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Alves, T., Goncalves, D., Gama, S. & Henriques-Calado, J. (2022). Examining user preferences based on personality factors in graphical user interface design. ICGI 2022 - International Conference on Graphics and Interaction, Proceedings. IEEE. https://doi.org/10.1109/ICGI57174.2022.9990341

T. A. Alves et al.,  "Examining user preferences based on personality factors in graphical user interface design", in ICGI 2022 - Int. Conf. on Graphics and Interaction, Proc., IEEE, 2022

@inproceedings{alves2022_1789161725275,
	author = "Alves, T. and Goncalves, D. and Gama, S. and Henriques-Calado, J.",
	title = "Examining user preferences based on personality factors in graphical user interface design",
	booktitle = "ICGI 2022 - International Conference on Graphics and Interaction, Proceedings",
	year = "2022",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/ICGI57174.2022.9990341",
	publisher = "IEEE",
	address = "",
	organization = "",
	url = "https://ieeexplore.ieee.org/document/9990341"
}

TY  - CPAPER
TI  - Examining user preferences based on personality factors in graphical user interface design
T2  - ICGI 2022 - International Conference on Graphics and Interaction, Proceedings
AU  - Alves, T.
AU  - Goncalves, D.
AU  - Gama, S.
AU  - Henriques-Calado, J.
PY  - 2022
DO  - 10.1109/ICGI57174.2022.9990341
UR  - https://ieeexplore.ieee.org/document/9990341
AB  - Individual differences play a major role in human-computer interaction. In particular, personality shapes how we process and act on the world, and how users perceive and accept technology. Nevertheless, there is limited evidence on the effect of different personality types in graphical user interface design preferences. Weighting how personality affects perception, we leverage in-depth synergies between personality variables and design preferences for graphical user interface elements to study whether it is possible to formulate a novel set of design guidelines that allow the creation of user interfaces customized to psychological variables. A clustering approach of the subjects (N=65) yielded three different personality profiles based on the personality variables of the Five-Factor Model. Then, an association rules algorithm produced a set of rules from which we created a set of design guidelines. We discuss the study implications and future work opportunities.
ER  -