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Our focus on external growth and related competence development as a process required observing and jointly examining a large number of variables that influence growth processes and, in particular, the complex relationships among them (Huber & Van de Ven, 1995). The heterogeneity of the phenomenon requires rich and deep descriptions aimed at assessing the abstractions and generalizations that can be meaningfully attempted (Davidsson, 2005, p. 56).

While the social sciences do not make “scientific discoveries” of the kind made in the natural sciences, the empirical patterns revealed in Tables 1a and b struck us as coming close to that. Consider especially the “organic as percent of total” columns. They show an astonishingly clear and strong relationship between the size class of firms and the proportion of total growth that is organic. The effect is actually so strong that large firms defined as “high growth” in terms of total employment growth actually shrink quite markedly in organic terms (cf. Davidsson, 2005, p. 153; Davidsson & Delmar, 2006).

The hallmark of the “European Approach to Entrepreneurship,” if there is such a thing, has been its diversity. European entrepreneurship research has been like Europe itself, a panoply of diverse ways of thinking, expressed in theories, methods, or research questions. Only when comparing European research to North American do observers find a semblance of commonality. For example, it appears that European researchers as a whole tend to use more fieldwork and qualitative analysis approaches than do their North American counterparts (e.g., Aldrich, 2000). However, this perhaps reflects a stronger academia-based and quantitative dominant research paradigm among North American researchers than among the diverse research traditions currently active in Europe.

The present study fills some of the void in the literature on the concept and phenomenon of innovation and imitation. Basing our arguments on features that the literature indicates are characteristics of innovation, we delimit the concepts of innovation and imitation. Using these features and considering a company’s degree of entrepreneurial and market orientation, we devise a typology of companies ranging from radical innovators to strict imitators. We also argue that some key factors and the choice of appropriate strategy determine whether innovative and imitative companies can obtain and maintain their sustainable competitive advantages.

How general can a “general” theory of entrepreneurship be? Abstraction is a necessity but is it possible to include venture opportunity variation in a general theory of entrepreneurship building on two contrasting perspectives such as equilibrium economics and disequilibrium economics. Two important boundaries need to be explicated. First, defining entrepreneurship as the creation of new economic activity includes both the creation of new means – ends (cf. Schumpeter, 1934) – as well as optimizing within known means – ends frameworks (cf. Kirzner, 1997). Second, such a theory includes an opportunity – actor nexus because it is the first tangible or intangible evidence of existing venture opportunities. Formal models of entrepreneurship often start with a person and at some point in time an exchange of persons with firms take place which is confusing because both levels of analysis and outcome are mixed with each other. Apparently, there is no such thing as entrepreneurship without actors, but if we want to create knowledge about the creation of economic activity, we need to frame our boundary around the nascent initiative instead of single actors and/or teams of actors because value can only be assessed in relation to the costs of services withdrawn. Analogous to this is, for example, the theory of firm and the theory of organizations with boundaries well beyond single actors or groups of actors. Another factor behind a venture-based theory of entrepreneurship comes from empirical evidence from the Swedish PSED, which suggests that approximately 16% (n=97) nascent entrepreneurs are exchanged during the start-up process. Formal models of entrepreneurship could therefore start with the nexus of venture opportunities and enterprising actors as suggested by Shane (2003) or with resources as suggested by Davidsson (2000) and progress forward in the entrepreneurial process. Entrepreneurship models built around the economic activity itself needs to be dynamic allowing different outcomes and feedback loops because resource combinations alter our perception of value and diffuses information, which may lead to additional resource combinations (Hayek, 1945).

Following an alarm signal from a control chart it may be necessary to conduct an adjustment to bring the process mean towards a target value. Addresses the problem of how such an adjustment should be made, based on the use of an EWMA control chart, including the special case in which the EWMA is equivalent to the Shewhart chart. The distribution of the EWMA statistic, at the occurrence of an alarm signal, is approximated by applying a Markov chain approach. This distribution is used to obtain a maximum likelihood estimator of the process mean. Adjustment strategies are defined based on the EWMA statistic, the ML‐estimator and a modification of the ML‐estimator. Compares the performance of these adjustment strategies in a simulation study, although the Shewhart case allowed for a simplified treatment requiring no simulation. Results indicate that adjustment based on the ML‐estimator provides substantial improvement over the EWMA statistic when the smoothing constant of the control chart is moderate or high. When the smoothing constant is given a very low value the EWMA statistic provides the preferable adjustment strategy. Differences between control chart designs were less substantial than the differences between adjustment strategies for a given control chart.

