Driving Sustainably – The Influence of IoT-based Eco-Feedback on Driving Behavior

One starting point to reduce harmful greenhouse gas emissions is driving behavior. Previous studies have already shown that eco-feedback leads to reduced fuel consumption. However, less has been done to investigate how driving behavior is affected by eco-feedback. Yet, understanding driving behavior is important to target personalized recommendations towards re-duced fuel consumption. In this paper, we investigate a real-world data set from an IoT-based smart vehicle service. We first extract seven distinct factors that characterize driving behavior from data of 5,676 users. Second, we derive initial hypotheses on how eco-feedback may affect these factors. Third, we test these hypotheses with data of another 495 users receiving eco-feedback. Results suggest that eco-feedback, for instance, reduces hard acceleration maneuvers while interestingly speed is not affected. Our contribution extends the understanding of measuring driving behavior using IoT-based data. Furthermore, we contribute to a better understanding of the effect of eco-feedback on driving behavior

Managing the Inner Workings of Collective Intelligence Approaches for Wicked Problems – An Assessment Model and Evaluation

Ill-defined and complex problems that affect multiple stakeholders with potentially conflicting perspectives are often referred to as wicked problems. The utilization of collective intelligence (CI) via web-based platforms is a promising approach for addressing such wicked problems. The management of these information systems would benefit from evidence-based decision support regarding facilitation and improvement efforts. However, to date, there is no suitable model to guide such efforts. Existing approaches address specific applications or cover certain assessment areas but do not provide a holistic perspective. Meanwhile, research offers substantial insights into best practices for addressing wicked problems and running CI applications. This paper develops an assessment model comprising five central success dimensions for information systems that address wicked problems. Their subdimensions and associated measurement metrics allow for evidence-driven facilitation and improvement of CI applications for wicked problems. Apart from the model’s capability to improve future runs and processes, it also offers the potential to provide immediate insights for facilitation efforts during runtime. The model was validated with a platform dealing with the assessment of risks presented by global climate change. This evaluation generated strong evidence for the model’s applicability and usefulness

Auf dem Weg zum vertrauensvollen, unternehmensübergreifenden automatisierten Datenaustausch von Maschinen – Identifikation von schützenswertem Wissen im Zeitalter von Industrie 4.0

Der unternehmensübergreifende Datenaustausch in der Welt von Industrie 4.0 birgt für Unternehmen immense Potenziale. So können Unternehmen wertvolles Wissen über den Einsatz ihrer Produkte gewinnen und ihren Kunden innovative Dienstleistungen anbieten. Umgekehrt können Kunden die Produkte zielgerichteter einsetzen, wenn sie beispielsweise Produktions- und Materialdetails kennen. Doch dabei möchte kein Unternehmen für sich geschäftskritisches Wissen an einen Partner im Wertschöpfungsnetzwerk freigeben. Zu groß ist das Risiko, Einblicke in beispielsweise Forschungs- und Entwicklungsergebnisse zu gewähren oder dem Kunden eine Kostenkalkulation aufgrund des genauen Prozessablaufes zu ermöglichen. Es ergibt sich die Frage, welche Daten bedenkenlos ausgetauscht werden können und in welchen Daten implizit wertvolles Wissen enthalten ist. Aus diesem Grund stellt der vorliegende Beitrag ein Vorgehensmodell zur Identifikation von schützenswertem Wissen vor dem Hintergrund des unternehmensübergreifenden automatisierten Datenaustauschs von Maschinen über Netzwerkplattformen vor. Mit Hilfe des Modells lassen sich Daten und Wissen analysieren und auf Basis der Schutzbedarfe und enthaltenen Potenziale einstufen. Ein möglichst umfangreicher unternehmensübergreifender Datenaustausch bei möglichst geringem Verlust von Know-how soll ermöglicht werden. Anschließend wird die Erprobung des Modells im Rahmen eines Anwendungsbeispiels vorgestellt und ein Ausblick gegeben

Palliative care as a digital working world (PALLADiUM) - A mixed-method research protocol

Abstract Background In Palliative Care, actors from different professional backgrounds work together and exchange case-specific and expert knowledge and information. Since Palliative Care is traditionally distant from digitalization due to its holistically person-centered approach, there is a lack of suitable concepts enabling digitalization regarding multi-professional team processes. Yet, a digitalised information and collaboration environment geared to the requirements of palliative care and the needs of the members of the multi-professional team might facilitate communication and collaboration processes and improve information and knowledge flows. Taking this chance, the presented three-year project, PALLADiUM, aims to improve the effectiveness of Palliative Care teams by jointly sharing available inter-subjective knowledge and orientation-giving as well as action-guiding practical knowledge. Thus, PALLADiUM will explore the potentials and limitations of digitally supported communication and collaboration solutions. Methods PALLADiUM follows an open and iterative mixed methods approach. First, ethnographic methods – participant observations, interviews, and focus groups – aim to explore knowledge and information flow in investigating Palliative Care units as well as the requirements and barriers to digitalization. Second, to extend this body, the analysis of the historical hospital data provides quantitative insights. Condensing all findings results in a to-be work system. Adhering to the work systems transformation method, a technical prototype including artificial intelligence components will enhance the collaborative teamwork in the Palliative Care unit. Discussion PALLADiUM aims to deliver decisive new insights into the preconditions, processes, and success factors of the digitalization of a medical working environment as well as communication and collaboration processes in multi-professional teams. Trial registration The study was registered prospectively at DRKS (Deutsches Register Klinischer Studien) Registration-ID: DRKS0025356 Date of registration: 03.06.21