Kansei Engineering (KE) was established by Mitsuo Nagamachi (1995a). ‘Kansei’ is the Japanese word that covers several English meanings such as sensitivity, sense, sensibility, feeling, aesthetics, emotion, affection, and intuition. All these words are associated with mental responses to external stimuli, often summarily referred to as feelings. Idea and goal of KE is similar to pleasurable product design. According to Jordan (2000) KE roughly translates as ‘Pleasure Engineering’. KE helps the investigator to understand the relationship between formal property and experiential properties of a product. KE is also helpful for gaining insights about expectations of user benefits and expected product properties through these expected user benefits. According to Nagamachi (1995a, 1996), KE follows four basic steps. First step starts with collecting appropriate Kansei words or adjectives related to product of interest from user class. Second step involves establishment of correlation between product features/attributes and Kansei words. In third step, a data bank of these correlations is searched for Kansei words, these words intern represented with semantic differential scales, and, analyzed, typically using factor analysis to reduce there often large numbers to a manageable set of words. Fourth and last step follows evaluation of new design with potential users in terms of Kansei words to establish how close the tested product is to the ideal product.

Nagamachi (1995b) has described two different directional approaches/ flows of KE, these are ‘from design to diagnosis’ and ‘from context to design’. The first approach involves manipulation of individual attribute of a product in order to test users’ overall responses to the product. In the second approach, qualitative data about products are gathered via field observations and then establishment of relationships between formal properties of design and the user benefits associated with products. 

With the help of KE, products could be engineered by the designers based on semantic meanings of the products to improve sales, usability, and user’s satisfaction. However, KE cannot predict feelings of consumers directly but it is still helpful to explain socially constructed phenomena.