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In a world that is increasingly and at times overwhelmingly reliant on data and research as the cornerstones of a sure future, designers are under ever-increasing pressure to justify their processes with research. The scientific method is comprised of a systematic approach to problem-solving (Vining, 2013). Design relies more on the state of wonder, the openness to learning new things that drive innovation (McAllister, 2014). Increasingly, contemporary studies are looking at the possible constraints that reliance on science and desire for certainty can place on all development and that of design in particular.
A few examples of when design exceeded scientific certainty can be found in the success of Airbnb, a platform through which people pay to stay in other peoples homes, and the phenomenal success of Apples iPhone (McAllister, 2014). Before Airbnb took off so hugely, the common observation of human behavior would in no way have suggested that people might pay for being allowed to stay in another persons or familys home. And indeed, based on pure fact and observational data on human behavior, it is challenging to see how the design was so successful. And yet, pure data cannot predict human behavior, and the concept took off hugely, revolutionizing the way we travel.
Before the iPhone and its touch screen completely revolutionized mobile phone technology, the widely accepted view, supported by data and thus possessing a scientific certainty, was that one of the most appealing and enduring features of the mobile phone was the physical keyboard. However, a forward-looking and innovative design disproved all assumptions and certainties by becoming phenomenally successful and overhauling an entire industry. Overall, then, we can see how at times, the creativity and sheer human ingenuity inherent in the design process have gone beyond the rigid limits of science and exceeded them.
References
McAllister, B. (2014). The allure of scientism. TEDxSMU. Web.
Vining, G. (2013). Technical Advice: Scientific Method and Approaches for Collecting Data. Quality Engineering, 25(2), 194201. Web.
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