Understanding user needs and preferences is increasingly recognized as a critical component of early stage product development. The large-scale needfinding methods in this series of studies attempt to overcome shortcomings with existing methods, particularly in environments with limited user access. The three studies evaluated three specific types of stimuli to help users describe higher quantities of needs. Users were trained on need statements and then asked to enter as many need statements and optional background stories as possible. One or more stimulus types were presented, including prompts (a type of thought exercise), shared needs, and shared context images. Topics used were general household areas including cooking, cleaning, and trip planning. The results show that users can articulate a large number of needs unaided, and users consistently increased need quantity after viewing a stimulus. A final study collected 1735 needs statements and 1246 stories from 402 individuals in 24 hr. Shared needs and images significantly increased need quantity over other types. User experience (and not expertise) was a significant factor for increasing quantity, but may not warrant exclusive use of high-experience users in practice.
Large-Scale Needfinding: Methods of Increasing User-Generated Needs From Large Populations
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 11, 2014; final manuscript received February 13, 2015; published online May 19, 2015. Assoc. Editor: Carolyn Seepersad.
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Schaffhausen, C. R., and Kowalewski, T. M. (July 1, 2015). "Large-Scale Needfinding: Methods of Increasing User-Generated Needs From Large Populations." ASME. J. Mech. Des. July 2015; 137(7): 071403. https://doi.org/10.1115/1.4030161
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