Open Science Practices in Information Systems Research

Permanent URI for this collectionhttps://hdl.handle.net/10125/107412

Browse

Recent Submissions

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item type: Item ,
    Black Box or Open Science? Assessing Reproducibility-Related Documentation in AI Research
    (2024-01-03) Koenigstorfer, Florian; Haberl, Armin; Kowald, Dominik; Ross-Hellauer, Tony; Thalmann, Stefan
    The surge in Artificial Intelligence (AI) research has spurred significant breakthroughs across various fields. However, AI is known for its Black Box character and reproducing AI outcomes challenging. Open Science, emphasizing transparency, reproducibility, and accessibility, is crucial in this context, ensuring research validity and facilitating practical AI adoption. We propose a framework to assess the quality of AI documentation and assess 51 papers. We conclude that despite guidelines, many AI papers fall short on reproducibility due to insufficient documentation. It is crucial to provide comprehensive details on training data, source code, and AI models, and for reviewers and editors to strictly enforce reproducibility guidelines. A dearth of detailed methods or inaccessible source code and models can raise questions about the authenticity of certain AI innovations, potentially impeding their scientific value and their adoption. Although our sample size inhibits broad generalization, it nonetheless offers key insights on enhancing AI research reproducibility.
  • Thumbnail Image
    Item type: Item ,
    Introduction to the Minitrack on Open Science Practices in Information Systems Research
    (2024-01-03) Doyle, Cathal; Chiu, Yi-Te; Nagle, Tadhg; Luczak-Roesch, Markus
  • Thumbnail Image
    Item type: Item ,
    Breaking Down Barriers: How Conversational Agents Facilitate Open Science and Data Sharing
    (2024-01-03) Mirbabaie, Milad; Rieskamp, Jonas; Hofeditz, Lennart; Stieglitz, Stefan
    Many researchers hesitate to provide full access to their datasets due to a lack of knowledge about research data management (RDM) tools and perceived fears, such as losing the value of one's own data. Existing tools and approaches often do not take into account these fears and missing knowledge. In this study, we examined how conversational agents (CAs) can provide a natural way of guidance through RDM processes and nudge researchers towards more data sharing. This work offers an online experiment in which researchers interacted with a CA on a self-developed RDM platform and a survey on participants’ data sharing behavior. Our findings indicate that the presence of a guiding and enlightening CA on an RDM platform has a constructive influence on both the intention to share data and the actual behavior of data sharing. Notably, individual factors do not appear to impede or hinder this effect.