eThesis - Kauppakorkeakoulun sähköiset gradut

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Objectives of the study

The Human-Computer Interaction (HCI) field has long concentrated on easily measured variables such as effectiveness, adoption and ease of use to study technology usage. In order to study user preference in particular, the inclusion of more socio-cognitive variables such aesthetics and emotions is necessary. However, these subjective experiences are intrinsically harder to measure: using subjective measures like questionnaires give results that are colored by cognitive processing, whereas subjective evaluations are formed instantly after exposure. The objective is to find what the effect of aesthetics and emotions are to user preference, and to test whether eye movement could provide an objective measure to support and validate subjective measures.

Academic background and methodology

Based on previous literature and studies, a model predicting user preference is developed. For eye movement tracking, modern abstract and representative art are used as test material. It is hypothesized that user preference is predicted by aesthetics, fixations (static eye movement), valence, arousal and dominance (emotions). The direct effect of these variables as well as the mediating effect of emotions is studied. The direct effect is tested by regression analysis, and the results are used for modify the model accordingly. The results are verified by path analysis which is also used to test the mediation effect of emotions and group differences of abstract and representative images.

Findings and conclusions

The research hypotheses were mainly confirmed by the study. It was found that aesthetics, valence and arousal explain and predict preference. Only dominance did not significantly predict preference. Aesthetic and affect reactions are formed instantly after viewing an object, they are involuntary in nature and the effect of these rapid evaluations is long lasting. Measuring such swift decisions is challenging, but it was found that eye movement and fixations in particular predict preference. In practical terms, it means fixations can be used along with self-reported measures to corroborate subjective evaluations.