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3 Research methodology 3.4 Models and operationalization of variables Quantitative models were applied to meet the research aims and address the research questions posed in Articles I, II and IV. Descriptive statistics, including distributions of the variables, univariate statistics and factor analysis, were also used in the quantitative analyses. In Article I, an LRA-model with a binary dependent variable combining innovation and growth was applied. The independent explanatory variables were location, branch of industry, size, age, success and public R&D funding. LRA-modelling was selected partly because it is appropriate for testing whether a firm belongs to a specific group characterised in at least two dimensions, and partly because LRA-models have fewer restrictive assumptions than regression analysis and discriminant analysis models. In this article, LRA-models are estimated first, ignoring a priori probabilities, then matched pair comparisons (cross-validation) are introduced. The innovation variable is the numbers of IPRs a company produced and owned during the period 1988-2005, while the growth variable refers to turnover growth during the period 2002-2005. The relationship between a company’s industrial sector and combined innovativeness and growth was analysed using univariate statistics, because of the small numbers of observations in some industrial sectors. All the other variables were included in the LRA-model. Several models, including RA-, DA- and LRA-models, were used in Article II to analyse the characteristics of high growth and highly successful firms. Growth of the firms was used as the dependent variable, and success as an independent variable in the RA-growth model, and vice versa in the RAsuccess model. In the DA- and LRA-models a binary variable intended to describe simultaneous growth and success of the firms was used as a dependent variable. Both DA- and LRA-models were estimated ignoring the a priori probabilities of firms displaying high growth and high success (Model 1) and then matched pair comparisons (cross-validation) were introduced (Model 2). In Article II explanatory variables included location, branch of industry, age, size, innovation activity and public R&D funding. The stepwise processing (conducted using SPSS-software) included some other explanatory variables and factors (growth in personnel, growth of the branch of industry, liquidity, profitability and solvency) identified by factor analysis. In the analyses presented in Article III, data acquired from detailed longitudinal case studies (2000-2009) were used. In Article IV mixed methods such as LRA-modelling and multiple case study methods were used to analyse three different data sets and address the research questions. The dependent variable in the LRA-model was the branch of industry, 42
43 3 Research methodology based on the national-level data, and the model was estimated using matched-pair comparison, acknowledging a priori probabilities. The explanatory variables included success and growth, size and age of the firm, innovativeness, public R&D funding, growth of the branch of industry and number of auxiliary business names. In the industry-level LRA-model the data for small KIBS firms located in Eastern Finland (North-Savo) were analysed. A dichotomous dependent variable for the model was constructed by classifying the firms into two groups, based on the effects of innovation activity of the firms on their turnover, as reported by key informants. CEOs of Group I and II firms stated that innovations, including renewal and development activities, accounted for more than and less than 25% of the increase in their turnover during the study period, respectively. Four variables were selected to measure the extent of innovation activities in the small KIBS firms: the rate of new product or service introduction, uniqueness of the new products or services, timeliness of new product introductions, and the success of new products or -services they launched. These variables were measured using 5-point Likert scales. Finally, in Article IV content analysis was applied to data obtained for the 12 T-KIBS case firms. Figure 4: Variables used to small KIBS firms according to the impact of innovations on their turnover growth (Siikonen et al., 2011) 3.5 Validity and reliability in mixed method studies Onwuegbuzie and Johnson (2006) stated that validity is not about singular truths and not limited only to quantitative measurements. Validity can have subjective, intersubjective and objective components and influences. The definitions of validity and reliability in quantitative research reveal two strands: validity is often understood to refer to the accuracy of the means of measurements, and whether the measurements really measure what they are intended to measure. Measurements obtained from a given instrument may be reliable, but not always valid, while others may be valid but not
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
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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
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Page 92 and 93: Appendixes Appendix 2 Construction
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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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