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

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

A Network-Graph Based IT Artifact Aiding the Theory Building Process

Proceedings of the 55th Hawaii International Conference on System Sciences | 2022The article of record at published may be found at https://hdl.handle.net/10125/80136To support theory building, we introduce a network-graph based IT artifact to provide high recall during exploratory searches and high precision using knowledge gained through the literature discovery process. The use of network graphs, where all data is represented as a node, relationship, or property of either, offers a flexible and tailorable methodology able to accommodate the highly iterative process of theory building. This IT artifact was developed to enable aggregation and normalization of data from varied sources and formats to support the acquisition and assessment of literature needed throughout this process. Our goal in presenting this IT artifact is to promote an accessible and pragmatic approach addressing the varied challenges of Information Systems researchers during the information seeking process

A Network-Graph Based IT Artifact Aiding the Theory Building Process

To support theory building, we introduce a network-graph based IT artifact to provide high recall during exploratory searches and high precision using knowledge gained through the literature discovery process. The use of network graphs, where all data is represented as a node, relationship, or property of either, offers a flexible and tailorable methodology able to accommodate the highly iterative process of theory building. This IT artifact was developed to enable aggregation and normalization of data from varied sources and formats to support the acquisition and assessment of literature needed throughout this process. Our goal in presenting this IT artifact is to promote an accessible and pragmatic approach addressing the varied challenges of Information Systems researchers during the information seeking process

Towards an Open Repository for Design Science Research: A Meta-Model and its Instantiation for the Representation of Design Science Research Processes

Design Science Research (DSR) is a well-established paradigm in the Information Systems field generating knowledge on the design of innovative solutions to real-world problems. The maturity of DSR has increased due to many methodological contributions, including conceptualization of the design process, templates on how to plan and document, as well as guidelines on how to conduct DSR projects. At the same time, given the dynamic nature of design in the digital era, DSR methods are also constantly further developed by the community. Both access to existing DSR methods and its further development are hindered today by the way we represent DSR methods. Most of the DSR methods are scattered in different papers or books. In order to foster accessibility and further development, we propose a harmonized representation of DSR process knowledge (as a core component of DSR methods) in an open repository. Applying DSR ourselves, we 1) identify meta-requirements for a DSR process modeling system 2) derive initial design principles 3) propose a meta-model 4) provide an instantiation of the meta-model in the form of an open repository, and 5) evaluate our design based on interviews with DSR researchers using the repository. We report from two DSR cycles, then discuss our findings and outline avenues for future research

How Best to Hunt a Mammoth - Toward Automated Knowledge Extraction From Graphical Research Models

In the Information Systems (IS) discipline, central contributions of research projects are often represented in graphical research models, clearly illustrating constructs and their relationships. Although thousands of such representations exist, methods for extracting this source of knowledge are still in an early stage. We present a method for (1) extracting graphical research models from articles, (2) generating synthetic training data for (3) performing object detection with a neural network, and (4) a graph reconstruction algorithm to (5) storing results into a designated research model format. We trained YOLOv7 on 20,000 generated diagrams and evaluated its performance on 100 manually reconstructed diagrams from the Senior Scholars\u27 Basket. The results for extracting graphical research models show a F1-score of 0.82 for nodes, 0.72 for links, and an accuracy of 0.72 for labels, indicating the applicability for supporting the population of knowledge repositories contributing to knowledge synthesi

Using Natural Language Processing Techniques to Tackle the Construct Identity Problem in Information Systems Research

The growing number of constructs in behavioral research presents a problem to theory integration, since constructs cannot clearly be discriminated from each other. Recently there have been efforts to employ natural language processing techniques to tackle the construct identity problem. This paper compares the performance of the novel word-embedding model GloVe and different document projection methods with a latent semantic analysis (LSA) used in recent literature. The results show that making use of an advantage in document projection that LSA has over GloVe, performance can be improved. Even against this advantage of LSA, GloVe reaches comparable performance, and adjusted word embedding models can make up for this advantage. The proposed approach therefore presents a promising pathway for theory integration in behavioral research

Detecting Flow Experiences in Cognitive Tasks - A Neurophysiological Approach

Das Flow-Erlebnis beschreibt einen Zustand vollständiger Aufgabenvertiefung und mühelosen Handelns, der mit Höchstleistungen, persönlichem Wachstum, sowie allgemeinem Wohlbefinden verbunden ist. Für Unternehmen stellen häufigere Flow-Erlebnisse der ArbeitnehmerInnen daher auch eine produktivitäts- und zufriedenheitsfördernde Basis dar. Vor allem da sich aktuell globale Phänomene wie die steigende Nachfrage nach Wissensarbeit und das niedrige Arbeitsengagement zuspitzen, können Unternehmen von einer Förderung von Flow profitieren. Die Unterstützung von Flow stellt allerdings aufgrund der Vielfalt von Arbeitnehmerfertigkeiten, -aufgaben, und -arbeitsplätzen eine komplexe Herausforderung dar. WissensarbeiterInnen stehen dynamischen Aufgaben gegenüber, die diverse Kompetenzen und die Kooperation mit anderen erfordern. Arbeitsplätze werden vielseitiger, indem die Grenzen zwischen ko-präsenten und virtuellen Interaktionen verschwinden. Diese Vielfalt bedeutet, dass eine solide Flow-Förderung nur durch personen-, aufgaben- und situationsunabhängige Ansätze erfolgen kann. Aus diesem Grund werden zunehmend die neurophysiologischen Grundlagen des Flow-Erlebens untersucht. Auf deren Basis könnten adaptive Neuro-Informationssysteme entwickelt werden, die mittels tragbarer Sensorik Flow kontinuierlich erkennen und fördern können. Diese Wissensbasis ist bislang jedoch nur spärlich und in stark fragmentierter Form vorhanden. Für das Individuum existieren lediglich konkurrierende Vorschläge, die noch nicht durch situations- und sensorübergreifende Studien konsolidiert wurden. Für Gruppen existiert noch fast keine Forschung zu neurophysiologischen Flow-Korrelaten, insbesondere keine im Kontext digital-mediierter Interaktionen. In dieser Dissertation werden genau diese Forschungslücken durch die situationsübergreifende Beobachtung von Flow mit tragbaren EKG und EEG Sensoren adressiert. Dabei werden zentrale Grenzen der experimentellen Flow-Forschung berücksichtigt, vor allem die Defizite etablierter Paradigmen zum kontrollierten Hervorrufen von Flow. Indem Erlebnisse in zwei kognitiven Aufgaben und mehreren Manipulationen (von Schwierigkeit, Natürlichkeit, Autonomie und sozialer Interaktion) variiert werden, wird untersucht, wie Flow intensiver hervorgerufen und wie das Erlebnis stabiler über Situationen hinweg beobachtet werden kann. Die Studienergebnisse deuten dabei insgesamt auf ein Flow-Muster von moderater physiologischer Aktivierung und mentaler Arbeitslast, von erhöhter, aufgabenorientierter Aufmerksamkeit und von affektiver Neutralität hin. Vor allem die EEG Daten zeigen ein diagnostisches Potenzial, schwächere von stärkeren Flow-Zuständen unterscheiden zu können, indem optimale und nicht-optimale Aufgabenschwierigkeiten (für Individuen und Gruppen) erkannt werden. Um das Flow-Erleben weiter zu fördern, werden geeignete Wege für zukünftige Forschung abschließend diskutiert