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18 T. Heimonen and M. Virtanen Glancey (1998) used OLS and 2SLS when investigating the determination of growth and profitability in small manufacturing firms in Scotland. The complete dataset consisted of 38 small firms (
Characteristics of successful gazelles 19 explanatory variables for profit. He used OLS and 2SLS estimation when analysing the data (N= 204–256). Cowling’s (2004) analysis supports the growth-profit-growth nexus which means that firms use profits for internal investment purposes but there is no growth-profit trade-off. An inverse relationship between profits and the age of the firm can be challenged since, for example, Audretsch (1995) suggested that growth generates more innovations in low-technological industries and these firms are more mature than high-technological industries. If we assume that innovations generate profits the result is the opposite of Cowling’s conclusion. The major problem with Cowling’s (2004) paper is the reporting of statistical results. The majority of the evaluation criteria are presented in tables but no interpretation of the goodness of fit or other evaluation criteria are explained in the text. 3.2.2 Discriminant analysis Siegel et al. (1993) analysed the characteristics which differentiate high-growth ventures from low-growth companies by using the so-called Reynolds data (smaller, younger firms) and Price Waterhouse data (mature, medium-sized firms). They defined high-growth as more than 25% annual growth for at least three consecutive years. Siegel et al. (1993) also refined the data by excluding exceptionally high-growth businesses as outliers. Their analyses distinguish industry experience as a differentiating factor in both samples. The Reynolds data showed that the highgrowth companies were more focused and more revenue was generated by a single product than in their low-growth counterparts. Moreover, smaller companies used scarcer resources, were more productive and reported a greater use of advanced technology. Larger companies (PW data) had a higher propensity for market and product diversification, balanced management teams, more rapid market growth and the ability to develop close customer contacts. In Siegel et al. (1993) the correct classification rates were high (around 80%) and thus the predictive power was good. They did not have serious problems with the skewness of the distribution even though a priori probabilities of the other Reynolds database were not equal for both small, high-growth and large, low-growth groups. Ettlinger and Tufford (1996) used DA when studying the performance of small firms in a local context. Their sample consisted of 63 manufacturing firms which employed fewer than 100 employees in Columbus, Ohio. The study by Ettlinger and Tufford (1996) analysed static and dynamic value added and sales per employee (labour productivity). They concluded that high performing firms invest more in labour than in capital, but most firms invest more in capital than labour. Discriminant functions and statistical parameters were well justified and reported, but the full potential for evaluating the models was not exploited. For example, a priori probabilities, cross-validation and classification rates were not reported. Moreno and Casillas (2007) used DA and a dichotomic dependent variable in differentiating high-growth firms from non-high-growth firms. They defined a firm as HG if its percentage of growth in the period 1998–2001 was more than 100% higher than the median of its branch of industry. Moreno and Casillas (2007) found that, according to their definition, more than 10% of the firms studied were high-growth businesses. They solved the problem of uneven distribution by dividing the non-high-growth businesses into nine groups of equal size. Even if this separation of data solves the problem of uneven distribution it is possible that randomly selecting the same number of non-high-growth and high-growth firms could have influenced the results.
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Aalto University publication series
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Author Tomi Heimonen Name of the do
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Acknowledgements This dissertation
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CONTENTS PART 1: Overview of the di
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List of Articles This thesis is bas
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1 Introduction Some scholars claim
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1 Introduction In addition, the dur
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1 Introduction Figure 1: Framework
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1 Introduction factors that affect
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1 Introduction 1.4 Key concepts and
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2 Literature review and conceptual
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3 Research methodology Constructivi
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3 Research methodology and the meth
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3 Research methodology The data use
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3 Research methodology 3.4 Models a
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3 Research methodology reliable. Se
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3 Research methodology concluded th
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4 Results, evaluation of the studie
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References Abetti, P. A. (2001). Ac
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References Bhide, A. (2000). The or
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References Creswell, J. W. (2003).
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References Golafshani, N. (2003). U
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References Leitch, C., Hill, F., an
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References Pellikka, J. and Laurone
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References Storper, M. (1992). The
Page 84 and 85: Appendixes Overview: Appendix 1: Ke
Page 86 and 87: Appendixes High growth When examini
Page 88 and 89: Appendixes profitability, liquidity
Page 90 and 91: Appendixes Appendix 1 Continued (Ke
Page 92 and 93: Appendixes Appendix 2 Construction
Page 94 and 95: Appendixes Appendix 2 Continued (Co
Page 96 and 97: not increased compared to Eastern F
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Page 100 and 101: propensity for risk taking (Covin a
Page 102 and 103: 2. Former studies and derivation of
Page 104 and 105: firm’s annual sales growth (Arund
Page 106 and 107: H4. Firm success has an impact on t
Page 108 and 109: other branches in which employment
Page 110 and 111: and Rao, 2008). Because of the unev
Page 112 and 113: Table 1. Characteristics of the dat
Page 114 and 115: firms from other firms. The data in
Page 116 and 117: Table 2. Logistic Regression analys
Page 118 and 119: Public R&D funding was shown to inc
Page 120 and 121: References Achtenhagen, L., Naldi,
Page 122 and 123: Kanter, R. (1985), “Supporting in
Page 124 and 125: Appendix 1. Corporate population ch
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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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