Description
People often choose one design over another for reasons of beauty and taste. This is known as aesthetic preference. Over the years, philosophers and psychologists have observed the significant impact of aesthetics on human behavior and sought a deeper understanding of how aesthetic preferences are formed and how they drive behavior and choices. Despite recent advancement in the scientific study of aesthetics with the emergence of neuroaesthetics and evolutionary psychology, the complexity and diversity of aesthetic preferences still pose a significant challenge for designers who design for a mass population. This study proposed and implemented a process through which unique aesthetic indicators were identified, from which distinct aesthetic typologies were then derived. To evaluate the process and generate practical results, a mixed-methods approach with exploratory sequential design was used. First, an online survey and semi-structured interviews were conducted (n=20). These methods were used to refine the survey instrument. Next, an extensive online survey (n=1038) was conducted to identify aesthetic indicators. To produce measurable outcomes and define the typologies of individuals based on their responses to survey questions, cluster analysis was applied to the data. Results indicated a set of unique aesthetic indicators from which distinct aesthetic typologies were derived. This study adds to the vast body of knowledge we can use to explore and improve our understanding of aesthetic preference. With the availability of quantitative data and the robust modeling capabilities of Artificial Intelligence (AI), it is not unrealistic that we would be able to model and predict future or unknown aesthetic preferences. Accurate predictions of these preferences can have immense value for the field of design. Aesthetic typologies provide the structure to move design in that direction.
Download count: 3
Details
Title
- Typologies of Taste: A Process to Derive Distinct Aesthetic Typologies from Unique Aesthetic Indicators
Contributors
- Duvenhage, Jonanda (Author)
- Takamura, John (Thesis advisor)
- Pivovarova, Margarita (Committee member)
- Fehler, Michelle (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2021
Subjects
Resource Type
Collections this item is in
Note
-
Partial requirement for: M.S.D., Arizona State University, 2021
-
Field of study: Design