Sampling issues and eco-networks on innovation management, quantitative research studies challenges

Abstract

The study examines the implications of Statistical Analysis when used for analyzing complex systems and ecosystems, such as the ones that represent networks of innovation and entrepreneurship. There is a serious concern regarding the effectiveness of using Quantitative Research Tools when analyzing the behavior, decisions, motives and outcomes of special target groups, such as innovators and entrepreneurs, and of course there are critical differences between them as well. Quantitative research depends on a large degree to acceptance of a number of key hypotheses, such as linear relationships, or population distribution in preselected categories, e.g. Gaussian type Curves. More specifically critical decisions related to sample choice, selection of statistical tools and methods which will be used for further analysis and conclusion may lead to less accurate estimations, regarding the population. On the other hand, innovation as a concept, ecosystems of innovation and individual entrepreneurs appear to have certain characteristics that makes it a bit questionable whether their population can actually meet several key criteria or further evidence and further analysis (Theory of Networks and Graph Theory) indicates that a new set of methodologies can provide more accurate results and highlight different aspects of business reality, compared to findings derived from traditional quantitative methods. Therefore the study examines several sampling and methodology questions affecting the results of the traditional quantitative research and hypothesis testing formation and to examine evidence that may question traditional quantitative research methods and their findings in the area of innovation management. Findings will provide researchers, policy makers and members of the innovation ecosystem useful insights for further research and a basis for further decisions and policy formation.