Open Access

One of the most significant challenges facing the world today is rapid climate change and the negative effects it is having on our environment. The transport sector is responsible for high CO2 emissions, and will have to make fundamental contributions to climate goal targets in the medium to long-term future. In the past, far-reaching measures have been tested regarding how to encourage people to switch from their own cars or motorised private transport (MPT) to the more emission-friendly local public transport (PT). Previous projects have only been able to convince people to switch temporarily through subsidised public transport. Detached from the ecological aspects, the turmoil in the global economy at the end of February 2022 resulted in a price shock in mineral oil prices, which shifted the primary focus of mobility behaviour from ecological to economic concerns. The logistic regression analysis of a quantitative survey (n = 611) in Germany confirms that a large number of journeys taken via private car were saved due to the increased price. However, despite high mineral oil prices, travelling by private car remains the primary means of transport for many people. Switching to public transport is particularly noticeable among women. This is due, among other things, to their lower incomes. Contrary to results of prior studies, the present analysis shows that participants from large cities have saved fewer trips by private car compared to people living in rural areas, even though large cities generally have a denser infrastructure with a more comprehensive range of mobility options. Travel time and reliability are the main factors in our respondents' choice of transport mode and are more compatible in large cities with denser public transport than in rural areas. The avoided car journeys are predominantly in the leisure sector and have not been substituted by other means of transport.

Consumers are using online reviews to decide which products to purchase. Cybercriminals produce fake reviews to influence unknowing consumers into buying products of lower quality, which can lead to financial, emotional and physical damage. We have little under-standing of how consumers make decisions about the veracity of online reviews, or incorpo-rate online reviews into purchasing decisions, especially outside of the laboratory. Therefore, in this study using a grounded theory approach we elaborate on how consumers determine the veracity and trustworthiness of online user reviews. Twenty-five interviews with Dutch and German consumers were held to identify deception cues, thought processes and other markers of online shopping behaviour. The results show that consumers use online reviews differently depending on context. Our new theory proposes that consumers process reviews in at least two steps. First, they scan the review for relevance and then they determine the trustworthiness, credibility, and veracity. Additionally, we identified different deception cues that are used. Together, these findings lead the way into a new understanding of human fake review detection online.

Das Feld des Verbraucherschutzes unterliegt einem permanenten Wandel. Hatten sich in der Vergangenheit im Bereich der Rechts- oder der Ernährungswissenschaften Ausbildungsgänge oder Spezialisierungsmöglichkeiten für dieses komplexe Berufsfeld entwickelt, so stehen die Akteure angesichts der beschleunigten Marktveränderungen doch immer wieder vor neuen Herausforderungen, die etwa heute auch Kompetenzanforderungen im Bereich des Hackens oder der Algorithmenkontrolle umfassen. Vor diesem Hintergrund gilt es über Verbraucherschutz als Beruf und Berufung genauer nachzudenken und insbesondere die Grenzen und Möglichkeiten der Professionalisierung in diesem Feld zu reflektieren.

The fields of humanoid service robotics and human-robot interaction are interdisciplinary domains that are increasingly gaining momentum in practice and academia. However, as it is a relatively new area of interest in retail contexts, there are unanswered questions and challenges. The aim of this study is to (1) investigate and evaluate the user experience and satisfaction of consumers in human-robot interactions in a retail scenario and (2) analyze the interaction between consumers’ age and satisfaction with their user experience with humanoid service robots. For this purpose, quantitative data was collected using a questionnaire filled out by customers of a shopping mall after they had interacted with a social robot of the model ‘Pepper’. Collected data was analyzed by using descriptive statistical analysis. The results suggest that consumers tend to evaluate their interaction experience positively and satisfactorily. In addition, age was found to have a significant impact on consumers' satisfaction with the robot, with younger participants tending to be more satisfied with the interaction than more senior ones. These results may have implications for the design of service robots and how innovative customer journeys may improve the attractiveness and satisfaction of retail shopping for consumers in different age groups.

