One of the difficult parts in your design science research project is to figure out what is your research problems? And in particular, you have to find out if your top level researchable problem is a design research problems or a knowledge question. Design research problems are researchable problems that call for a change in the real world. They want something to be improved in the real world. And knowledge questions, they ask how the real world is, what phenomena exist, how they are produced and what their properties are. So for example, you may be asked how to build a ride hailing application for city taxis. That’s obviously a design research problems because this design, the ride hailing application is a change in the world and hopefully it will be an improvement. So this is, this is a researchable problem that calls for a change in the world. In that project, you may encounter a knowledge question, namely, what is the impact of ride hailing on travel behavior? So now, you have to study something, you have to observe what is happening in a city that is using a ride hailing application. So that would be a knowledge question. You’re asked how the real world is, what phenomena exist, how they are produced and what their properties are. So in design research problems, the solution to a design research problems is a specification how to do something. And this, in the example, is how to build a ride hailing app. And in a knowledge question, the answer is a proposition that you claim to be true. So if you find, for example, in the city that people travel more, when there is a ride hailing app, then that is a proposition that you claim to be true. Now, the solutions are the answers. They are evaluated on different kinds of criteria. So for design research problems, the solution is evaluated on utility and utility is goal contribution. So in the case of the ride hailing, there are stakeholders and stakeholders have goals. Actually in any design research problems there are stakeholders and any stakeholder will have goals. Now, in this example of the ride hailing app, there will be drivers. There will be riders who want to make a trip with a taxi. There are other participants in traffic, there are pedestrians. So there are all kinds of stakeholders and all of these stakeholders, they have goals. And you can evaluate the application on utility by trying to assess its contribution to those goals. There is always some imperfection in any solution. So, often actually I’d rather talk about a treatment than about a solution, but let’s just for simplicity, use the word solution here. So the solution will be imperfect, there’ll be limitations. It will make assumptions about the context. And in a context and an environment where these assumptions are not satisfied, you cannot guarantee that this solution will be any improvement, will be useful. And this also defines the limits of reusability of your application. In Any context in which these assumptions are satisfied, the application, the solution could be usable, and otherwise you cannot give any guarantees. So a ride hailing app may make an assumption about the number of taxis, the density of taxis in an area, it will make an assumption about distances. It will make assumptions about the structure of the road network, if it’s completely random, or if there are regularities and maybe other kinds of assumptions. And that’s, uh, those assumptions, to the extent that the … utility of the solution depends on those assumptions, these assumptions are important and this determines the extent of reusability. For knowledge questions the criterion is not utility. The answer to knowledge question could be totally useless or extremely useful. But this is not what it is evaluated on. It’s evaluated on truth. Is it true what you say. And together with your truth claim, there should always be an assessment of how certain you are of this claim. The degree of certainty, or if you wish the degree of uncertainty. And this may be qualitative, or you may even be able to quantify your degree of uncertainty. But whatever way you specify it, you should make clear on what empirical evidence you make your truth claim. So what is the evidence? What are the observations or the experiments? What are the facts on which you base your truth claim, your claim that the proposition is true. And then in science and design science and other kinds of sciences, you want your answer to be, your evidence to be replicable, to be repeatable. Other researchers, they should be able to make the same observations for researchable problem, do the same experiments, and then see the same results. And if your, question is an explanatory question, if you’re asking why something is the case, for example, why does route planning make people travel more frequently, if that would be the case, … so if I ask him why something is the case, there are always alternative explanations. There are no alternative facts, as we all know. So the concept of alternative fact is nonsense, uh, there is one state of the world. Of course it may be difficult for us to figure out exactly what is happening in our cases, but there’s one state of the world. This is maybe a philosophical simplification, but this is how we look at it. There are no alternative facts in researchable problem. Also not in politics, not in religion, not anywhere else. But there are possibly alternative explanations. Compare this to the doctor who … you enter the doctor’s office. You give … you describe your symptoms. The doctor does some observations, and those then are the facts of the case. The facts they should be agreed on by you and by the doctor and anybody else who would