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Effects of Increased Usage of Artificial Intelligence (AI) Technology on User’s Perception of Dependency on Chatbots and Smart Home Systems

Erdmann, Matthias; Lassleben, Lennart; Wagner, Laurin; Prinzing, Christian...

30.05.2025.
DOI: 10.1007/978-3-031-93412-4_10


Peer Reviewed
 

AI-based technologies are becoming increasingly significant while transforming human-machine interactions. Yet, many important questions remain unanswered. One example is the research question of the present contribution, regarding the effects of artificial intelligence (AI) on users’ perceptions of dependency on chatbots and smart home systems. The objective is to analyze to what extent users perceive dependencies on these technologies and which factors influence this perception. Based on a survey of 325 users, it was found that dependency perception is currently low but increases with more frequent usage. The results show a substantial association between perception of dependence and frequency of use, with a stronger effect among users of chatbots compared to users of smart home systems. Furthermore, a slight negative effect was observed for attitudes toward AI on dependency perception for both chatbots and smart home systems. To test the hypotheses, linear regression analyses were employed, revealing substantial associations between usage frequency and dependency perception. Despite limitations such as gender imbalance in the sample—even though gender had a negligible effect on PoD –, this study provides valuable insights into the societal impacts of AI and lays the groundwork for future research in this area. Future studies should include larger and more representative samples and develop validated measurement instruments to enhance the generalizability of findings. The results emphasize the necessity of critically evaluating the deployment and integration of AI technologies to identify potential dependencies at an early stage.

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Statistik 1 -Einführung in die Statistik mit einem Schwerpunkt auf Prognose-Modellierung

Sauer, Sebastian (2025)

Independently published.


 

Dieses Buch führt in die Grundlagen der Statistik ein. Der Schwerpunkt liegt auf Prognose-Modellierung. Es ist ein angewandtes Buch für Anfänger. Sie lernen, Daten mit Hilfe der Programmiersprache R aufzubereiten und Vorhersagen abzuleiten mit Hilfe einfacher linearer Modelle.

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The Social Media Hate Speech Barometer: Making of

Sauer, Sebastian; Piazza, Alexander; Schacht, Sigurd (2023)

Proceedings - 5th International Conference Business Meets Technology 2023.
DOI: 10.4995/BMT2023.2023.16724


Open Access Peer Reviewed
 

Hate speech, particularly on social media channels, is a pressing cybersecurity concern and can even threaten the very foundations of societal stability. While there is a growing body of literature on how to detect and mitigate hate speech, applied researchers lack a state-of-the-art yet easily accessible infrastructure to build their own hate speech detection pipelines. We aim to provide an example of such an infrastructure that can serve as a template for other researchers. The infrastructure we present is based on the latest machine learning technologies available in the R environment: The Tidymodels framework and its extension Tidytext, plus the Targets project management approach, are the building blocks of our proposed infrastructure. In short, our data pipeline starts with downloading and preprocessing tweets, using various methods to convert text into numerical information. We then apply state-of-the-art supervised machine learning pipelines, drawing on a range of learning algorithms and incorporating new tuning capabilities. The focus of this paper is to explain the setup and rationale of the infrastructure. Our infrastructure is freely available on Github at https://github.com/sebastiansauer/hate-speech-barometer.

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Data Visualization – The Effect of Graph Animation on Persuasiveness

Hoffmann, Franziska; Sauer, Sebastian; González-Ladrón-de-Guevara, Fernando (2023)

Proceedings - 5th International Conference Business Meets Technology 2023.
DOI: 10.4995/BMT2023.2023.16727


Open Access Peer Reviewed
 

To make data visualization more attractive, not just color or different shapes are used, but also animations are nowadays added to graphs. Software tools for data visualization use different representation styles or display features and include animation in the data visualization process. Until now, the positive effect of different representation styles has been taken for granted in practice. However, this raises the question of the extent to which such representation styles are effective and what results can be seen in research in this regard. A literature review on data visualization is given to examine this in more detail. It was considered to what extent there are already findings on whether animated graphs have an effect on consumers’ behaviors. In the following work, we mean by an animated graph, a graph that is not visible from the beginning. Instead, the graph builds up over time. This way, motion is added to the graph. The literature review shows that data visualization has been studied more frequently in some areas. In marketing, this has received less consideration so far. Animations in graphs have not been studied much in the literature.

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A bibliometric analysis on reciprocal human-machine-interactions

Erdmann, Matthias; Perello-Marin, M. Rosario; Suárez Ruz, Maria Esperanza ...

