Exploring the Scientific Impact of Information Systems Design Science Research

2021 | Communications of the Association for Information Systems | Citations: 4

Authors: Wagner, Gerit; Prester, Julian; Schryen, Guido

Abstract: While design science research has established its position as a prominent field ... Expand

Abstract: While design science research has established its position as a prominent field of research in the IS community, there is a lack of transparency regarding the impact of recent information systems design science research (IS DSR) papers. This lack of insight arguably poses challenges to an informed discourse and limits our ability to communicate the progress that IS DSR has achieved. Therefore, after mapping impactful IS DSR papers, we develop a scientometric study to address the lack of insights into factors that affect the scientific impact of IS DSR papers in top IS journals. In this study, we focus on active, IS-specific DSR areas and consider papers published in the AIS Senior Scholars' basket of journals between 2004 and 2014. Specifically, we develop a model that explores factors that affect IS DSR papers' scientific impact. Our findings show that theorization and novelty significantly explain scientific impact. We discuss our work's implications and derive recommendations intended to shape future knowledge creation in IS DSR. Collapse

Topics: IS discipline data quality business process management relational database knowledge base

Methods: bibliometric analysis co-citation analysis design theory computational algorithm design science

TheoryOn: A Design Framework and System for Unlocking Behavioral Knowledge Through Ontology Learning

2020 | Management Information Systems Quarterly | Citations: 25

Authors: Li, Jingjing; Larsen, Kai; Abbasi, Ahmed

Abstract: The scholarly information-seeking process for behavioral research consists of th ... Expand

Abstract: The scholarly information-seeking process for behavioral research consists of three phases: searching, accessing, and processing of past research. Existing IT artifacts, such as Google Scholar, have in part addressed the searching and accessing phases, but fall short of facilitating the processing phase, creating a knowledge inaccessibility problem. We propose a behavioral ontology learning from text (BOLT) design framework that presents concrete prescriptions for developing systems capable of supporting researchers during their processing of behavioral knowledge. Based upon BOLT, we developed a search engine— TheoryOn—to allow researchers to directly search for constructs, construct relationships, antecedents, and consequents, and to easily integrate related theories. Our design framework and search engine were rigorously evaluated through a series of data mining experiments, a randomized user experiment, and an applicability check. The data mining experiment results lent credence to the design principles prescribed by BOLT. The randomized experiment compared TheoryOn with EBSCOhost and Google Scholar across four information retrieval tasks, illustrating TheoryOn’s ability to reduce false positives and false negatives during the information-seeking process. Furthermore, an in-depth applicability check with IS scholars offered qualitative support for the efficacy of an ontology-based search and the usefulness of TheoryOn during the processing phase of existing research. The evaluation results collectively underscore the significance of our proposed design artifacts for addressing the knowledge inaccessibility problem for behavioral research literature. Collapse

Topics: knowledge representation search engine perceived usefulness usage intention information retrieval

Methods: experiment applicability check design science ontological modelling design artifact

Theories: organizational behavior theory technology acceptance model learning theory information systems theory behavioral theory