What Makes Good Design? Revealing the Predictive Power of Emotions and Design Dimensions in Non-Expert Design Vocabulary

• Published in: The Design journal Vol. 22; no. 3; pp. 325 - 349
• Main Author: So, Chaehan
• Format: Journal Article
• Language: English
• Published: Oxford Routledge 04.05.2019; Taylor & Francis Ltd
• Subjects: Algorithms; Co-design; Design; design psychology; Design thinking; Emotions; Factor analysis; Machine learning; Neural networks; participatory design; Psychology; user-centred design
• ISSN: 1460-6925; 1756-3062
• DOI: 10.1080/14606925.2019.1589204

This paper investigates how non-experts 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 β = .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.