Design science as experimental methodology in innovation and entrepreneurship research:A primer

As a research methodology, design science operates at the interface of creative design and explanatory science to create and test innovative solutions. Design Science (DS) methodologies have emerged in various disciplines such as information systems, operations management, innovation management, and entrepreneurship. A major source of inspiration for the rise of DS is Simon’s monograph (1969) "The Sciences of the Artificial." DS is a broadly applicable methodology because it can be applied to tangible artifacts (e.g. hardware and software), intangible artifacts (e.g. innovation processes and team collaboration), or combinations of the two (e.g. a system for creating deep-tech ventures). Moreover, it can draw on a diverse set of (semi)experimental and related methods for collecting and analysing (qualitative and/or quantitative) data, which also enables its application to innovation settings in which the number of observations and cases initially is rather small. DS therefore differs from action research by focusing on solutions as artifacts as well as adopting a broader and more flexible approach to data collection and analysis. DS also goes beyond merely problem-solving, because it draws on evidence-based protocols and also fuels theory development. This primer outlines what DS is, what it is good for, and how it can be used.