80 and 81: References Pellikka, J. and Laurone
Page 82 and 83: References Storper, M. (1992). The
Page 84 and 85: Appendixes Overview: Appendix 1: Ke
Page 86 and 87: Appendixes High growth When examini
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Appendixes profitability, liquidity
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Appendixes Appendix 1 Continued (Ke
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Appendixes Appendix 2 Construction
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Appendixes Appendix 2 Continued (Co
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not increased compared to Eastern F
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propensity for risk taking (Covin a
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2. Former studies and derivation of
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firm’s annual sales growth (Arund
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H4. Firm success has an impact on t
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other branches in which employment
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and Rao, 2008). Because of the unev
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Table 1. Characteristics of the dat
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firms from other firms. The data in
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Table 2. Logistic Regression analys
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Public R&D funding was shown to inc
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References Achtenhagen, L., Naldi,
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Kanter, R. (1985), “Supporting in
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Appendix 1. Corporate population ch
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Appendix 3. Distribution of personn
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12 Int. J. Business and Globalisati
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14 T. Heimonen and M. Virtanen we u
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16 T. Heimonen and M. Virtanen suff
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18 T. Heimonen and M. Virtanen Glan
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20 T. Heimonen and M. Virtanen 3.2.
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28 T. Heimonen and M. Virtanen All
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412 M. Virtanen and T. Heimonen “
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424 M. Virtanen and T. Heimonen 4.6
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432 M. Virtanen and T. Heimonen Cha
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392 Int. J. Entrepreneurial Venturi
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394 J. Siikonen et al. firms’ gro
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396 J. Siikonen et al. generally re
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398 J. Siikonen et al. determinants
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400 J. Siikonen et al. 3.1 National
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402 J. Siikonen et al. 2 the firm
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404 J. Siikonen et al. 4 Results 4.
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406 J. Siikonen et al. innovation a
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408 J. Siikonen et al. the responde
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410 J. Siikonen et al. activities i
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412 J. Siikonen et al. Eastern Finl
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414 J. Siikonen et al. Delmar, F.,
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416 J. Siikonen et al. Oerlemans, L
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418 J. Siikonen et al. Appendix 1 V
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