TY - JOUR A1 - Herrmann, Frank A1 - Engl, Fabian A1 - Trubjansky, Philipp T1 - Ein funktionaler Vergleich der SAP Analytics Cloud und Microsoft Power BI zur Verwendung im Bereich People Analytics bei Vitesco Technologies JF - Anwendungen und Konzepte der Wirtschaftsinformatik N2 - Aufgrund leistungsbedingter Einschränkungen durch die aktuelle Business-Intelligence-Software PowerBI vergleicht die People-Analytics-Abteilung von Vitesco Technologies diese mit der Alternativsoftware SAP Analytics Cloud. Dafür wurden zunächst aktuelle Herausforderungen im People-Analytics-Umfeld identifiziert und basierend darauf Vergleichskriterien erarbeitet. Als Vergleichsmodell kommt das Kano-Modell zum Einsatz. Die durchgeführte Evaluation favorisiert aus funktionaler Sicht einen Umstieg auf die SAP Analytics Cloud, identifiziert allerdings eine Reihe an Herausforderungen, die einen sofortigen Wechsel einschränken. Zu diesen gehören sowohl die Verfügbarkeit als auch die Qualität der HR-Daten. KW - Business Intelligence KW - SAP Analytics Cloud KW - SAC KW - Power BI KW - Cloud Computing KW - Kano-Modell KW - Vergleich KW - People Analytics KW - HR Y1 - 2022 U6 - https://doi.org/10.26034/lu.akwi.2022.3337 SN - 2296-4592 IS - 15 SP - 8 EP - 21 PB - Hochschule Luzern ER - TY - JOUR A1 - Engl, Fabian A1 - Herrmann, Frank T1 - A Machine Learning based Approach on Employee Attrition Prediction with an Emphasize on predicting Leaving Reasons JF - Anwendungen und Konzepte der Wirtschaftsinformatik N2 - Using Vitesco Technologies as an example, this article examines whether machine learning models are suitable for detecting employee attrition at an early stage, with the aim of uncovering underlying reasons for leaving. Nine different machine learning algorithms were examined: K-nearest-neighbors, Naive Bayes, logistic regression, a support vector machine, a neural network, a random forest, adaptive boosting, and two gradient boosting models. A three-way-holdout validation method was implemented to assess the quality of the results and measure both the f-score and the degree of model generalization. Initially, it was found that tree-based methods are best suited for classifying employees. A multiclass classification approach showed that under certain conditions it is even possible to predict the underlying leaving reasons. KW - Machine Learning KW - Employee Attrition Prediction KW - Fluctuation Prediction KW - Employee Attrition KW - Leaving Reasons KW - AI Y1 - 2023 U6 - https://doi.org/10.26034/lu.akwi.2023.4488 SN - 2296-4592 IS - 18 SP - 30 EP - 40 PB - AKWI ER - TY - CHAP A1 - Kristen, Meret A1 - Engl, Fabian A1 - Mottok, Jürgen T1 - Enhancing Phishing Detection: An Eye-Tracking Study on User Interaction and Oversights in Phishing Emails T2 - SECURWARE 2024 : The Eighteenth International Conference on Emerging Security Information, Systems and Technologies, November 03-07, 2024, Nice, France N2 - Phishing remains a significant threat to organizational security, necessitating effective countermeasures. This paper presents findings from an in-depth eye-tracking study with 103 participants, evaluating the effectiveness of phishing awareness tools and trainings. The study examines how a phishing awareness system influences user behavior, efficiency, and the ability to identify phishing attempts. By analyzing eye movements, the study reveals real-time interactions and oversights, providing insights into the decision-making process. Results indicate that while the system improves the efficiency of users already proficient in phishing detection, it does not universally enhance recognition rates. Notably, participants using the tool spent significantly less time looking at attachment-related phishing markers, indicating partial efficiency improvements. Since phishing attempts containing suspicious attachments were successful in 19% of cases, as compared to an overall phishing success rate of 15%, the phishing awareness tool is particularly useful here. A usability evaluation revealed that users reporting a higher perceived usability score profited more from the help of the tool. Additionally, no improvement in phishing detection rates was observed in users who had completed prior IT-security training, highlighting the necessity for a paradigm shift in phishing training to adequately prepare users for phishing attempts. Y1 - 2024 UR - https://www.thinkmind.org/library/SECURWARE/SECURWARE_2024/securware_2024_2_80_30041.html SN - 978-1-68558-206-7 SP - 71 EP - 80 PB - IARIA ER - TY - CHAP A1 - Bittner, Dominik A1 - Hauser, Florian A1 - Engl, Fabian A1 - Mottok, Jürgen ED - Mottok, Jürgen ED - Hagel, Georg T1 - Eye Movement Modelling Examples on Usability Heuristics T2 - Proceedings of the 6th European Conference on Software Engineering Education : ECSEE 2025, Seeon Germany, June 02-04, 2025 N2 - The user interface (UI) and user experience (UX) design is of crucial importance for human-computer interaction (HCI), particularly in the context of web applications. In light of the high expectations of users and the competitive nature of the market, it is imperative to employ usability measurement techniques to avoid losing users. Heuristic evaluation (HE) is a cost- and resource-efficient method for evaluating the usability of websites in which evaluators are guided by heuristics. However, the level of expertise of the evaluators has a significant impact on the results, with experts identifying up to 50% more usability issues than novices. To address this gap, this paper proposes Eye Movement Modeling Examples (EMMEs) to demonstrate Jakob Nielsen’s ten usability heuristics in an easy-to-understand format for all levels of experience while also incorporating expert knowledge. In particular, the eye movements and verbal feedback of a usability expert are recorded as the expert analyses the usability of a simple website application in terms of Jakob Nielsen’s ten usability heuristics. This reveals the strategies and cognitive processes of the expert when assessing the usability of a website and makes them more tangible for non-experts or novices. The findings of a questionnaire-based assessment indicate that EMMEs are perceived as beneficial and supportive during the learning process. Ultimately, this comprehensive analysis not only enables a deeper understanding of heuristics for usability novices, but could also lead to EMMEs being applied more efficiently in diverse domains. Y1 - 2025 SN - 9798400712821 U6 - https://doi.org/10.1145/3723010.3723035 SP - 106 EP - 114 PB - ACM ER -