Design theories provide explicit prescriptions, such as principles of form and function, for constructing an artifact that is designed to meet a set of defined requirements and solve a problem. Design theory generation is increasing in importance because of the increasing number and diversity of problems that require the participation and proactive involvement of academic researchers to build and test artifact-based solutions. However, we have little understanding of how design theories are generated. Drawing on key contributions by Herbert A. Simon, including the ideas of satisfice and bounded rationality and reviewing a large body of information systems and problem-solving literature, we develop a normative framework for proactive design theorizing based on the notion of heuristic theorizing. Heuristics are rules of thumb that provide a plausible aid in structuring the problem at hand or in searching for a satisficing artifact design. An example of a problem-structuring heuristic is problem decomposition and an example of an artifact design heuristic is analogical design. We define heuristic theorizing as the process of proactively generating design theory for prescriptive purposes from problem-solving experiences and prior theory by constantly iterating between the search for a satisficing problem solution, i.e., heuristic search, and the synthesis of new information that is generated during heuristic search, i.e., heuristic synthesis. Heuristic search involves alternating between structuring the problem at hand and generating new artifact design components, whereas heuristic synthesis involves different ways of thinking, including reflection and learning and forms of reasoning, that complement the use of heuristics for theorizing purposes. We illustrate the effectiveness of our heuristic theorizing framework through a detailed example of a multiyear design science research program in which we proactively generated a design theory for solving problems in the area of intelligent information management and so-called big data in the finance domain. We propose that heuristic theorizing is a useful alternative to established theorizing approaches, i.e., reasoning-based approaches. Heuristic theorizing is particularly relevant for proactive design theorizing, which emphasizes problem solving as being highly intertwined with theorizing, involves a greater variety of ways of thinking than other theorizing approaches, and assumes an engaged relationship between academics and practitioners.
