The duality of science: Knowledge in information systems research

Abstract

Rigorous research in Information Systems requires an understanding of how scientific foundations drive both the process and outcome. This science duality addresses the effective application of the scientific method (process) as well as the advance of knowledge (outcome) from the research project. This commentary responds to Siponen and Klaavuniemi’s paper with a focus on the uses of knowledge in Information Systems research.

Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection

“…Moreover, DRs are part of the problem space, aid in the evaluation of the designed solutions, and are an integral component in EDTs that aim to explain how general design components address general requirements (Baskerville & Pries-Heje, 2010;vom Brocke et al, 2020;Venable et al, 2016). To adapt and justify the developed DRs, we used descriptive and, especially prescriptive knowledge (Gregor & Hevner, 2013;vom Brocke et al, 2020;Hevner, 2020). In scientific literature, knowledge that was contributed via prior research projects and is used in a new DSR project is also denoted as input knowledge (vom Brocke & Maedche, 2019).…”

“…We transferred these generic DRs into our application domain (i.e., automated hate speech detection for human moderators) to establish an anchor in this domain as well as in the associated knowledge bases. For example, the generic design requirements from Meth et al (2015) are part of a design theory that the authors developed in their DSR project and are therefore part of the prescriptive knowledge base on which we build on (vom Brocke et al, 2020;Hevner, 2020). Another component of the prescriptive knowledge base that we rely on are design entities (vom Brocke et al, 2020;Hevner, 2020).…”

“…For example, the generic design requirements from Meth et al (2015) are part of a design theory that the authors developed in their DSR project and are therefore part of the prescriptive knowledge base on which we build on (vom Brocke et al, 2020;Hevner, 2020). Another component of the prescriptive knowledge base that we rely on are design entities (vom Brocke et al, 2020;Hevner, 2020). The prescriptive knowledge base in our DSR project consists of contributions on AI-based hate speech detection with transfer learning (e.g., Ayo et al, 2020;Plazadel-Arco et al, 2021), XAI-based UI design (e.g., Barda et al, 2020;Cheng et al, 2019), and prior research on information systems for hate speech detection (e.g., Modha et al, 2020;Paschalides et al, 2020).…”

“…By investigating the before described knowledge bases and operationalizing the insights as well as contributions, we aimed to adequately transfer the generic design requirements into our application domain. Consequently, we consumed existing knowledge that informed the proposed design knowledge (vom Brocke & Maedche, 2019;Gregor & Hevner, 2013;Hevner, 2020). The discussed DRs were refined based on the insights gained during the evaluations of the three design cycles.…”

“…Decision-makers' need for information that assists them in their decision-making processes has been clearly identified in the existing literature (e.g., Gönül et al, 2006;Kaplan & Haenlein, 2019, 2020Li & Gregor, 2011). Therefore, the following DR aims to provide appropriate information and knowledge, which are integral parts of decision-making (Zack, 2007).…”