Design Research, or the power of change(ing tiny details)

Réhman, S., Sun, J., Liu, L., & Li, H. (2008). Turn Your Mobile Into the Ball: Rendering Live Football Game Using Vibration

Media technologies is in my opinion best evaluated through user testing. Interface design and usability can be clinically tested in and evaluated in lab, but that controlled environment will not tell you anything about how the end user will view your product. Users see your work differently than you do, since they have no preconceived ideas about what will work (if you do your study correctly and manage not to influence them). As a researcher it’s easy to get stuck on thinking that the idea you have developed is the best way to achieve your goal, and it can be hard to see your own bias. It is always hard to predict how and what a person gets from an interaction until it’s done. This makes prototypes invaluable in our field of research, since you need something to evaluate and iterate upon. Especially when you are dealing with physical products there is no way to know how it will work until you have a prototype that can be tested. In the Réhman et al paper they show how vital it is to have a prototype, since before they developed one all their theory was just that, theory. Prototyping is the best way to convert theory into practice, and to get confirmation that the theory works in your context.

This also applies to a proof of concept prototype. If the goal of your research is to develop a product that will be operated by users, in the end you have to make something that can actually be used.  Before that point, no matter how many low fidelity prototypes you have, it is just speculation that your design will work as intended.

It might be tempting to work directly on your high fidelity prototype then, since that is in a way the end goal. However, that is mostly not the right way to go. This sort of research is dependent in iteration, and to iterate on a nearly finished product if often a waste of resources. Low fidelity prototypes allow for quicker changes with less effort. They are not a replacement for a proof of concept though, because as I said previously, you need to prove that your concept is valid and working as intended at some point.

A proof of concept is also in most cases the best way to communicate your results. User based research is hard to present in graphs if you don’t have a functional or nearly functional prototype

to test, since it’s by it’s very nature subjective and qualitative.

Finding design qualities in a tangible programming space - Fernaeus & Tholander

Differentiated Driving Range - Lundström

The empirical data in both of the concerning papers is test based. Both Fernaeus & Tholander and Lundström did their research by looking at existing technologies, and then developing their own and testing that in an environment similar to where it would be used in an actual case.

How then is this contributing to the pool of knowledge? I would argue that finding different and perhaps better ways of doing things is in itself a goal to be strived for, since otherwise we might still use the text based user interfaces of the computers early days. If iteration wasn’t considered to be a knowledge contribution the only things that would be counted would be to make new things. There would be no improvement on existing stuff, and that would be a shame.

There is a difference between designing for research and designing for production. When you design for production you need something that works. That is the end goal, and it is quite possible that when you find that something that achieves your goals you happily stop there. Designing for research on the other hand might mean iterating for the sake of iterating, to learn new things. You care about not just that it works, but how and why and why something else works better or worse. The end goal is not a well functioning design, although that might be a side goal, but to learn things about what you are studying.

All research is dependent on the context in which it has been made. While it might be easier to view a quantitative study on the mating habits of frogs as a more replicable study there must be space for the context to matter. Sure, design research is by it’s nature narrower in its applicable context than other types might be, but I think it’s a mistake to view it as therefore being always worth less.