The primary purpose of this study is to obtain results that can be generalized directly to a particular real-world situation, the phenomenon of rapid growth of firms in Norway. The goal of this research is to link the theoretical and empirical worlds. As researchers, we use theory to explain our results and the results to refine theory. According to Howe and Eisenhart (1990), the research questions should drive the research strategy rather than vice versa. Platt (1964, p. 348) warns about becoming “method-oriented” rather than “problem-oriented.” To obtain our goal, we can use different research strategies, like qualitative and quantitative research. Qualitative research is first and foremost used to identify, describe, and understand social phenomena. Such research is therefore relevant in order to gain insight into new or incompletely documented phenomena. Quantitative research tries to build or test theories
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through the use of data analyzed statistically and is therefore appropriate for studying problems based on defined problems and developed theory.
The phenomenon of RGFs was identified in the early 1990s, and we observe several early case studies based on qualitative methods. These first studies focused on the phenomenon rather than theory. As the phenomenon was described, more theories were applied and developed. However, the field is still young, and almost no research has been conducted in the Norwegian context. Therefore, I want to start with a descriptive analysis of RGFs in Norway compared with the rest of the population of firms. This study will be my basis for identifying the population of RGFs in the Norwegian population, which will be used as a basis for selecting RGFs in a follow-up study. The hypotheses are based on previous research and theories and are centered on the firms’ industrial and regional distribution, economic performance, and market dynamics. A descriptive analysis was chosen for several reasons: to compare central characteristics with other studies from other countries, to investigate whether firm performance or market dynamics are related to growth, and because of their relevance for the policy debate regarding growth. It would be almost impossible to conduct such an analysis based on qualitative research. The population is nearly 100,000 firms, and official register data are available.
Cook and Campell (1979) classify quantitative design into three main categories: the classical experiment, the quasi-experiment, and the non-experimental field study. The first is favorable for establishing causal relationships and testing theory. However, reproducing complex social events and relationships for treatment manipulation in a laboratory setting is costly and probably impossible to conduct when studying RGFs. The quasi-experiment is an experiment in a natural setting; however, it has quite similar limitations. Non-experimental or longitudinal design based on panel data and time series allows for observations from several periods. As such, it is possible to define two separate situations in time and statistically demonstrate that the cause precedes the effect. However, the available register data do not have all the variables needed to answer Q2 and Q3. The register data do not contain information on external relations or on internally developed capabilities and resources.
Therefore, another research strategy had to be explored—namely, correlation design.
Correlation does not prove causation. Even though social research aims to develop causal systems, causal order “can seldom be checked nonexperimentally” (Davis, 1985, p. 9).
Q2 hypothesizes about correlations between organizational capabilities (X) and use of external sources of information and knowledge (Y). In this case, however, it is difficult to decide if X comes before Y or vice versa. They might also influence each other. To establish
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the direction of causality, the cause must precede the effect (in time), or, as Davis put it: “after cannot cause before […] one-way arrows flow with time” (Davis, 1985, p. 11). Separating the cause and effects of organizational capabilities and external sources of information implies that we have to know the exact time when the capabilities are developed and when the external relation is established. The problem is that both capabilities and relations are dynamic; they are developed over time, with no definite start or end point. Even the birth date of the firm cannot be regarded as a start point. Actors in the firm can, for example, have experiences (capabilities) and networks (external relations) from other firms or situations before the new firm started up. Therefore, I seek to find statistical correlations between variables while being careful not to assume directions.
Regarding Q3, we have a slightly different situation. Here the effect is growth after the period of rapid growth. The question posed is what can explain a firm’s later growth. As such, we have a defined period, a start and end point for the dependent variable (2006–2009). In this case, I can use official register data as independent variables separated in time from the dependent variable. An equity ratio can measure the solidity of the firm, and a low equity ratio can indicate a higher level of debt. During the financial crisis, lending policy became more restrictive, and solid firms may have had better opportunities to finance later growth.
Similar arguments can be used for return on sales, an indication of profitability. By selecting figures from 2005, it is possible to test if solidity and profitability is related to firms’ later growth (2006–2009). According to the evolutionary perspective, RGFs may be trapped by structural and cultural inertia when the competitive environment changes. Small and young firms are therefore probably more able to respond to changes. On the other hand, large firms may have the necessary resources to implement changes. Therefore, we can use register data on age and size to test these relations.
Other data such as firm-developed capabilities, experience, and educational level are not available from official statistics and must be collected using a survey. Better structured and more efficient firms might be able to increase their effectiveness. Firms with intangible resources oriented towards innovation, and firms oriented towards international markets, might use their capabilities to diversify and grow. Firms with experienced managers or highly educated managers might have better knowledge of market dynamics and a better understanding of how to react to uphold their growth. Two problems arise regarding separating these variables in time: 1) It is very difficult to predict which firms are in the process of becoming an RGF. A survey of RGFs must therefore be retrospective. 2) To ensure that variables collected retrospectively are clearly separated in time from the dependent
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variable, we have to use a dependent variable from a period after the collection. In our case, that would be 2010–2014. However, my interest was in the first period after firms’ rapid growth, and especially the financial crisis, which appeared in 2009, not 2014. Therefore, I have to acknowledge that the variables collected in a survey will violate the requirement for establishing direction for causality.
When selecting a research strategy, one option would be to use only official register data to reduce design problems related to time and causality. If this solution is chosen, then new research questions must be developed. It is not possible to test all the hypotheses derived from research questions Q2 and Q3 based on available data. The other choice is to keep the questions and accept the problems related to causality. I kept the questions and developed a questionnaire and am using these data for Q2 and Q3. The descriptive analysis of Q1 is only based on register data. Register data are moreover added to the database of answers from the survey. This process will be explained soon. Problems related to validity, reliability, and generalizability will be discussed next.