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3 Research methodology and the methods used for addressing them. Creswell (2006) pointed out that quantitative data consist of close-ended information, on (for instance) attitudes, behaviour and/or performance instruments. Analysis of quantitative data commonly involves statistical analyses of scores (numbers) collected by survey instruments and/or from public documents to answer research questions and/or test hypotheses. Qualitative data often contain open-ended information that the researcher collects through interviews with participants. Open-ended questions asked during these interviews allow the interviewees to provide answers in their own words, which may provide much richer information than closed questions. Qualitative data may also be collected by gathering documents from private or public sources, observing participants, collecting audio-visual materials such as interview-records and videotapes or artifacts. Analysis of the qualitative data often involves aggregating the words or images into information categories and presenting the diversity of items collected during data gathering process (Creswell, 2006). The data used in the studies underlying this dissertation were collected (then refined and restructured as required) using a purposeful sampling strategy (Miles and Huberman, 1994; Achtenhagen et al., 2010), from the following seven sources: 1. Accounting data for the years 2000-2009 from the extensive database stored in the Voitto+ CD-ROM (http://www.asiakastieto.fi/voitto), which includes financial statement data for around 200,000 Finnish companies (Suomen Asiakastieto Oy, 2006-2011). 2. The National Board of Patents and Registration of Finland, and the international registry of patent data esp@cenet (http://www.prh.fi/en.html). 3. Public R&D funding data from the Finnish Funding Agency for Technology and Innovation (http://www.tekes.fi). 4. Success index data for firms in Finnish provinces acquired from Balanced Consulting Ltd. (http://www.balanceconsulting.fi) (Appendix 2). 5. Data from surveys of 213 small knowledge intensive business service (KIBS) firms (NACE 72-74) located in Eastern Finland (North-Savo) 6. Data obtained from interviews of key informants of five HGS firms located in Eastern Finland 7. Data obtained from interviews of key informants of 12 smalltechnology based knowledge intensive business service (T-KIBS) firms located in Eastern Finland (North-Savo) 38
39 3 Research methodology From the Voitto+ CD-ROM database, 567 growing businesses were identified – 466 in urban and 101 in rural areas. In order to obtain robust results, the sample was carefully analysed to ensure that it reflected the success data provided by Balance Consulting Ltd. In order to focus on high growth and highly successful SMEs the data were refined as needed. Because the full dataset included some figures for micro businesses, all businesses that had fewer than 10 employees were excluded. All firms that had more than 250 employees were also excluded, since these included several multinationals and their subsidiaries. The final step was to exclude data for investment banks, finance and holding companies, which have disproportionately large assets compared to other companies with similar numbers of personnel. The full data set (n=567) was used when analysing branches of industry in Article IV. Altogether, 219 companies were eliminated prior to the analyses presented in Articles I and II. Thus, the refined data provided information regarding 348 growing SMEs (262 located in urban and 86 in rural areas) and were used in the analyses presented in Articles I and II. The growth and success data covered the period 2002-2005. Growth businesses were selected by applying a 10% threshold in annual turnover, corresponding to the definition of a growth company suggested by Smallbone et al. (1995), as one that doubles turnover within 10 years. High-growth firms (gazelles) were defined as those that increased turnover by at least 30% per year, and thus more than doubled their turnover during the examination period. The figures were not deflated, since the annual change in the consumer price index during the study period was less than 1%. Moreover, the closing month of the accounting period may vary among firms, which could skew the impact of the annual inflation rate as a deflator. Using these criteria, 273 non-high growth (NHG) and/or non-highly successful (NHS) and 75 high growth (HG) and high success (HS) businesses were identified. In the study reported in Article III five HGS firms were selected from the longitudinal data, which included 348 growing SMEs. Altogether this data set included information on 49 HGS firms, nine of which were located in rural areas (Eastern Finland). The growth and success of those nine firms exceeded the industry average in 2002-2005. Four were excluded because they represented branches of industry where they had monopoly power because of legislation (motor-vehicle inspection) or could cause some bias in employment and job creation (labour leasing). The remaining cases were classified as highly successful business if their sales increased by at least 20% on average and by at least 15% every year in the period 2002-2005, and their Success Index was at least 70 points out of the 100 maximum (Appendix 2).
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 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
Page 88 and 89: Appendixes profitability, liquidity
Page 90 and 91: Appendixes Appendix 1 Continued (Ke
Page 92 and 93: Appendixes Appendix 2 Construction
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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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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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