© OWMs

User Experience



selected Publications

Eisbach, S., Daugs, F., Thielsch, M. T., Böhmer, M., & Hertel, G. (in press). Predicting Rating Distributions of Website Aesthetics with Deep Learning for AI-Based Research. ACM Transactions on Computer-Human Interaction (TOCHI).

Abstract: The aesthetic appeal of a website has strong effects on users’ reactions, appraisals, and even behaviors. However, evaluating website aesthetics through user ratings is resource intensive, and extant models to predict website aesthetics are limited in performance and ability. We contribute a novel and more precise approach to predict website aesthetics that considers rating distributions. Moreover, we use this approach as a baseline model to illustrate how future research might be conducted using predictions instead of participants. Our approach is based on a deep convolutional neural network model and uses innovations in the field of image aesthetic prediction. It was trained with the dataset from Reinecke and Gajos (2014) and was validated using two independent large datasets. The final model reached an unprecedented cross-validated correlation between the ground truth and predicted rating of LCC = 0.752. We then used the model to successfully replicate prior findings and conduct original research as an illustration for AI-based research.


Reiners, S., Müller, L.S., Becker, J., Hertel, G. (2022). Measuring the Influence of Characteristics on Decision-Making Scenarios: A Prototype. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2022 Posters. HCII 2022. Communications in Computer and Information Science, vol 1580. Springer, Cham. https://doi.org/10.1007/978-3-031-06417-3_52

Hertel, G. & Meeßen, S. M. (2020). Wie Technologien das Vergessen unterstützen – und warum das wichtig ist. Wirtschaftspsychologie aktuell, Heft 1 2020, S. 38-42. https://www.psychologenverlag.de/Produkte/dCatID/162/pid/761/backLink/Produkte__catID__162

Hertel, G., Meeßen, S. M., & Höddinghaus, M. (2020). Trust in the Context of e-HRM. In Bondarouk, T. & Fisher, S. (Eds.), Encyclopedia of Electronic HRM (pp. 76–81). Berlin: De Gruyter Oldenbourg. https://doi.org/10.1515/9783110633702-012. [PDF]

Meeßen, S.M., Thielsch, M. T., Riehle, D. M. & Hertel, G. (2020). Trust is Essential: Positive Effects of Information Systems on Users’ Memory require Trust in the System. Ergonomics, 63 (7), 909-926. https://doi.org/10.1080/00140139.2020.1758797

Müller, L. S., Meeßen, S. M., Thielsch, M. T., Nohe, C., Riehle, D. M., & Hertel, G. (2020). Do not Disturb! Trust in Decision Support Systems improves work outcomes under certain conditions. MuC '20: Proceedings of the Conference on Mensch und Computer 2020, pp. 229–237. New York: ACM. https://doi.org/10.1145/3404983.3405515

Hertel, G., Meeßen, S. M., Riehle, D. M., Thielsch, M. T., Nohe, C., & Becker, J. (2019). Directed forgetting in organisations: The positive effects of decision support systems on mental resources and well-being. Ergonomics, Vol. 62 (No. 5), 597-611. doi: https://doi.org/10.1080/00140139.2019.1574361

Thielsch, M. T., Engel, R. & Hirschfeld, G. (2015). Expected usability is not a valid indicator of experienced usability. PeerJ Computer Science, 1:e19. http://dx.doi.org/10.7717/peerj-cs.19

Thielsch, M. T., Blotenberg, I. & Jaron, R. (2014). User evaluation of websites: From first impression to recommendation. Interacting with Computers, 26 (1), 89-102. http://dx.doi.org/10.1093/iwc/iwt033

Thielsch, M. T. & Hirschfeld, G. (2012). Spatial frequencies in aesthetic website evaluations – explaining how ultra-rapid evaluations are formed. Ergonomics, 55 (7), 731-742. http://dx.doi.org/10.1080/00140139.2012.665496

Moshagen, M. & Thielsch, M. T. (2010). Facets of visual aesthetics. International Journal of Human-Computer Studies, 68 (10), 689-709. http://dx.doi.org/10.1016/j.ijhcs.2010.05.006