What Makes Good Design? Revealing the Predictive Power of Emotions and Design Dimensions in Non Expert Design Vocabulary
- Authors
- So, Chaehan
- Issue Date
- 2019
- Publisher
- ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
- Keywords
- design psychology; design thinking; user-centred design; participatory design; machine learning
- Citation
- DESIGN JOURNAL, v.22, no.3, pp.325 - 349
- Journal Title
- DESIGN JOURNAL
- Volume
- 22
- Number
- 3
- Start Page
- 325
- End Page
- 349
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/12990
- DOI
- 10.1080/14606925.2019.1589204
- ISSN
- 1460-6925
- Abstract
- This paper investigates how nonexperts perceive digital design, and which psychological dimensions are underlying this perception of design. It thus constructs a measurement instrument to analyse user response to online displayed design and to predict design preference. Study 1 let non-experts rank the usefulness of 115 adjectives for describing good design in an online survey (n = 305). This item pool was condensed to 12 design descriptive and five emotion items. Exploratory factor analysis revealed the four underlying psychological dimensions Novelty, Energy, Simplicity and Tool. Study 2 (n = 1955) tested Study 2's model in three real-world design projects. Emotions clearly outperformed the best design descriptive dimensions (Novelty and Tool) in predicting users' design preference (Net Promoter Score) with beta = .82. Study 3 (n =1955) confirmed Study 2's results by several machine learning algorithms (neural networks, gradient boosting machines, random forests) with cross-validation. This measurement instrument benefits designers to implement a participatory design thinking process with users.
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- Appears in
Collections - International Desing for Advanced Studies > Design Management > 1. Journal Articles
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