Constructing and measuring domain-specific emotions for affective design: a descriptive approach to deal with individual differences

Abstract

Assessing design solutions via domain-specific emotions has been widely concerned and explored in the field of affective design. However, the examination and accommodation of individual differences have not been settled sufficiently in the literature. To address this research gap, this paper proposes a descriptive approach to draw calibrated collective emotion patterns in survey-based affective design assessment. A ‘Repertory Grid Interview linked with Rate-All-That-Apply’ (RGI/RATA) procedure is firstly conducted to elicit and code the individual’s personal emotional descriptions into mid-level Emotion Words (EWs) and to gather emotion data grids with each grid quantified by an individual’s own EWs. The obtained individualised emotion data grids are then subjected to Multiple Factor Analysis (MFA) to extract collective emotional space, thus to enable conceptualising collective emotional dimensions and measuring calibrated collective responses. A case study demonstrating the implementation process for a simple project of appearance design assessment is also presented.

Practitioner Summary: The proposed methodology may help a design team to investigate the shared patterns of domain-specific emotions through a single assessment survey. With the provided post hoc analysis tools, designers may also evaluate multi-level individual differences (e.g. regarding user groups or even intra-individual) quantitatively and at a low cost.

Abbreviations: EWs: emotion words; HF/E: human factors and ergonomics; IEA: International Ergonomics Association; MFA: multiple factor analysis; PCT: personal construct theory; PCA: principal component analysis; RGI/RATA: repertory grid interview linked with rate-all-that-apply; RGI: repertory grid interview; RATA: rate-all-that-apply; SD: standard deviation; USB: universal serial bus

Keywords:

• Domain-specific emotions
• affective design
• individual differences
• repertory grid interview
• multiple factor analysis

Acknowledgements

We would like to acknowledge the participants of the case study for their time and involvement in the interview. We also thank the editor and anonymous reviewers for their valuable comments and advice.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [71771045, 71471033].