Development of Kansei-based Intelligent Decision Support System (KIDSS) for Quality Evaluation of a Biological Greening Material

Consumer satisfaction is one of the essential factors in quality evaluation of a biological greening material. It is relatively difficult to be elicited. This paper presents development of Kansei-based Intelligent Decision Support System (KIDSS) to elicit satisfaction. Elicitation was proposed by quantifying design attributes into Weighted Average Importance Index (WAII) and Consistency Index (CI). Attributes were selected using questionnaires and quantified using Kansei fitness function. The function was subsequently optimized by particle swarm optimization. As a case study, KIDSS was demonstrated on Sunagoke moss (Rhacomitrium japonicum). The result successfully indicated consistency of WAII and CI values regarding robustness. KIDSS was effectively validated in different consumer segments. WAII and CI are possible abstractive parameters to elicit satisfaction for quality evaluation.