Christine Legner

Christine Legner is a Professor of Information Systems at the Faculty of Business and Economics (HEC), University of Lausanne. She is also the academic director of the Competence Centers Corporate Data Quality (CC CDQ), an industry-funded research consortium and expert community with 20 corporate partners (among them BASF, Bayer, Beiersdorf, Bosch, Nestlé, Schaeffler, SAP, Siemens and Tetrapak). Together with Dr. Olivier Verscheure, she is the co-director of the Executive Certificate in Data Science and Management (CAS), a joint program offered by University of Lausanne and EPFL. 

She has published articles in leading IS journals, including the Journal of Management Information Systems, the Journal of the AIS, the Journal of Strategic Information Systems, the Journal of Information Technolology, the Information Systems Journal, and Business & Information Systems Engineering. She is also editor of a book on Strategic Enterprise Architecture Management and has co-authored eBooks on data strategy and data catalogs.

Christine Legner and her research team develop concepts, tools and methods for data management. Sustainability has become a research focus, because reporting on sustainability initiatives and goals requires collecting, processing, and interpreting substantial amounts of data (for instance, on emissions or recycled material) that were previously neither captured nor analyzed.

Krasikov, P., Legner, C. (2023): Introducing a Data Perspective to Sustainability: How Companies Develop Data Sourcing Practices for Sustainability Initiatives, Communications of the AIS, accepted with major revisions

Naous D., Bonner, M., Humbert, M., Legner C. (2022): Learning from the Past to Improve the Future – Value-added Services as a Driver for Mass Adoption of Contact Tracing Apps. Business Information Systems & Engineering (BISE), 64(5), pp. 597-614 [10.1007/s12599-022-00742-2]

Legner, C., Pentek, T., Otto, B. (2020): Accumulating Design Knowledge with Reference Models: Insights from 12 Years’ Research into Data Management. Journal of the Association for Information Systems (JAIS), 21 (3) pp. 735–770 [10.17705/1jais.00618]