Optimization Design of a Webpage Based on Kansei Engineering

Abstract

Webpage design has become an important component affecting user satisfaction when they surf the Internet. Interface designers are struggling to improve the quality of user experience by designing webpages that meet users’ emotional needs. An optimization design method of webpage interface is proposed in this study based on Kansei engineering theory, and a job‐hunting website homepage is taken as the research example. After determining the materials, the semantic differential (SD) method is used to extract user‐centered emotional dimensions, and the key design factors of homepages’ appearance are acquired. Next, based on the obtained semantic differential evaluation data, back propagation neural network (BPNN) is conducted to identify quantitative relations between key design factors and emotional dimensions. Finally, genetic algorithm (GA) is employed to search for a near‐optimal design. The proposed method is helpful to design webpages that can satisfy participants’ emotions. It can also be used in a variety of design cases.

Optimization of user experience in interaction design through a Taguchi‐based hybrid approach

“…Through the OA of the Taguchi method, the experimental samples were reduced from 1,458 to 18, which can make the overall UX tests cost decreased greatly. Compared with traditional optimizing methods—for example, the genetic algorithm that typically uses only the mean scale rating in the UX evaluation (Guo et al, )—the proposed method produces a more robust optimal design by using GRG that builds on the S/N ratios through which both the mean and the variance of the quality characteristics are considered. More important, on the basis of GRG or S/N ratios, the relationships between the performance characteristics and design patterns can be made evident, which can provide considerable insight for design.…”

“…It should be noted that the sample size of the participants was 42 in the case study, which supports the use of statistics and interpretation of quantitative data (Sauro & Lewis, 2016). Thus, the number of participants is adequate (Guo et al, 2016;S.-W. Hsiao et al, 2010S.-W. Hsiao et al, , 2016, and the interpretation of the results is reliable; however, if this approach is applied to the interactive product design of companies, the sample size is not sufficient, and more data should be used. Additionally, to measure the UX we adopted the questionnaire developed by Park et al (2013).…”

“…However, adopting UCD to derive the optimal product entails limitations such as high cost and time consuming (Zhou, Heesom, & Georgakis, 2007). To effectively derive the optimal design for interactive products, Guo, Liu, Cao, Liu, and Li (2016) proposed a method that used back-propagation neural network to build quantitative model between design patterns and performance characteristics and then used a genetic algorithm to search for an optimal design. They used questionnaires to collect evaluation data, adopting only the mean scale rating.…”

Web-based virtual learning environment (EmoViLe) with emotive interface feature

“…Webpage design has turn into a vital component affecting user satisfaction when they browse the internet [19]. Based on their research about optimization design of a webpage based on Kansei Engineering, nine key design factors and 13 sub factors were determined on job-hunting website homepage.…”

Uso Do Eye Tracking Para Obtenção De Medidas Quantitativas Em Testes De Usabilidade: Um Estudo Focado Na Medida Da Satisfação

“…Com relação ao uso do eye tracking para medição direta da satisfação, foi verificado que muitos artigos utilizam o eye tracking em testes de usabilidade apenas para medir eficiência e satisfação, ou outros dados relativos à cognição, interesse visual e atenção mas continuam usando questionários para medir a satisfação, especificamente (CASTILLA et al, 2015;BAHARUM & JAAFAR, 2014;CHENG & LIU, 2011;SI-MUNICH, ROBINS & KELLY, 2015;RENSHAW, STEVENS & DENTON, 2009;GUO et al, 2016). Alguns dos artigos buscam fazer algumas inferências a partir dos resultados do eye tracking referente a algumas medições mais subjetivas como engajamento Em alguns dos artigos pesquisados, existe uma preocupação em obter dados quantitativos para a medida da satisfação.…”