Abstract
The designers usually apply various qualitative and quantitative techniques in investigating users’ desires. Once such techniques are applied, they deal with the difficulties of making the outcome data comprehensible enough to be used in the subsequent steps. This paper presents some encountered complexities of a pilot survey titled “Designing and Implementing an Artificial Design Tool Based on Improved Kansei Engineering”, in an effort to describe the borderline between quantitative and qualitative methods and their transformation point, and a practical solution suggested as co-qual-quant model. Through conducted case study, we have used qualitative methods on the human side (e.g. PPP and Mood Board) and quantitative techniques (e.g. AHP) on the other side and inferred some co-qual-quant model-based parameters to be further injected to the design machine.
Keywords
User-Centric Design, Qualitative Techniques In Design, Design Problem Solving, Kansei Engineering, Ahp In Kansei Engineering, Co-Qual-Quant Model In Ke
Citation
Ghadir, N., Garavand, D., and Faraji, A. (2010) Converting the Participants’ Verbal Expressions into Design Factors, in Durling, D., Bousbaci, R., Chen, L, Gauthier, P., Poldma, T., Roworth-Stokes, S. and Stolterman, E (eds.), Design and Complexity - DRS International Conference 2010, 7-9 July, Montreal, Canada. https://dl.designresearchsociety.org/drs-conference-papers/drs2010/researchpapers/45
Converting the Participants’ Verbal Expressions into Design Factors
The designers usually apply various qualitative and quantitative techniques in investigating users’ desires. Once such techniques are applied, they deal with the difficulties of making the outcome data comprehensible enough to be used in the subsequent steps. This paper presents some encountered complexities of a pilot survey titled “Designing and Implementing an Artificial Design Tool Based on Improved Kansei Engineering”, in an effort to describe the borderline between quantitative and qualitative methods and their transformation point, and a practical solution suggested as co-qual-quant model. Through conducted case study, we have used qualitative methods on the human side (e.g. PPP and Mood Board) and quantitative techniques (e.g. AHP) on the other side and inferred some co-qual-quant model-based parameters to be further injected to the design machine.