The indoor thermal comfort in both homes and workplaces significantly influences the health and productivity of inhabitants. The heating system, controlled by Artificial Intelligence (AI), can automatically calibrate the indoor thermal condition by analyzing various physiological and environmental variables. To ensure a comfortable indoor environment, smart home systems can adjust parameters related to thermal comfort based on accurate predictions of inhabitants’ preferences. Modeling personal thermal comfort preferences poses two significant challenges: the inadequacy of data and its high dimensionality. An adequate amount of data is a prerequisite for training efficient machine learning (ML) models. Additionally, high-dimensional data tends to contain multiple irrelevant and noisy features, which might hinder ML models’ performance. To address these challenges, we propose a framework for predicting personal thermal comfort preferences, combining the conditional tabular generative adversarial network (CTGAN) with multiple feature selection techniques. We first address the data inadequacy challenge by applying CTGAN to generate synthetic data samples, incorporating challenges associated with multimodal distributions and categorical features. Then, multiple feature selection techniques are employed to identify the best possible sets of features. Experimental results based on a wide range of settings on a standard dataset demonstrated state-of-the-art performance in predicting personal thermal comfort preferences. The results also indicated that ML models trained on synthetic data achieved significantly better performance than models trained on real data. Overall, our method, combining CTGAN and feature selection techniques, outperformed existing known related work in thermal comfort prediction in terms of multiple evaluation metrics, including area under the curve (AUC), Cohen’s Kappa, and accuracy. Additionally, we presented a global, model-agnostic explanation of the thermal preference prediction system, providing an avenue for thermal comfort experiment designers to consciously select the data to be collected.

Vehicle emissions have been identified as a cause of air pollution and one of the major reasons why air quality in many large German cities such as Berlin, Bonn, Hamburg, Cologne or Munich does not meet EU-wide limits. As a result, in the recent past, judicial driving bans on diesel vehicles have been imposed in many places since those vehicles emit critical pollutant groups. For the increasing urban population, the challenge is whether and how a change of the modal split in favor of the more environmentally and climate-friendly public transport can be achieved. This paper presents the case of the Federal City of Bonn, one of five model cities sponsored by the German federal government that are testing measures to reduce traffic-related pollutant emissions by expanding the range of public transport services on offer. We present the results of a quantitative survey (N = 14,296) performed in the Bonn/Rhein-Sieg area and the neighboring municipalities as well as the ensuing logistic regressions confirming that a change in individual mobility behavior in favor of public transport is possible through expanding services. Our results show that individual traffic could be reduced, especially on the city's main traffic axes. To sustainably improve air quality, such services must be made permanently available.

Konsument:innen scheint die Lust vergangen zu sein, individuellen Kleidungsstil auszudrücken, da der Onlinehandel zur Steigerung von Auswahlmöglichkeiten geführt hat. Dies mündet unter anderem in der Nutzung virtueller Stilberatungen. Diese Dienste dienen dazu, Kund:innen möglichst effizient, individuell und authentisch „zu machen“, und sind somit als paradoxaler Demokratisierungsprozess zu verstehen. Eine Erklärung für den Erfolg dieser Dienstleistungen soll mit Reckwitz’ Singularisierungsthese gestützt werden.

Eine Untersuchung der Margen-Entwicklung in der Fleischbranche kommt zu einem eindeutigen Ergebnis: Der Lebensmitteleinzelhandel profitiert weit mehr als die anderen Teile der Wertschöpfungskette von den steigenden Preisen.

Occupants' personal thermal comfort (PTC) is indispensable for their well-being, physical and mental health, and work efficiency. Predicting PTC preferences in a smart home can be a prerequisite to adjusting the indoor temperature for providing a comfortable environment. In this research, we focus on identifying relevant features for predicting PTC preferences. We propose a machine learning-based predictive framework by employing supervised feature selection techniques. We apply two feature selection techniques to select the optimal sets of features to improve the thermal preference prediction performance. The experimental results on a public PTC dataset demonstrated the efficiency of the feature selection techniques that we have applied. In turn, our PTC prediction framework with feature selection techniques achieved state-of-the-art performance in terms of accuracy, Cohen's kappa, and area under the curve (AUC), outperforming conventional methods.

