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General Design Issues

Part of the International Studies in Entrepreneurship book series (ISEN, volume 5)

Summary and Conclusion

I have argued in this chapter that knowledge development in entrepreneurship benefits from different types of research—“qualitative” as well as “quantitative”, and laboratory research as well as studies that rely on data from the real setting. Preferably, these different types of research should be combined in comprehensive programs. At least, it would be to the advantage of knowledge development if the different forms of research informed and inspired one another, rather than different methodological camps or entrepreneurship researchers developing separate and noncommunicating discourses.

I have further argued that entrepreneurship research can, and should be conducted on different levels of analysis. However, in order to qualify as entrepreneurship research the study has to take new venturing on the studied level into explicit consideration. In empirical entrepreneurship research, the focal phenomenon should not be reduced to an assumption. Regardless of the level of analysis chosen, it is important that it be properly matched with the theory, as discussed in the previous chapter.

Because of the profile of my own expertise I have given more room here—or will at least do so elsewhere—to “quantitative” studies based on primary survey data or secondary data from available registers. Regarding such studies, I have advocated that they be given a longitudinal design, so that processes can be adequately studied. Preferably the data should also be concurrent rather than retrospective, so as to emphasize the foci on emergence, uncertainty and outcome variability, and avoid biases stemming from hindsight and selection of successful cases only.

Keywords

Real Setting Knowledge Development International Entrepreneurship Global Entrepreneurship Monitor Entrepreneurial Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science + Business Media, Inc. 2004

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