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.
Bibliographical noteFunding Information:
This work was supported by the 2016 Hongik University New Faculty Research Fund.
This work was supported by the 2016 Hongik University New Faculty Research Fund. The author would like to thank Alex Hayes (University of Wisconsin-Madison) for his helpful comments on evaluation of machine learning models.
All Science Journal Classification (ASJC) codes
- Arts and Humanities (miscellaneous)
- Computer Graphics and Computer-Aided Design