Proceedings - 5th International Conference Business Meets Technology 2023, 163-176.
DOI: 10.4995/BMT2023.2023.16728


Open Access Peer Reviewed
 

Research into artificial intelligence is not a very young field; its precursors can be traced back as far as the 16th century. Today’s technical development, however, is virtually leaping forward, with new intelligent chat systems and social robots playing no small part in this. This is revolutionizing a wide range of scientific and social fields. The very large publication numbers in this field illustrate this as well. In order to keep track of the discourse in the field, the representatives of the field, the publications as well as the topics and their future development, it is indispensable for academics and scientists to prepare them in a bibliometric analysis. Only in this way it is possible to uncover thematic gaps as well as further points of contact and to drive research forward in a targeted and stringent manner. It is precisely this sorting and processing of the research discourse, the topics, and the authors, which is necessary for further research, that is carried out in this paper. For this purpose, using bibliometric analysis tools, an overview of the past, present, and future of the research field is created, and the general relevant topics are uncovered. The analysis includes as performance analysis a) the total number of publications and b) the total number of citations, and for science mapping c) a co-citation analysis (past), d) a bibliographic coupling (present) and e) a co-word analysis (future). The data needed for the analysis are identified and extracted from the SCOPUS or Web of Science (ISI) databases

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Most healthcare interventions tested in Cochrane Reviews are not effective according to high quality evidence: a systematic review and meta-analysis

Howick, Jeremy; Koletsi, Despina; Ioannidis, John P.A.; Madigan, Claire...

Journal of Clinical Epidemiology 148, 160-169.
DOI: 10.1016/j.jclinepi.2022.04.017


Peer Reviewed
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Dataset "predictors of performance in stats test"

Sauer, Sebastian (2021)

Open Science Framework.
DOI: 10.17605/OSF.IO/SJHUY


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Statistics education from a data-centric perspective

Gehrke, Matthias; Kistler, Tanja; Lübke, Karsten; Markgraf, Norman; Krol, Bianca...

Teaching Statistics 43 (S1), S201-S215.
DOI: 10.1111/test.12264


Open Access Peer Reviewed
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Analyse der Nützlichkeit von Amazon-Produktbewertungen mittels Text Mining

Bosten, Florian; Di Stefano, Manuel; Hartmann, Maren; Sauer, Sebastian...

Buchkremer, R., Heupel, T., Koch, O. (eds), Künstliche Intelligenz in Wirtschaft & Gesellschaft. FOM-Edition. , 609-644.
DOI: 10.1007/978-3-658-29550-9_30


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Analyse von Nützlichkeits- und Sterne-Online-Bewertungen mittels Machine Learning am Beispiel von Amazon

Di Stefano, Manuel; Bosten, Florian; Erhard, Michel Sebastian; Sauer, Sebastian...

Buchkremer, R., Heupel, T., Koch, O. (eds) Künstliche Intelligenz in Wirtschaft & Gesellschaft., 609-644.



Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Menden, Michael P.; Wang, Dennis; Mason, Mike; et al, ...; Sauer, Sebastian...

Nature Communications 10, 2674.
DOI: 10.1038/s41467-019-09799-2


Open Access
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TBS-TK-Rezension: Rasch-basiertes Depressionsscreening (DESC, 1. Auflage)

Groen, Gunter; Sauer, Sebastian (2019)

Psychologische Rundschau 70 (1), 96-98.
DOI: 10.1026/0033-3042/a000431


Open Access Peer Reviewed
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Moderne Datenanalyse mit R: Daten einlesen, aufbereiten, visualisieren und modellieren

Sauer, Sebastian (2019)

(FOM-Edition). Springer, Wiesbaden.


Open Access
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Die Arbeitsweise der Forschung zu Zeiten von Digitalisierung und Reproduzierbarkeitskrise: Neue Methoden, alte Probleme

Sauer, Sebastian; Sülzenbrück, Sandra (2019)

Arbeitswelten der Zukunft. FOM-DÈdition. Springer Gabler, Wiesbaden , 181-200.
DOI: 10.1007/978-3-658-23397-6_11


Open Access
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Psychologisches Kapital fördern durch Führung - eine quantitative Beobachtungsstudie zu den Wirkmechanismen von Servant Leadership

Sülzenbrück, Sandra; Ferreira, Yvonne; Sauer, Sebastian; Strehl, Timo (2019)

Wirtschaftpsychologie 21 (3), 60-68.


Peer Reviewed

Wie populistisch tweeten unser Politiker? – Eine Data-Mining-Studie

Sauer, Sebastian (2018)

51. Kongress der deutschen Gesellschaft für Psychologie. Frankfurt, 15.-20.09.2018.



Prädiktoren des AfD-Wahlerfolgs bei der Bundestagswahl 2017

Sauer, Sebastian (2018)

51. Kongress der deutschen Gesellschaft für Psychologie. Frankfurt, 20.09.2018.



Reproduzierbares Schreiben in der Wissenschaft am Beispiel des Buchprojekts „Moderne Datenanalyse mit R“

Sauer, Sebastian (2018)

51. Kongress der Deutschen Gesellschaft für Psychologie, Frankfurt, 18.09.2020.



Real-time Prediction of User Performance based on Pupillary Assessment via Eye Tracking

Buettner, Ricardo; Sauer, Sebastian; Maier, Christian; Eckhardt, Andreas (2018)

AIS Transactions on Human-Computer Interaction 10 (1), 26-56.
DOI: 10.17705/1thci.00103


Peer Reviewed
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Observation oriented modeling revised from a statistical point of view

Sauer, Sebastian (2018)

Behavior Research Methods 50 (4), 1749-1761.


Peer Reviewed
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