Therefore, a more structured KE methodology is proposed, which integrates the Kansei text mining process for robust service design. For example, the Kansei “happy” in a luxury hotel should ideally and directly be linked to happiness-related service attributes, e.g. First, it builds and presents an integrative framework of Kansei Engineering with online review mining for robust service design.
The objective was to reduce the complexity and uncertainty of the mapping between Kansei's response and visual impressions. The challenge in Kansei's research is to quantify these factors, with the ultimate goal of indexing images with the "inner impression" experienced by the viewer. Thus, the focus was on the viewer, not the image, and the similarity measure derived from Kansei indexing represents similarity in internal experience rather than visual similarity.
In essence, the proposed integrated model of the refined KE with an online review methodology for robust service design consists of 10 substeps. The Kansei models formed are then checked and confirmed with 'the true meaning of Kansei'.
Case study on zoo services
Kansei's linear models are then inverted regarding which service attributes are critical to particular Kansei. Service attributes based on text mining output take into account five dimensions of the SERVQUAL model, namely the TERRA (Tangible, Empathy, Reliability, Responsiveness and Assurance), which has then been validated with previous research. There were 19 final service attributes with Kano category, perception, expectation gap and satisfaction score.
Indifferent (I) refers to service characteristics that neither contribute to customer satisfaction nor cause dissatisfaction. This validation is essential to verify the appropriateness of Kansei's effect on certain service attributes. It combines these three methods with a linear regression analysis of the mean Kansei response and the mean perceived response of negative service attributes with Kan's A and O categories.
The linear model is derived from multiple linear regression analysis, which is used to predict the value of the dependent variable (i.e. Kansei response) based on the value of the independent variables (i.e. perception of zoo service attributes ' XYZ'). Based on previous studies (see Hartono and Tan 2011), service attributes that are potential to be pursued for continuous improvement will be captured.
Discussion
It is found in the outer and inner layers of customer needs, both verbal and non-verbal. What will be found in the innermost layer of customer needs refers to hidden/latent/unspoken needs. It is a great challenge for designers to accurately capture what lies in the inner customer's mind, even though the customers do not express it explicitly.
Kansei is considered to be very important as it is one of the kinds of literature that discusses customer feelings and emotions required for product design and development, service design and innovation, and understanding customers better for marketing purposes. In other words, what is expected, processed and perceived by the human Kansei will be translated into perceptual service design elements. Again, this will also be different when the service experience is intensively and comprehensively improved and different in relation to the function of time.
From the empirical study, ten clear Kansei words represented the emotional needs and most lasting impressions of customers in zoo services. More methods and approaches used in EC methodology are essential in assessing the current state of service experiences and the suitability and sustainability of proposed solutions. In other words, initial formalized solutions will be biased due to noise factors such as the dynamics of customer expectations, field conditions or equipment.
Here, in the zoo service experience, noise factors refer to, for example, the temperature and humidity of the park environment and the number of visitors. It is offered that the EC methodology engaged with sustainable solutions and the mining process is quite promising. Traditionally, "direction - clear road direction" as the most prioritized service attribute will be selected by a one-step solution, such as "providing visual-based information with less wording".
Here the 'provision of visually-based information with fewer words' will be explored and narrowed down to 2-level factors by the Taguchi method. In the artificial intelligence (AI) era these days, how to identify and generate the pattern of customer emotions through big data collected is a big challenge and opportunity. This is an area of concern and promise for researcher and practitioner in the field of human emotion-based service design, innovation and experience.
Conclusion and further research
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