A precurser to User Experience Fundamentals

We go through a lot of great experiences in life. And for some reason, we think our lives have become more fulfilling because of those expe...

We go through a lot of great experiences in life. And for some reason, we think our lives have become more fulfilling because of those experiences. This is especially true when we experience great products and services. The great experience lingers in our memories, and it drives us to use that product or service again and again. So when do we feel like we had a really great experience? What kind of changes take place in ourselves as a result of that experience? What are the conditions that need to be satisfied for a great experience?

user experience

Cases of Big Changes

“Professor Kim, I’m sorry to tell you that I won’t be making it to our dinner gathering tonight. I was looking forward to it, and I feel terrible for not being able to come.”

This was a message I received via a phone call from the design chief of the mobile design division of an electronics company in Korea in the spring of 2007. Since early 2000, I’ve been part of a monthly dinner gathering that brings together college professors and industry leaders who are involved in mobile phone design. The goal of this gathering wasn’t grand; it was an opportunity for like-minded people to get together and talk honestly about diverse topics while eating good food.

However, on that spring day in 2007, something didn’t feel right. Most of the regular attendees from the industry weren’t able to come to the meeting, so it was eventually cancelled. I started wondering what was going on. I asked the design chief of the electronics company if there was some huge crisis going on.

“The situation is pretty bad. I don’t remember the last time I went home. A few weeks ago, Apple released a new product called the iPhone and our executives are furious about it. This phone is something quite different, and they are pushing us to identify the iPhone’s strengths and weaknesses and how we can catch up. Honestly, I have no idea. There’s nothing to compare it to, our company is on the verge of a mental breakdown nowadays.”

That same week, I heard a similar story during a technology advisory meeting at an electronics company. As someone who researches Human-Computer Interaction (HCI), I attended a monthly meeting at that electronics company as an outside expert regarding how to apply new types of technologies into their business. That month, the agenda of the meeting involved a lot of condemnation such as: How could none of you think of making a phone like the iPhone? Why didn’t any of you predict that Apple was going to release a product like this? How could none of our technology advisors and engineers foresee and utilize this impactful new technology? As one of their technology advisors, I felt quite sorry and even shameful. Why couldn’t I, a so called HCI expert, get a grip on the release of such a product?

It’s not just physical products like the mobile phone that go through such big changes. Let’s take short messaging service (SMS) as an example. Even just 4 to 5 years ago, the influence of SMS was vast. Most mobile phone users made use of SMS almost as much as phone calls. Then out of the blue, mobile instant messaging (MIM) appeared and almost instantly replaced SMS as the to-go form of service for instant communication between users. Recently, Korean firm Kakao, which offers the KakaoTalk instant messaging service, merged with Daum Communications, Korea’s number two internet portal. This news caused quite a sensation since it was Kakao, a relatively new and young firm, which acquired Daum, a traditional portal giant. How could a venture firm that started out with a MIM service create so much influence as to swallow a veteran portal giant?

There are a lot of cases like this that take place so quickly that there just isn’t enough time to analyze them all. But what do those cases have in common in terms of the technology they represent?

Technology Cannot Explain Everything

When we merely look at the iPhone and the KakaoTalk service in terms of their technology, they do not offer new, innovative technological breakthroughs. But this was precisely why I couldn’t answer why I wasn’t able to predict this new technology. Neither the iPhone nor KakaoTalk place their technology as their main feature. In fact, the iPhone wasn’t evaluated very highly at all by technological experts when it first entered the market. KakaoTalk received similar evaluations. The users, however, perceived them as truly innovative.

The lack of technological innovation acts as two sides of a coin. Purely technological development can be predicted to a fair amount of accuracy, such as the speed of a semiconductor and the semiconductor industry can build new business models based on those predictions. However, it is extremely difficult to predict future results when users perceive and experience a product or service as an innovation while there is no clear technological advancement. Professor Clayton Christensen of Harvard University coined this type of technology, that brings rapid and radical changes to people, industry, and society, as “disruptive innovation” (Christensen 1997). But disruptive innovation focuses on technological development and application. Technology itself is not enough to explain the impact of the iPhone and KakaoTalk. So how can we try to understand the phenomena of the iPhone and KakaoTalk?

