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

摘要

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 p = .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.