University website design with multivariate statistical techniques in kansei engineering

In this age of advanced technology, every university has a website to endorse their programs and encourage students around the world to join one of their faculties. However, universities give much priority to the functionality and usability of their websites and they give less attention to meet the users' demands for visually attractive websites that satisfy their emotions. This study proposes Factor Analysis (FA), Partial Least Squares (PLS) regression statistical methods and Kansei Engineering to identify elements of website design that are emotionally appealing to 18 - 37 age students in Turkey universities. A total of 22 Kansei words and 9 sample websites of Turkey universities are selected to investigate, 172 students consists of 84 females, and 88 males were asked to evaluate the selected websites using Kansei words (KWs). A 5-point semantic differential scale is used to evaluate the relationship between website elements and KWs. Multivariate Statistical Methods such as FA and PLS regression were performed to explore the most influential KWs and the corresponding websites. The results showed the highest and the lowest rating websites, the website categories that have a positive and negative impact on students Kansei. The outcome implied that the FA and PLS regression and Kansei Methodology in this study played a crucial role in website design in terms of satisfying users' demands in this study. Keywords: Factor Analysis; PLS regression; Kansei Engineering; Visual design; Kansei Words.