To be published in Lecture Notes in Computer Science (LNSC) #7939 ©Springer, 2013 © Springer-Verlag Berlin Heidelberg 2013 Using Empirical Knowledge and Studies in the Frame of Design Science Research Ilia Bider 1,2 Paul Johannesson 1 , Erik Perjons 1 1 DSV, Stockholm University, Stockholm, Forum 100, SE-16440 Kista, Sweden 2 IbisSoft AB, Stockholm, Box 19567, SE-10432 Stockholm, Sweden ilia@<ibissoft.se|dsv.su.se>, <pajo,perjons>@dsv.su.se Abstract. The focus of this research in progress is relationships between De- sign Science Research (DSR) on one hand, and Empirical Research (ER) on the other. More specifically, it is devoted to investigating which tasks included in a DSR project should/could require conducting ER studies or using already exist- ing ER knowledge. The paper presents a methodology for enumerating DSR tasks and gives examples of logical analysis of some of them to determine re- quirements or usability of ER studies or ER-related knowledge for completing these tasks. The enumeration of DSR tasks is done by considering possible tra- jectories of DSR projects in a specially constructed state space. The latter con- sists of two subspaces; one is the space of specific situations, problems and so- lutions, the other – of generic situations, problems and solutions. The first sub- space represents test cases used for validating DSR hypotheses that the second subspace represents. In the terms of this space, DSR is considered to be a way of generating and testing hypotheses for future adoption. The project trajectory is identified via movements within and between subspaces. Examples of such movements are: generalization of a specific situation/problem, designing a ge- neric solution, evaluating the results of implementing a solution in a specific situation. Keywords: design science, empirical research 1 Introduction There is a substantial difference between qualitative and quantitative empirical re- search (ER) on one hand and design science research (DSR) on the other. The former [1] is aimed at investigating real life situations as-is, or as they were at some point in the past, in order to find commonalities between them that can give rise to a theory explaining the current or past state of affairs. DSR [2] is related to finding new gener- ic solutions for problems known or unknown [3]. Following [4], we see DSR as a tool for generating and testing solutions for future adoption. Despite the differences, both are needed for making progress in the field of Infor- mation Systems (IS). The goal of this paper is to investigate relationships and points of connection between DSR and ER. This work was inspired by S. Gregor’s presenta-