This map shows the geographic impact of Kai Heinrich's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kai Heinrich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Heinrich more than expected).
This network shows the impact of papers produced by Kai Heinrich. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kai Heinrich. The network helps show where Kai Heinrich may publish in the future.
Co-authorship network of co-authors of Kai Heinrich
This figure shows the co-authorship network connecting the top 25 collaborators of Kai Heinrich.
A scholar is included among the top collaborators of Kai Heinrich based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Kai Heinrich. Kai Heinrich is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wanner, Jonas, et al.. (2021). ADOPTION BARRIERS OF AI: A CONTEXT-SPECIFIC ACCEPTANCE MODEL FOR INDUSTRIAL MAINTENANCE. Journal of the Association for Information Systems.5 indexed citations
6.
Wanner, Jonas, Lukas-Valentin Herm, Kai Heinrich, Christian Janiesch, & Patrick Zschech. (2020). White, Grey, Black: Effects of XAI Augmentation on the Confidence in AI-based Decision Support Systems. Journal of the Association for Information Systems.10 indexed citations
7.
Heinrich, Kai, et al.. (2020). FOOL ME ONCE, SHAME ON YOU, FOOL ME TWICE, SHAME ON ME: A TAXONOMY OF ATTACK AND DE-FENSE PATTERNS FOR AI SECURITY. Journal of the Association for Information Systems.2 indexed citations
8.
Wanner, Jonas, Kai Heinrich, Christian Janiesch, & Patrick Zschech. (2020). How Much AI Do You Require? Decision Factors for Adopting AI Technology. Journal of the Association for Information Systems.17 indexed citations
Zschech, Patrick, et al.. (2019). Towards a Text-based Recommender System for Data Mining Method Selection.. Journal of the Association for Information Systems.2 indexed citations
11.
Heinrich, Kai, et al.. (2019). Demystifying the Black Box: A Classification Scheme for Interpretation and Visualization of Deep Intelligent Systems.. Journal of the Association for Information Systems.3 indexed citations
12.
Heinrich, Kai, et al.. (2019). EVERYTHING COUNTS: A TAXONOMY OF DEEP LEARNING APPROACHES FOR OBJECT COUNTING. Journal of the Association for Information Systems.4 indexed citations
13.
Zschech, Patrick, et al.. (2019). Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA's Turbofan Degradation.. Journal of the Association for Information Systems.7 indexed citations
14.
Heinrich, Kai, et al.. (2019). Yield Prognosis for the Agrarian Management of Vineyards using Deep Learning for Object Counting. Journal of the Association for Information Systems. 407–421.6 indexed citations
Zschech, Patrick, et al.. (2017). ARE YOU UP FOR THE CHALLENGE? TOWARDS THE DEVELOPMENT OF A BIG DATA CAPABILITY ASSESSMENT MODEL. Journal of the Association for Information Systems. 2613.2 indexed citations
18.
Heinrich, Kai. (2015). Integration von Topic Models und Netzwerkanalyse bei der Bestimmung des Kundenwertes. Qucosa (Saxon State and University Library Dresden). 277–284.1 indexed citations
19.
Schieber, Andreas, Stefan Sommer, Andreas Hilbert, & Kai Heinrich. (2011). Analyzing customer sentiments in microblogs – A topic-model-based approach for Twitter datasets. Americas Conference on Information Systems.14 indexed citations
20.
Angst, Jules, R Battegay, D Bente, et al.. (1967). [On the common proceedings of a German and Swiss work group in the field of psychiatric documentation].. PubMed. 100(1). 207–11.4 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.