We argue that entrepreneurship research should use meta-analysis to integrate the findings of the field. A meta-analytical approach has several advantages as compared with narrative reviews: First, narrative reviews are likely to bias empirical evidence because they are limited by the information-processing capacities of the reviewers (Tett, Jackson, & Rothstein, 1991). This is often a downward bias leading to the conclusion of little positive knowledge in the field. For example, frequency counts of significant results ignore sampling errors of individual studies, reliability problems of instruments, range restrictions of samples, dichotomization of continuous variables, imperfect construct validity, and extraneous factors (Hunter & Schmidt, 2004). These issues usually result in a higher incidence of Type II errors (i.e., rejecting the hypothesis wrongly). Thus, narrative reviews are more likely to lead to the conclusion that there are no relationships between independent and dependent variables in entrepreneurship when in fact they are (Hunter & Schmidt, 1990; Tett et al., 1991). Second, meta-analysis accumulates studies based on a set of explicit decision rules and, therefore, is less biased by subjective perceptions of the reviewer than narrative reviews. Meta-analyses require judgments as well, e.g., when defining the area of the study or coding moderator variables. However, the decisions are public and open to criticism and replication by other scientists (Johnson & Eagly, 2000). Third, meta-analysis is based on many studies and, thus, avoids the influence of single studies. Fourth, meta-analysis controls for sampling error variance and, thus, controls for power deficits of individual studies (Hunter & Schmidt, 2004). For example, the Brockhaus and Nord (1979) study is frequently cited in the entrepreneurship literature for providing evidence that there is no relationship of personality characteristics with entrepreneurship. However, this study is based on a small sample of 31 business owners and therefore, has serious statistical power problems. Noteworthy, the effect sizes of small samples are less precise in estimating a population value than effect sizes of larger samples. Fifth, meta-analyses can correct many errors of individual studies (Hunter & Schmidt, 2004). Since meta-analyses estimate population correlations between given variables, it is important to correct for errors of studies (e.g., unreliability, range restriction, and sampling error) to achieve unbiased estimates. Sixth, meta-analysis allows an assessment of the magnitude of relationships and, thus, provides more precise and often comparable assessments of the validity of concepts. Thus, meta-analyses support the assessment of the practical significance of findings. Seventh, meta-analysis tests for variations in relationships across studies and, therefore, allows an assessment of the generalizeability of effects. If the size of reported relationships varies considerably between different studies, there will be context conditions that account for these variations. These context conditions are moderators that affect the size of relationships. The moderators may include study characteristics, method moderators, and theoretical moderators. Thus, meta-analyses also help to identify areas for new studies. Finally, meta-analysis techniques allow to test more than one independent and/or moderator variable by using methods based on regression analysis (Lipsey & Wilson, 2001). Using such procedures allows to estimate the independent contribution of variables on results, to control for methodological variables, and to test the interactions between moderator variables.

The high-growth potential has long been the dominant view on RBSUs among researchers and policy makers. Several researchers indicate that RBSUs, once they have reached a certain critical mass, exhibit faster average employment growth rates than non-high-tech starters (Mustar, 1995; Licht & Nerlinger, 1998; Storey & Tether, 1998; Delapierre, Madeuf, & Savoy, 1998; Autio & Parhankangas, 1998). However, in recent years several researchers showed that the idea of fast growth does not hold for most RBSUs. Rickne and Jacobsson (1999) found that the vast majority of new technology-based firms (NTBFs) remained very small. Also Autio and Yli-Renko (1998) reported that most NTBFs in Finland did not grow at all. Similar findings were reported in France (Mustar, 1997), Italy (Chiesa & Piccaluga, 2000) and in Cambridge, UK (Segal Quince Wicksteed, 2000). Delappiere et al. (1998) further argue that high-tech firms that concentrate on R&D and work primarily as research subcontractors for large groups show little employment growth. In contrast, firms that deal with turning technology into new uses tend to grow and create employment as they develop their manufacturing and marketing capabilities. Clearly, there is still much discussion and uncertainty regarding the growth potential of RBSUs.