A Weather Forecasting Stone

The anxiety that uncertainty poses to IT firms is beyond mere worry; it’s better described as hysteria. The IT industry has far too often experienced the demise of a product or service that was at its peak. Top mobile manufacturers Nokia and Motorola gave way to Apple and Samsung, and heavy-weight TV manufacturers such as Sony and Mitsubishi are no longer appealing to consumers. These cases only exacerbate the anxiety that IT firms that are currently at the top of their game feel. Market research firms and the media take a jab at predicting next year’s hottest trends every year partly in order to lessen the anxiety of change. Topics such as what will replace the smartphone, when the high-definition curved TV display will launch, and the rise of the Internet of Things (IoT) are frequently discussed.

I think of an image in my mind every time I hear the latest predictions: a small rock to predict the weather. (http://en.wikipedia.org/wiki/Weather_rock). This rock was tied onto a rope and hung on a tree branch. No other tools were necessary. But its weather forecast accuracy is greater than that of any supercomputer today. This rock is based on a few principles: “If the rock is wet, then it is raining. If the rock is not wet, it is not raining. If there is a shadow under the rock, the sun is shining. If the top of the rock is white, it is snowing. If the rock is shaking violently up and down, there is a hurricane or an earthquake. And if the rock has disappeared, a tornado has passed.”

Isn’t it extremely accurate? However, I don’t need to explain why this weather rock is not useful. The weather rock merely portrays the current state of the weather; it does not predict the future. No matter how hard we try to accurately analyze what is happening in front of our eyes, there is a definitive limit to predicting the future. There is also no clear logic behind the current situation. Even firms that attempt to predict next year’s hot trends and technologies possess the same problems. It is hard to predict the future, and it is even harder to logically explain the reasons behind the current situation.

So Close, yet so Far Away: Technology Innovation Theory

Theories such as the technology innovation theory or technology acceptance theory take a different approach in predicting technology (Schumpeter 1942). Let’s take the well-known S-curve theory as an example (Henderson and Clark 1990). When a technology is first introduced into the market, its response is weak and growth is meager. But when it passes a certain point, the technology picks up dramatic growth. Then as time passes, growth stagnates. Another example is the technology acceptance theory (Rogers 2003). An innovative technology is first accepted by a
few selected people, followed by a majority of people. After a certain period of time passes, even people not familiar with this technology start accepting it. At this time, the technology starts to stagnate in the market.

From when they were introduced, numerous scholars have provided evidence that support the aforementioned theories. Additional theories that support these theories have also been developed and applied in numerous different fields. These theories also provide explanations as to why certain types of phenomena take place. For example, numerous products start out with a devoted group of users but end up not being accepted by the majority of users because of a “chasm” that exists between the two groups of users, and the technology was not able to successfully bridge that chasm.

However, these theories possess problems characteristically different to the problems that market research firms’ predictions possess. While it’s true that generally, technology has developed based on the principles of the theories, they aren’t as helpful in highlighting which specific part of the cycle a product or service is at and what kind of product or service should be made accordingly. While these theories are right in a broad perspective, it’s not clear now to apply them to a specific product or service. This is why it is difficult to specifically explain the rise of innovative products or services based on these theories.

Perhaps we need something that connects the short term trend predictions of market research firms and the broad perspective of the technology innovation theory in order to make a really great product or service. This something should not only be able to explain the reasons behind the rise of a currently trending product or service, it should also be able to predict which direction that product or service is heading towards. This something should also be able to provide reasons behind its prediction of the future based on a strong theoretical foundation. And if I were to be more ambitious about the existence of this something, it should also be easily understood and used by ordinary people.

I think a method based on a person’s experience can satisfy all of the above conditions. An approach based on the vivid experience of how a person feels and thinks while using a specific product or service is very concrete and specific. To add to that, theories regarding human experience provide fundamental bases on the why’s of experiences. Before I get into explaining the theory and approach based on human experience, I want to introduce a brief history of HCI, a field that emphasizes UX, especially on why the recent works in HCI focus on UX.



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