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Many surveys require respondents to place themselves on a left-right ideology scale. However, non-experts may not understand the scale or their ’objective’ position. Furthermore, a uni-dimensional approach may not suffice to describe ideology coherently. We thus propose a novel way to measure voter ideology: Combining expert and voter survey data, we use machine learning to infer how political experts would place voters on three axes: general left-right, economic left-right and social/cultural ’GAL-TAN’. Our analysis suggests that i) voters are more likely to place themselves at the political center than we would pre-dict experts to do, ii) voters are ideologically most fragmented along the ’GAL-TAN’ axis, iii) European countries differ significantly in all ideological dimensions, and iv) ’objective’ ideology as predicted by our models improves the predictive power of simple spatial voting models even after accounting for the subjective ideological distance between voters and parties as perceived by the voters themselves.

Repair is a central component of a circular economy to extend the operational phase of products. Yet, the number of repair service providers as well as demand for repair have declined over the last decades, while more products than ever before were sold. Thus, for a successful transition from a linear to a circular economy the demand for repair services must be boosted to promote repair business. A starting point to achieve this goal is to increase knowledge about the end consumers’ intention whether to repair broken products or not. An extensive literature research revealed a comprehensive set of drivers of the repair intention covering aspects related to environmental protection, social acceptance, and economic considerations as well as socio-demographic variables, past behaviour, and perceived repair difficulty. Those factors are not only relevant for a specific product category but for repairs of consumer goods in general. The aim is to evaluate the relevance of those drivers for three different repair intentions: (1) to make use of repair service providers, (2) to self-repair broken items, and (3) to use repair service providers incorporated in a repair network. A quantitative online survey was designed, and distributed in Styria, Austria. By means of a structural equation model the acquired data of 900 respondents was analysed. The results emphasise the trade-off between acting environmentally-friendly and economic aspects like repair cost and time, but also highlight the effect of government intervention – in the form of setting up a network and financial support for repair – on shaping this trade-off. Furthermore, past behaviour is found to strongly drive repair intention. Our research contributes to scientific literature by shedding light on the influence of diverse drivers on different repair intentions. It is also relevant for supporting repair companies’ decision making with respect to repair service design, as well as public authorities interested in promoting repair.

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...

In view of global environmental and social challenges the transition towards a Circular Economy is considered as a crucial factor for sustainable development. Therefore, the replacement of traditio...

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.

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.

Effective and efficient closed-loop supply chain processes can constitute a significant competitive edge for companies. However, the integration of forward and reverse processes poses some challenges both on the supply side—e.g., availability of remanufacturable products—and on the demand side, e.g., cannibalization between new and remanufactured products. In this paper a two-period newsvendor-type approach is presented. The model is used to characterize the optimal production and remanufacturing policies. The main emphasis is on studying supply side interactions, in particular, the link between production and sales of new products and the resulting subsequent supply of used products. Further, the issue of storing excess production is addressed. The relationship between inventory and remanufacturing decisions is quantified.

Effective and efficient closed-loop supply chain processes can constitute a significant competitive edge for companies. However, the integration of forward and reverse processes poses some challenges both on the supply side - e.g. availability of remanufacturable products - and on the demand side, e.g. cannibalization between new and remanufactured products. In this paper a model for studying supply side interactions is presented. In a two-period setting with uncertain demand a newsvendor-type approach is used to characterize the optimal production and remanufacturing policies. The main emphasis is on the link between production and sales of new products and the resulting subsequent supply of used products. Further, the issue of storing excess production is addressed and the substitution effect between inventories and remanufacturable products is quantified.

Driver distraction is a major challenge in road traffic and major cause of accidents. Vehicle industry dedicates increasing amounts of resources to better quantify the various activities of drivers resulting in distraction. Literature has shown that significant causes for driver distraction are tasks performed by drivers which are not related to driving, like using multimedia interfaces or glancing at co-drivers. One key aspect of the successful implementation of distraction prevention mechanisms is to know when the driver performs such auxiliary tasks. Therefore, capturing these tasks with appropriate measurement equipment is crucial. Especially novel quantification approaches combining data from different sensors and devices are necessary for comprehensively determining causes of driver distraction. However, as a literature review has revealed, there is currently a lack of lightweight frameworks for multi-device integration and multi-sensor fusion to enable cost-effective and minimally obtrusive driver monitoring with respect to scalability and extendibility. This paper presents such a lightweight framework which has been implemented in a demonstrator and applied in a small real-world study involving ten drivers performing simple distraction tasks. Preliminary results of our analysis have indicated a high accuracy of distraction detection for individual distraction tasks and thus the framework’s usefulness. The gained knowledge can be used to develop improved mechanisms for detecting driver distraction through better quantification of distracting tasks.

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.