Aim of this study is to investigate the effects of user experience (UX) on shopping mall customers’ intention to use a social robot. Therefore, we used a Wizard of Oz approach that enabled data collection in situ. Quantitative data was obtained from a questionnaire completed by shopping mall customers who interacted with a social robot. Data was used in a regression analysis, where user experience factors served as predictors for robot use in retail. The regression model explains up to 23.2% of the variance in customers’ intention to use a social robot. In addition, we collected qualitative data on human-robot-interactions and used the data to complement the interpretation of statistical results. Our findings suggest that only hedonic qualities significantly contribute to the prediction of customers’ intention, that shopping mall customers are reluctant to grant pragmatic qualities to social robots, and that UX evaluation in HRI requires additional predictors.

Personal-Information-Management-Systeme (PIMS) gelten als Chance, um die Datensouveränität der Verbraucher zu stärken. Datenschutzbezogene Fragen sind für Verbraucher immer dort relevant, wo sie Verträge und Nutzungsbedingungen mit Diensteanbietern eingehen. Vor diesem Hintergrund diskutiert dieser Beitrag die Potenziale von VRM-Systemen, die nicht nur das Datenmanagement, sondern das gesamte Vertragsmanagement von Verbrauchern unterstützen. Dabei gehen wir der Frage nach, ob diese besser geeignet sind, um Verbraucher zu souveränem Handeln zu befähigen.

Advocates of autonomous driving predict that the occupation of taxi driver could be made obsolete by shared autonomous vehicles (SAV) in the long term. Conducting interviews with German taxi drivers, we investigate how they perceive the changes caused by advancing automation for the future of their business. Our study contributes insights into how the work of taxi drivers could change given the advent of autonomous driving: While the task of driving could be taken over by SAVs for standard trips, taxi drivers are certain that other areas of their work such as providing supplementary services and assistance to passengers would constitute a limit to such forms of automation, but probably involving a shifting role for the taxi drivers, one which focuses on the sociality of the work. Our findings illustrate how taxi drivers see the future of their work, suggesting design implications for tools that take various forms of assistance into account, and demonstrating how important it is to consider taxi drivers in the co-design of future taxis and SAV services.

Since stationary self-checkout is widely introduced and well understood, previous research barely examined newer generations of smartphone-based Scan&Go. Especially from a design perspective, we know little about the factors contributing to the adoption of Scan&Go solutions and how design enables consumers to take full advantage of this development rather than being burdened with using complex and unenjoyable systems. To understand the influencing factors and the design from a consumer perspective, we conducted a mixed-methods study where we triangulated data of an online survey with 103 participants and a qualitative study with 20 participants. Based on the results, our study presents a refined and nuanced understanding of technology as well as infrastructure-related factors that influence adoption. Moreover, we present several implications for designing and implementing of Scan&Go in retail environments.

Digital Business behandelt die Besonderheiten digitaler Geschäftsmodelle, den Umgang mit Daten, erläutert die Funktionsweise digitaler Märkte und deren Auswirkungen auf Servicefunktionen wie HR, Kommunikation, Finanzierung und Marketing. Zudem werden wesentliche Erfolgsfaktoren wie agiles Management und Customer Experience behandelt. Insgesamt haben 30 Experten mit ihrem spezifischem Know How an der Erstellung des praxisorientierten Litello-eBook mitgearbeitet, dass sich auch gut als Basis für einschlägige Lehrveranstaltung anbietet.

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.

Im Projekt wurden die Nachhaltigkeitspotentiale partizipativer landwirtschaftlicher Produktionskonzepte exemplarisch untersucht und der Versuch gemacht, deren Zufriedenheitspotential für Erzeuger und Bürger zu erfassen. Die landwirtschaftlichen Betriebe konnten dabei auf der Grundlage der Leitlinien der Sustainability Assessment of Food and Agriculture (SAFA) in den Dimensionen gute Unternehmensführung, ökologische Integrität, ökonomische Resilienz und soziales Wohlergehen bewertet werden. Die einzelnen Ergebnisse sind nach Dimensionen und korrespondierenden Indikatoren in diesem Working Paper beschrieben. Zudem konnten Handlungsempfehlungen auch dazu erarbeitet werden, wie Untersuchungen anhand der SAFA-Leitlinien erfolgreich durchgeführt werden können.