be in the room. And then the doctor tries to give a diagnosis. And the diagnosis is an explanation of your symptoms. It’s a theory about which disease you have. And typically there is more than one explanation. And if it is a very complicated disease, there may actually be, or a complicated set of symptoms, there may actually be many, many alternative diagnosis. So there are alternative explanations, which you have to discuss. And then also you have to try to order them on plausibility, or at least you should have an argument for which of these alternative explanations is the most plausible one. So that’s quite a bit of work. And just like you have in design research problems, there you had the reusability, here qe have the concept of generalizability. So in design science, in research, we want the answers to our knowledge questions to be generalizable. This has to do with the replicability. Other people in other contexts, or, in similar contexts, they make the same observations. Or the people doing similar experiments will see the same phenomena. And your explanations, of that you will claim that they have a certain generality. So you have to say something about your generalizability, and this is part of the evaluation of the truth claim of a proposition. So there’s quite a bit of difference between these two kinds of researchable problem. And then in all kinds of research problems, in design research problems and in knowledge questions, you will always encounter analytical research problems, simply by the fact that we are using language. People are language animals. So we have to use concepts. And because we are doing research, we actually have to define our concepts. And we have to analyze their implications and their conceptual relationships. So in the solution to a design research problems or answer to a knowledge question, there will always be some definitions and the discussion of their logical implications. And these definitions and implications, they will be evaluated on the quality of the definitions, about clarity and so on, the correctness of the proofs and the soundness of the arguments. So in the case of the ride hailing application, if you’re asking for the impact, then the question is, what is “impact”? How do you define impact? Is this about travel duration? Is it about the frequency of travel? Is it about the time of day at which people travel? And before you answer any questions you have to define, what is a taxi in this example/ And what is a city actually. Where does does the city end, what’s its boundary? And then because you’re defining an algorithm as a solution, you will probably have to say a little bit about the complexity of the algorithm. And you will have to make sure that it terminates in all cases. And complexity and termination, they are mathematical researchable problem. They are solved by analysis, by conceptual analysis. So we have three kinds of researchable problem. And in your research project, one of these three will be the top level one, most, probably a design research problems or a knowledge question. And this is important because we have three different methods to approach these research problems, to solve them. For the design research problems, we will have the design cycle, and I will have a few chapters about that. For the knowledge questions, we have an empirical cycle and for analytical research problems, we have conceptual analysis. So all of that seems quite simple. However, the thing is a bit more complicated because, all these research problems structures are nested. When you start with a design research problems, for example, and you decompose it, you will encounter a knowledge question. To solve a design research problems, you may want to investigate and analyze the researchable problem that you have to solve. Well, that is a knowledge question. And once you have proposed a solution, then you want to analyze or to know something about the solution. What is the performance? In which contexts does it work and doesn’t it work? And so, decomposing a design researchable problem, you will encounter knowledge questions. And then, decomposing a knowledge question you will encounter a design research problems, namely, you have to design your research. If you want to investigate, for example, what is the impact of, ride hailing on travel behavior in the city, now what do you have to do? You have to investigate … well, first of all, you have to select a city to study, for example. And then you have to figure out what am I going to observe here? Am I going to put some sensors in taxis? Am I going to put some some elements in the software to measure how often they are used? Do I have volunteers who want to participate in this research? Do I want to put cameras in the city or other kinds of measurements so that they can measure the impact on traffic flow? So there are many things you have to think about. Technical things, but also more the social science kind of thing.Like maybe you want to interview people. Or maybe you want to send out questionnaires. Then you have to design the interview or design and test also the questionnaire. So in general, to answer a knowledge question, you need to design your research. And the chapters about the empirical cycle in the book are very much about how to design your empirical research And whatever researchable problem you start with. you will always encounter analytical subresearch problems. You have to define your concepts, you have to analyze them, define their relationships, and also motivate their, let’s say the conceptual utility of your definitions for the design research problems and knowledge questions that you want to answer.
