DISKNET – A Platform for the Systematic Accumulation of Knowledge in IS Research

Research output: Contributions to collected editions/worksPublished abstract in conference proceedingsResearchpeer-review

Authors

  • David Dann
  • Alexander Maedche
  • Tim Teubner
  • Benjamin Mueller
  • Christian Meske
  • Burkhardt Funk
The accumulation of knowledge is key for any discipline, IS being no exception. With the number of publications, theoretical constructs, and empirical findings growing, surging demand for structuring and meta-analysis is foreseeable. We introduce DISKNET, an online platform that enables the extraction, exploration, and aggregation of construct’s definitions, semantic relations, and analytical relations. While these aspects exhibit a rather standardized structure in theory, their practical documentation is non-uniform, highly dispersed, and tricky to seize technically. This has impeded the efficiency and effectiveness of review and meta-analytical processes, and resulted in a fragmented theoretical superstructure. We suggest that tool support for systematic knowledge accumulation is a central step to counteract these issues and to build to a consistent body of knowledge within the IS discipline. The current prototype of DISKNET draws on a large sample of SEM-based studies to demonstrate relevant design principles for a platform for systematic accumulation of knowledge.
Original languageEnglish
Title of host publicationICIS 2019 Proceedings : IS research methods, theorizing and philosophy of science
Number of pages9
PublisherAssociation for Information Systems
Publication date2019
Article number Paper ID 2785
ISBN (electronic)978-0-9966831-9-7
Publication statusPublished - 2019
EventInternational Conference on Information Systems - ICIS 2019 - International Congress Center Munich, München, Germany
Duration: 15.12.201918.12.2019
https://aisel.aisnet.org/icis2019/
https://icis2019.aisconferences.org/

    Research areas

  • Business informatics
  • Construct identity, Knowledge repository, Meta-analysis, Nomological network