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34 T. Heimonen and M. Virtanen The second question was “what are the problems connected with different methods of analysing factors that distinguish high growth and HS SMEs and how does the selection of different analytical techniques affect the results?” In Appendix 3, we present characteristics of the statistical methods used. These characteristics include modelling criteria and assumptions, evaluation criteria for models and challenges in interpretation. In all the models – as in all the statistical model analysis – the specification of the model is the basic problem. Model specification affects the validity of the results and in that sense it is of the utmost importance for the robustness of the analysis. Operationalisation and measurement have an impact on reliability of the analysis. Selection of variables (Appendix 3) has an important role from the perspective of both validity and reliability. Skewed distributions may cause the most severe problems when using discriminant or LRA if cross validation is not used. In DA the overall classification without cross validation was 78% but correct classification of HGS was only 15%, whereas with cross validation the rates for both were 64%. In LRA without cross validation the overall classification rate was 80% but the correct classification was only 27% compared to 65% with cross-validation. Thus we can conclude that ignorance of a priori probabilities will reduce the performance of discriminant and logistic regression models and the results will be adversely affected. How could the robustness of the data, and methods used in the analysis be improved? In this paper we have explained the refinement of data and compared the results of different analyses. It can be concluded that, before using certain methods for analysing data, one should carefully investigate the characteristics of the data and be aware of the assumptions and aims of the selected methods of analysis. Moreover, it is appropriate to exploit statistical procedures that take account of the skewed data characteristics. This paper is inspired by our experiences of analysing growth, success and the development of growing and successful businesses in a regional context. In the course of the research process we have learned that there are more myths than realities in the world of HG and HS businesses. These myths are driven by the unconscious application of widely respected statistical methods to problems and contexts where their use is not necessarily appropriate. The same or even better results could be achieved by simple analysis of distributions and the application of univariate statistics. However, careful analysis of data and its refinement may improve our understanding of the parallel growth and success of businesses. Low and McMillan’s (1988) advice to select a proper framework and define the purpose of any analysis should be remembered. The purpose and framework of the study affects the methodological opportunities and selection of analysis. We have to acknowledge that the chosen framework in this study has not enhanced our aim to exploit a variety of methods, but we have learned through experience – through trial and error. This has been very valuable and we wish to share the experience to save our peers’ and students’ time and expenses. The limitation of this study was the use of cross-sectional analysis. Even if we have longitudinal data the selection of methods and the time span of the data do not support the use of longitudinal data. Siegel et al. (1993) stated that: “A longitudinal study that follows companies through defined stages of growth and focuses on the characteristics that set companies apart at different stages in their life cycle would greatly contribute to our ability to predict winners and losers at their inception”. Bygrave (2006) demands more field research, including descriptive and in-depth longitudinal case studies.
Characteristics of successful gazelles 35 This paper describes the development and learning process associated with a series of quantitative papers analysing the same basic dataset. In Virtanen and Heimonen (2011), we followed Bygrave’s (2006) advice and picked five HG and HS cases from Eastern Finland and analysed their development from inception to a HG period in 2002–2005. We also followed their development before and after the HG period. The main difference compared to quantitative analysis is the fact that strategic behaviour is really important for the high performance of a firm. Moreover, it seems that HG and high success firms pay a lot of attention to their profitability before their HGS period (Davidsson et al., 2009; Steffens et al., 2009). Since continuation of the HGS period is not guaranteed (Parker et al., 2010; Steffens et al. 2009), future research should focus on a period longer than three to five years and take into account of uncertainty and the discontinuity of variables. This probably necessitates a qualitative approach, in which behavioural and management issues could also be included. Acknowledgements Financial support from the Foundation for Economic Education and the Finnish Cultural Foundation is gratefully acknowledged. References Acs, Z.J., Parsons, W. and Tracy, S. (2008) High-Impact Firms: Gazelles Revisited, SBA, Office of Advocacy, Washington, USA. Almus, M. (2002) ‘What characterizes a fast-growth firm?’, Applied Economics, Vol. 34, No. 12, pp.1497–1508. Ardichvili, A., Cardozo, S., Harmon, S. and Vadakath, S. (1998) ‘Towards a theory of new venture growth’, Proceedings of the 1998 Babson Entrepreneurship Research Conference, Ghent, Belgium. Audretsch, D.B. (1995) ‘Firm profitability, growth, and innovation’, Review of Industrial Organization, Vol. 10, No. 5, pp.579–588. Balance Consulting Oy (2007) Data Base: ‘Success Firms in Provinces’, Eastern Finland and Uusimaa Region, Finland. Birch, D.L. (1979) The Job Generation Process, MIT, Cambridge, MA. Birch, D.L. (1987) Job Creation in America: How our Smallest Companies put the Most People to Work, Free Press, New York. Birley, S. and Weasthead, P. (1990) ‘Growth and performance contrasts between types of small firms’, Strategic Management Journal, Vol. 11, No. 7, pp.535–557. Bygrave, W.D. (2006) ‘The entrepreneurship paradigm (I) revisited’, in Neergaard, Helle and Ulhøi, John Parm (Eds.): Handbook of Qualitative Research Methods in Entrepreneurship, pp.17–48, Elgar, Cheltenham. Chandler, G.N. and Lyon, D.W. (2001) ‘Issues of research design and construct measurement in entrepreneurship research: the past decade’, Entrepreneurship, Theory and Practise, Vol. 25, No. 4, pp.101–112. Cooper, A.C. (1993) ‘Challenges in predicting new firm performance’, Journal of Business Venturing, Vol. 8, No. 3, pp.241–253. Cowling, M. (2004) ‘The growth-profit nexus’, Small Business Economics, Vol. 22, No. 1, pp.1–9.
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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
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Appendixes Overview: Appendix 1: Ke
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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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