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Individuals are frequently confronted with privacy-related decisions under uncertainty especially in online contexts. The resulting privacy concerns are a decisive factor for individuals to (not) use online services. In order to support individuals to make more informed decisions, we assess the current state of practice of certain online services. This analysis is focused on ride-sharing and includes popular services in Germany, Austria, and Switzerland and we investigate how they handle user data. The results show that services include a wide-ranging set of personal data and lack standardization. Furthermore, they offer limited privacy-related features. Based on this analysis, we developed a Transparency Enhancing Technology in the form of a browser extension that informs users about data practices of the services at the time of data disclosure. In addition to this, we conducted a scenario-based online experiment with a representative sample to evaluate the usability of our tool and its effect on users’ concerns and behavior. Results show significant improvements in awareness and decision reflection with limited decrease in disclosure rates of personal data.

Die zunehmende Verfügbarkeit und Nutzung technologischer Schlüsselkomponenten für die digitale Transformation hat in 2022 zu einer Dynamik bei der Entwicklung und Anwendung von Feldrobotern geführt. Dies betrifft insbesondere Sensorsysteme, Dateninterpretation (insb. Künstliche Intelligenz) und -kommunikation und digitale Zwillinge. Der Übergang vom Feldroboter zur Feldrobotik, d.h. die Systemintegration in den landwirtschaftlichen Betrieb, steht hinsichtlich des nachhaltigen Nutzens und der Robustheit der Systeme im Fokus. Bei der komplexen adaptiven Integration der Feldrobotik in die Praxis sind neben dem Zusammenspiel von Pflanzenbau/Tierhaltung, Technik und Betriebswirtschaft weitere Bereiche von zentraler Bedeutung, z.B. arbeitswissenschaftliche, juristische und gesellschaftliche Aspekte sowie die Weiterbildung ("Neue Interdisziplinarität"). Anhand des Forschungsprojektes "Agro-Safety" wird das Zusammenspiel der Themen Autonomie, Sensorik und funktionale Sicherheit illustriert.

Die Schlüsselwörter dabei sind:

• Digitale Transformation
• Feldrobotik
• neue Interdisziplinarität
• funktionale Sicherheit

In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules for regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which, in turn, affects vehicle idling time at intersections, fuel consumption, CO2 emissions, and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable, and inexpensive way to identify the type of intersection control (e.g., traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicle crossing intersections is proposed. A modification of a well-known clustering algorithm is used to detect stopping and deceleration episodes. These episodes are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per intersection arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the intersection arms according to their traffic control type (dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single intersection arm are used (one-arm model) and another where features also from neighboring intersection arms of the same intersection are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 95% to 97%.

With rising electricity demand through digitization and innovation, the urgency of climate change mitigation, and the recent geopolitical crisis, stakeholders in developing countries face the complex task to build reliable, affordable, and low-emission energy systems. Information inaccessibility, data unavailability, and scarce local expertise are major challenges for planning and transitioning to decentralized solutions. Motivated by the calls for more solution-oriented research regarding sustainability, we design, develop, and evaluate the web-based decision support system NESSI4Dweb+ that is tailored to the needs and capabilities of various stakeholders in developing countries. NESSI4Dweb+ is open access and considers location-specific circumstances to facilitate multi-energy planning. Its applicability is demonstrated with a case study of a representative rural village in southern Madagascar and evaluated through seven interviews with experts and stakeholders. We show that NESSI4Dweb+ can support the achievement of the United Nations Sustainable Development Goals and enable the very prerequisite of digitization: reliable electrification.

Voice commerce creates unprecedented opportuni­ties for consumers and vendor firms to interact, engage, and relate. With artificial intelligence–pow­ered voice assistants, consumers can make technol­ogy-mediated purchases without using their tactile senses, which represents a new space for commer­cial interactions. Drawing on the customer journey as an organizing framework, this article proposes a structured research agenda, in an attempt to shed light on the bright side effects of voice commerce while also acknowledging concerns for consumer protection and society in general. Voice assistants can enhance every stage of the purchase journey, yet their use might have negative consequences for customer relationships. In the prepurchase stage, voice assistants can act as information curators or manipulators. In the purchase stage, voice assis­tants can adopt roles as shopping concierges or impediments. In the postpurchase and usage stage, voice assistants can become trusted relationship partners or hostile intruders in consumers’ lives.