Open Access

Critical consumerism is complex as ethical values are difficult to negotiate, appropriate products are hard to find, and product information is overwhelming. Although recommender systems offer solutions to reduce such complexity, current designs are not appropriate for niche practices and use non-personalized intransparent ethics. To support critical consumption, we conducted a design case study on a personalized food recommender system. Therefore, we first conducted an empirical pre-study with 24 consumers to understand value negotiations and current practices, co-designed the recommender system, and finally evaluated it in a real-world trial with ten consumers. Our findings show how recommender systems can support the negotiation of ethical values within the context of consumption practices, reduce the complexity of finding products and stores, and strengthen consumers. In addition to providing implications for the design to support critical consumption practices, we critically reflect on the scope of such recommender systems and its appropriation.

Since its advent, the sustainability effects of the modern sharing economy have been the subject of controversial debate. While its potential was initially discussed in terms of post-ownership development with a view to decentralizing value creation and increasing social capital and environmental relief through better utilization of material goods, critics have become increasingly loud in recent years. Many people hoped that carsharing could lead to development away from ownership towards flexible use and thus more resource-efficient mobility. However, carsharing remains niche, and while many people like the idea in general, they appear to consider carsharing to not be advantageous as a means of transport in terms of cost, flexibility, and comfort. A key innovation that could elevate carsharing from its niche existence in the future is autonomous driving. This technology could help shared mobility gain a new boost by allowing it to overcome the weaknesses of the present carsharing business model. Flexibility and comfort could be greatly enhanced with shared autonomous vehicles (SAVs), which could simultaneously offer benefits in terms of low cost, and better use of time without the burden of vehicle ownership. However, it is not the technology itself that is sustainable; rather, sustainability depends on the way in which this technology is used. Hence, it is necessary to make a prospective assessment of the direct and indirect (un)sustainable effects before or during the development of a technology in order to incorporate these findings into the design and decision-making process. Transport research has been intensively analyzing the possible economic, social, and ecological consequences of autonomous driving for several years. However, research lacks knowledge about the consequences to be expected from shared autonomous vehicles. Moreover, previous findings are mostly based on the knowledge of experts, while potential users are rarely included in the research. To address this gap, this thesis contributes to answering the questions of what the ecological and social impacts of the expected concept of SAVs will be. In my thesis, I study in particular the ecological consequences of SAVs in terms of the potential modal shifts they can induce as well as their social consequences in terms of potential job losses in the taxi industry. Regarding this, I apply a user-oriented, mixed-method technology assessment approach that complements existing, expert-oriented technology assessment studies on autonomous driving that have so far been dominated by scenario analyses and simulations. To answer the two questions, I triangulated the method of scenario analysis and qualitative and quantitative user studies. The empirical studies provide evidence that the automation of mobility services such as carsharing may to a small extent foster a shift from the private vehicle towards mobility on demand. However, findings also indicate that rebound effects are to be expected: Significantly more users are expected to move away from the more sustainable public transportation, leading to an overcompensation of the positive modal shift effects by the negative modal shift effects. The results show that a large proportion of the taxi trips carried out can be re-placed by SAVs, making the profession of taxi driver somewhat obsolete. However, interviews with taxi drivers themselves revealed that the services provided by the drivers go beyond mere transport, so that even in the age of SAVs, the need for human assistance will continue – though to a smaller extent. Given these findings, I see action potential at different levels: users, mobility service providers, and policymakers. Regarding environmental and social impacts resulting from the use of SAVs, there is a strong conflict of objectives among users, potential SAV operators, and sustainable environmental and social policies. In order to strengthen the positive effects and counteract the negative effects, such as unintended modal shifts, policies may soon have to regulate the design of SAVs and their introduction. A key starting point for transport policy is to promote the use of more environmentally friendly means of transport, in particular by making public transportation attractive and, if necessary, by making the use of individual motorized mobility less attractive. The taxi industry must face the challenges of automation by opening up to these developments and focusing on service orientation – to strengthen the drivers’ main unique selling point compared to automated technology. Assessing the impacts of the not-yet-existing generally involves great uncertainty. With the results of my work, however, I would like to argue that a user-oriented technology assessment can usefully complement the findings of classic methods of technology assessment and can iteratively inform the development process regarding technology and regulation.

Data emerged as a central success factor for companies to benefit from digitization. However, the skills in successfully creating value from data – especially at the management level – are not always profound. To address this problem, several canvas models have already been designed. Canvas models are usually created to write down an idea in a structured way to promote transparency and traceability. However, some existing data science canvas models mainly address developers and are thus unsuitable for decision-makers and communication within interdisciplinary teams. Based on a literature review, we identified influencing factors that are essential for the success of data science projects. With the information gained, the Data Science Canvas was developed in an expert workshop and finally evaluated by practitioners to find out whether such an instrument could support data-driven value creation.

Autonomous driving enables new mobility concepts such as shared-autonomous services. Although significant re-search has been done on passenger-car interaction, work on passenger interaction with robo-taxis is still rare. In this paper, we tackle the question of how passengers experience robo-taxis as a service in real-life settings to inform the interaction design. We conducted a Wizard of Oz study with an electric vehicle where the driver was hidden from the passenger to simulate the service experience of a robo-taxi. 10 participants had the opportunity to use the simulated shared-autonomous service in real-life situations for one week. By the week's end, 33 rides were completed and recorded on video. Also, we flanked the study conducting interviews before and after with all participants. The findings provided insights into four design themes that could inform the service design of robo-taxis along the different stages including hailing, pick-up, travel, and drop-off.

2019 wurde bekannt, dass mehrere Anbieter von Sprachassistenten Sprachaufnahmen ihrer NutzerInnen systematisch ausgewertet haben. Da in den Datenschutzhinweisen angegeben war, dass Daten auch zur Verbesserung des Dienstes genutzt würden, war diese Nutzung legal. Für die NutzerInnen stellte diese Auswertung jedoch einen deutlichen Bruch mit ihren Privatheitsvorstellungen dar. Das Zweckbindungsprinzip der DSGVO mit seiner Komponente der Zweckspezifizierung fordert neben Flexibilität für den Verarbeiter auch Transparenz für den Verbraucher. Vor dem Hintergrund dieses Interessenkonflikts stellt sich für die HCI die Frage, wie Verarbeitungszwecke von Sprachassistenten gestaltet sein sollten, um beide Anforderungen zu erfüllen. Für die Erhebung einer Nutzerperspektive analysiert diese Studie zunächst Zweckangaben in den Datenschutzhinweisen der dominierenden Sprachassistenten. Darauf aufbauend präsentieren wir Ergebnisse von Fokusgruppen, die sich mit der wahrgenommenen Verarbeitung von Daten von Sprachassistenten aus Nutzersicht befassen. Es zeigt sich, dass bestehende Zweckformulierungen für VerbraucherInnen kaum Transparenz über Folgen der Datenverarbeitung bieten und keine einschränkende Wirkung im Hinblick auf legale Datennutzung erzielen. Unsere Ergebnisse über von Nutzern wahrgenommene Risiken erlauben dabei Rückschlüsse auf die anwenderfreundliche Gestaltung von Verarbeitungszwecken im Sinne einer Design-Ressource.