[AISWorld] CFP "Data Science & Business Analytics" - WI2024, Germany

[Hopf, Konstantin]

Dear IS scholars,

We welcome your submissions to the track "Data Science & Business Analytics" at the 19th International Conference on Wirtschaftsinformatik (WI 2024) September 16 - 19 in Würzburg, Germany.
Please find the call for paper announcement below. The submission deadline is March 15, 2024.

Contributions in English are highly encouraged and will be given equal consideration, even if the conference language is regularly German.

Kind regards,
Konstantin Hopf (University of Bamberg),
Patrick Zschech (University of Leipzig),
Barbara Dinter (TU Chemnitz),
Natalia Kliewer (FU Berlin)

--------------
Track 4: Data Science & Business Analytics

Track description
Rapid developments in the areas of computing power, sensor technology, storage systems, and internet technologies have a significant impact on our society and changing the way we live, interact, and work. Vehicles start driving autonomously in real traffic, smart home systems recognize and adapt to individual user preferences, and medical assistance systems will support doctors in diagnosing diseases that are difficult to detect. The business environment is also becoming increasingly digitized, and the ubiquitous use of IT systems is indispensable for many companies. Such scenarios require the collection of large volumes of data, which can be generated at high frequency in a wide variety of source systems. The resulting data sets represent a valuable resource for establishing data-centric business processes and enabling fact-based decision-making.

To exploit this potential and create organizational value using data, modern data analysis and data management methods and tools are required, which are often summarized under the collective term Data Science & Business Analytics. This includes a variety of approaches from different disciplines such as statistics, artificial intelligence, natural language processing, process mining, visual analytics, business intelligence, data quality management, data governance and many more.

Against this background, we welcome the entire diversity of business informatics-related research efforts in the areas of Data Science & Business Analytics (DS & BA) in our track. These range, for example, from the generation, collection, and representation of (big) data to the development of innovative theories, methods, and procedures for solving business and social problems, the design of analytical artifacts and the adoption and integration of these approaches in companies. Research papers on the development of new statistical and machine learning methods are welcome, as long as they are related to the solution of a business or social problem. We encourage authors to submit relevant and original contributions that exploit the methodological breadth of the research field.

Possible topics include:

  *   Innovation and emerging trends in DS & BA
  *   Business value and monetizing of DS & BA
  *   Adoption, routinization, maturity, and use of DS & BA
  *   DS & BA for social good, individual and societal empowerment, and digital responsibility
  *   Explainable artificial intelligence and interpretable machine learning
  *   Data privacy, data quality, and data governance
  *   Opportunities and challenges of sharing data and open data
  *   Digital manufacturing and the Internet of Things
  *   Operational, real-time, or event-driven business analytics
  *   Process mining and the benefits of robotic process automation
  *   Visual analytics and unstructured data analysis (e.g., text, image, audio, video) to address organizational and/or societal challenges
  *   Prescriptive analytics and operations research
  *   Data work and data science occupation

Track Chairs

  *   Konstantin Hopf (University of Bamberg)
  *   Patrick Zschech (University of Leipzig)
  *   Barbara Dinter (TU Chemnitz)
  *   Natalia Kliewer (FU Berlin)

Associate Editors:

  *   Kai Heinrich (OVGU Magdeburg)
  *   Sandra Zilker (FAU Erlangen-Nürnberg)
  *   Sven Weinzierl (FAU Erlangen-Nürnberg)
  *   Niklas Kühl (Universität Bayreuth)
  *   Karoline Glaser (TU Dresden)
  *   Frederik Möller (Universität Braunschweig)
  *   Thorsten Schoormann (TU Braunschweig)
  *   Nicolas Pröllochs (Universität Gießen)
  *   Dimitri Petrik (Universität Stuttgart)
  *   Jeannette Stark (TU Dresden)
  *   Stefan Greulich (TU Dresden)
  *   Simon Emde (Universität Jena)
  *   Milad Mirbabaie (Universität Bamberg)
  *   Oliver Müller (Universität Paderborn)
  *   Mayur Joshi (University of Ottawa)
  *   Sebastian A. Günther (Universität Bamberg)
  *   Henning Baars (Universität Stuttgart)
  *   Benjamin van Giffen (University of St. Gallen)
  *   Christian Schieder (OTH Amberg-Weiden)
  *   Burkhardt Funk (Leuphana University Lüneburg)
  *   Roland Müller (HWR Berlin)
  *   Paul Alpar (Universtiät Marburg)
  *   Ivo Blohm (University of St. Gallen)
  *   Alexander Mädche (Karlsruhe Institute of Technology)
  *   Bastian Amberg (FUB)
  *   Lin Xie (University of Twente)
  *   Andreas Fink (HSU Hamburg)
  *   Guido Schryen (Universität Paderborn)


Schedule for the review process

  *   Submission deadline: March 15, 2024
  *   Notification of authors: May 31, 2024
  *   Submission of  "camera-ready" versions: June 28, 2024
  *   Notification of final acceptance: July 05, 2024
  *   Main conference: September 16 - 19, 2024

Further details: https://wi2024.de/wissenschaftliches-programm/data-science-business-analytics/

-----
Dr. Konstantin Hopf
Senior Researcher and Lecturer

Chair of Information Systems and Energy Efficient Systems
University of Bamberg, Germany

tel:   +49 (0)951 / 863 2236
email: konstantin.hopf at uni-bamberg.de<mailto:konstantin.hopf at uni-bamberg.de>
web:   http://www.uni-bamberg.de/en/eesys/