Tag Archives: research

User Expectations, Prototypes, and Professionals

Back in the 1980s a Japanese professor by the name of Noriaki Kano developed a model of product development and what we today might call user experience. This model is now known as the Kano model after the eponymous professor. I’m not going to go into details on the model but one of the interesting aspects that it describes is the notion of “threshold attributes” which are basically the “must-have” features that a product needs to incorporate in order to meet customer needs. If a product hits the market and doesn’t have these features then users will not adopt the product because it provides a lousy or dissatisfying experience. Moreover, the model predicts that over time, more exciting and newer features become expected in subsequent generations of products.

I suspect that many of the high-profile usable products on the market (e.g. from the likes of Apple and Google) are raising the bar for what users expect in a new product. Can you imagine going from a retina display back to a low-res screen? And isn’t it annoying, just a little bit, when search suggestions don’t populate as you type? I was using Many Eyes earlier this week to do some visualization and was frustrated that the conventions and technologies with which that UI were built are now about 5 or 6 years old.

Essentially the bar is always going up in terms of what users expect. These features in turn put pressure on any prototype builder (including those at start-ups, or in academia) to meet those expectations. Basic usability (e.g. undo/redo, error handling, clear labeling), an aesthetic design, and solid information architecture and navigation seem to be givens. And Google has trained us all to expect low latency in a web-app, which can demand a lot of engineering and systems-building time. According to Kano, if a product doesn’t have these kinds of things we’re bound to notice and have a less awesome user experience.

If you assume that threshold attributes are indeed important (and a moving target), what does this mean in terms of getting prototypes and products built? I’ll address the academic space since that’s the realm I’m most familiar with. I think it’s particularly hard to provision threshold attributes in an academic setting because of (1) limited human resources (i.e. possibly just 1 graduate student for a year), (2) limited student expertise (i.e. in visual or interaction design or user research – they’re there to learn!), and (3) different incentives (i.e. novelty tends to get the emphasis which makes building new features more important than implementing threshold attributes to support a baseline user experience).

For a certain type of research I think it makes sense to organize the work such that a graduate student and faculty work more closely with professionals such as programmers or interaction designers and UX researchers. The extra polish and experience that professionals bring to the table would enable a prototype to reach those threshold attributes, while allowing researchers to focus on identifying / implementing new features to evaluate, or deciding on what data to gather and analyze during a deployment so that new knowledge is produced. You could imagine interaction designers or UX researchers being part-time on a number of projects, essentially becoming internal consultants on university projects. The types of prototypes produced might also have the added advantage of being further along the path to products and less “throw-away” thus helping the university-incubator. I think if universities really want to incubate prototypes and see those prototypes turning into products they need to reorganize work to include more professionals in the process. These professionals should not be paid for out of raw research budgets, but from university overhead.

People Scopes, Platforms, and Research

I recently finished reading Pasteur’s Quadrant. The gist of the book is that the author, Donald Stokes, argues that the traditional (back to the Greeks) distinction between basic research and applied research is misguided.  Louis Pasteur didn’t make that distinction. He in fact was very much driven to solve real-world problems whilst also pursuing basic scientific understanding of the phenomena that he observed. The book does a great job of explaining the historical antecedents of the basic-applied distinction in the modern research-industrial complex, and I would highly recommend it to other researchers.

Stokes defines basic research as “experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts” whereas applied research is concerned with “the elaboration and application of the known … to convert the possible into the actual, to demonstrate the feasibility of scientific or engineering development, to explore alternative routes and methods for achieving practical ends.” But he argues that this one dimensional dichotomy is too simple and that it should be expanded to a two dimensional typology with consideration of use on one axis, and fundamental understanding on the other. The quadrant of this typology that is concerned with fundamental understanding AND considerations of use is termed Pasteur’s quadrant, or alternately “use-inspired basic research.”

I reproduced a diagram from the book that illustrates the typology:

Use-inspired basic research can advance both fundamental knowledge as well as technology. Which is a good thing because new (or better) technology enables new scientific questions to be asked. And the answers to those scientific questions can often lead to better technology designs. The scanning electron microscope (SEM) is a good example.

I think it’s likely that many interdisciplinary fields thrive at this intersection of applied and basic research and Human Computer Interaction (HCI) is no exception. A lot of HCI research seeks to harness fundamental knowledge for the design of interactive systems but at the same time use new technologies and interfaces to ask fundamental questions about people and interfaces (though these two phases do not always occur simultaneously, nor must they). Basic findings can trickle back to core disciplines (e.g. psychology, sociology), and other findings from other core disciplines can inform the designs and the engineering that goes into building the next generation of interactive systems.

Take a simple new technology that has had a huge impact on computational social science research: Twitter. Twitter is the computational social science “scope” that lets researchers ask all kinds of interesting questions about social psychology. Refining such knowledge could lead to a newer social scope (Twitter 2.0?) that is even better. Another example is Digg.com, which a few years ago was a technology that helped advance our understanding of information novelty and decay.

Real people-scopes working in naturalistic settings are essential for basic research as well as for driving technology forward. Academia (not just in HCI, but in other interdisciplinary social sciences) needs to get more strategic about building people-scopes, basically platforms that enable new human-centered questions to be asked, at scale. Unfortunately academia is not traditionally good at platforms. Right now I can only think of a few academic projects that have done this successfully: Movie Lens at University of Minnesota, Scratch at MIT, and maybe IBM has also had some semi-successful ones.

There are likely a number of reasons why academia is not that good at platforms: (1) grad students may not be around long enough to grow and maintain the system, (2) the risk of failure is immense and too high for a pre-tenure faculty to bear, (3) there are not enough sustained resources to maintain the systems, and (4) there are little to no marketing resources to support the acquisition of users. So there are incentive as well as resource issues here.

It may be that start-ups are simply a better source of new social platforms, since the market can quickly winnow out the unsuccessful ones, and the risk is externalized. But I think it may also warrant thinking about how funding agencies like the NSF might better support (e.g. through sustained resources and incentives) the construction of the next generation of people-scopes.