Open AccessThe development of Kansei-based mining model for robust service design
Author(s) -
Markus Hartono
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1072/1/012057
Subject(s) - kansei , kansei engineering , process (computing) , computer science , categorization , structuring , service (business) , fuzzy logic , artificial intelligence , product (mathematics) , meaning (existential) , data mining , human–computer interaction , mathematics , psychology , geometry , economy , finance , economics , psychotherapist , operating system
This study proposes a model which shows the importance of Kansei Engineering (KE) methodology in supporting the design of robust services. The KE methodology is enhanced by the Kansei-based mining process, SERVQUAL, and Kano categorization in order to conceptualize robust service design and development. Due to complexity and contextual based, Kansei words as emotion representative are usually formed in fuzzy and abstract terms. It may lead to ambiguous and unclear meaning. Mapping and structuring more representative Kansei is needed. Hence, through Kansei mining system, historical data includes customer Kansei feedbacks are critical. KE model incorporating Kansei mining process followed by expected contribution which captures more organized and captured Kansei of product and service experience is proposed and discussed.