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

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)325-349
Number of pages25
JournalDesign Journal
Volume22
Issue number3
DOIs
Publication statusPublished - 2019 May 4

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Vocabulary
Emotion
Factor analysis
Learning algorithms
Learning systems
Neural networks
Psychological
Descriptive
Novelty
Adjective
Participatory Design
Energy
Cross-validation
Designer
Digital Design
Factor Analysis
Real World
Neural Networks
Machine Learning
Simplicity

All Science Journal Classification (ASJC) codes

  • Arts and Humanities (miscellaneous)
  • Computer Graphics and Computer-Aided Design

Cite this

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What Makes Good Design? Revealing the Predictive Power of Emotions and Design Dimensions in Non-Expert Design Vocabulary. / So, Chaehan.

In: Design Journal, Vol. 22, No. 3, 04.05.2019, p. 325-349.

Research output: Contribution to journalArticle

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