This book presents the current state of research in information systems and digital transformation. Due to the global trend of digitalization and the impact of the Covid 19 pandemic, the need for innovative, high-quality research on information systems is higher than ever. In this context, the book covers a wide range of topics, such as digital innovation, business analytics, artificial intelligence, and IT strategy, which affect companies, individuals, and societies.
This volume gathers the revised and peer-reviewed papers on the topic "Technology" presented at the International Conference on Information Systems, held at the University of Duisburg-Essen in 2021.
Reihe
Auflage
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Zielgruppe
Illustrationen
64 farbige Abbildungen, 90 s/w Abbildungen
XIX, 773 p. 154 illus., 64 illus. in color.
Maße
Höhe: 235 mm
Breite: 155 mm
Dicke: 43 mm
Gewicht
ISBN-13
978-3-030-86796-6 (9783030867966)
DOI
10.1007/978-3-030-86797-3
Schweitzer Klassifikation
Frederik Ahlemann is a Full Professor of Information Systems at the University of Duisburg-Essen, Germany. His research interests are strategic IT management and digital transformation.
Reinhard Schütte is a Full Professor of Information Systems at the University of Duisburg-Essen, Germany. His main research interests are retail and large enterprise systems, digital transformation and philosophy of science.
Stefan Stieglitz is a Full Professor of Information Systems at the University of Duisburg-Essen, Germany. His research is about communication and collaboration technologies, including social media and virtual reality.
Data Science & Business Analytics.- Information Extraction from Invoices: A Graph Neural Network Approach for Datasets with High Layout Variety.- Knowledge Sharing in Digital Platform Ecosystems - A Textual Analysis of SAP's Developer Community.- Leveraging Natural Language Processing to Analyze Scientific Content: Proposal of an NLP pipeline for the field of Computer Vision.- Hybrid Recommender Systems for Next Purchase Prediction Based on Optimal Combination Weights.- Towards a Trust-aware Item Recommendation System on a Graph Autoencoder with Attention Mechanism.- A Holistic Framework for AI Systems in Industrial Applications.- Managing Bias in Machine Learning Projects.- Design, Management and Impact of AI-based Systems.- User-specific Determinants of Conversational Agent Usage: A Review and Potential for Future Research.
