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Additive manufacturing becomes a more and more important technology for production, mainly driven by the ability to realise extremely complex structures using multiple materials but without assembl...

Graphical abstract, We present a generic method for considering incomplete but gradually expandable sociological data in agent-based modeling based on the classic model of cultural dissemination by Axelrod. Our method extension was inspired by research on the diffusion of citizen photovoltaic initiatives, i.e. by initiatives in which citizens collectively invest in photovoltaic plants and share the profits. Owing to the absence of empirical interaction parameters, the Axelrod model was used as basis for considering peer effects with contrived interaction data that can be updated from empirical surveys later on. The Axelrod model was extended to cover the following additional features: • Consideration of empirical social science data for concrete social interaction. • Development of a variable and fine-tunable interaction function for agents. • Deployment of a generic procedure for modeling peer effects in agent-based models.

Reprocessing of used products is a growing field, with respect to both scientific and practical approaches. In this context, we present an in-depth case study dealing with the reverse logistics processes at Repair- and Service Center R.U.S.Z, an Austrian Work Integration Social Enterprise (WISE) located in Vienna, Austria. The main business segments of R.U.S.Z are reprocessing, repairing, and servicing of (used) products and repair services. The reverse logistics activities include relevant processes like acquisition, testing and grading, and disposition/reprocessing of used goods. Based upon the case study, we present the gained insights and furthermore identify research opportunities. Our main findings are: (1) the reverse logistics activities of this non-profit-organization are equivalent compared with the profit-driven approaches used in literature; (2) the business of R.U.S.Z is not solely profit- or cost-driven but the company is based on the triple bottom line and pursues environmental and social goals, too; (3) in spite of legislation aiming at the reuse of used products, there is lack of collaboration between manufacturers and reprocessors.

In automated driving, an appropriate level of driver trust is essential to improve safety and ensure zero fatalities. Drivers must have a sufficient level of trust to intervene correctly in safety-critical situations: very low levels may lead to either continuous and excessive monitoring of the functions, reducing the attention paid to the environment or switching off these functions, whereas extreme trust in automated driving functions can result in dangerous driving situations because the environment is either insufficiently monitored or not monitored at all. A deeper understanding of trust in automated driving is challenging and requires a triangulated study in which the type of driver, vehicle usage, and environmental data are varied. However, many previous studies were based on a rather limited set of data sources, often relying on qualitative means such as pre-and-post interviews or trust questionnaires to evaluate trust in autonomous driving functions. Although data gathered through empirical research, such as conducting quantitative surveys or qualitative interviews, are simple to store and analyze, the collection and integration of vehicle and sensor data from different data sources usually pose important technical challenges in practice. Hence, a suitable data collection and integration strategy is required to address these challenges. In this context, we propose a general framework for collecting and integrating data from different sources with diverse capabilities and requirements to determine a driver’s trust in automated driving. Our proposed framework facilitates the integration of empirical and measurement data, allowing a triangulated investigation to provide a road map for the automotive industry.