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

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.