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The digitization of financial activities in consumers' lives is increasing, and the digitalization of invoicing processes is expected to play a significant role, although this area is not well understood regarding the private sector. Human-Computer Interaction (HCI) and Computer Supported Cooperative Work (CSCW) research have a long history of analyzing the socio-material and temporal aspects of work practices that are relevant for the domestic domain. The socio-material structuring of invoicing work and the working styles of consumers must be considered when designing effective consumer support systems. In this ethnomethodologically-informed, design-oriented interview study, we followed 17 consumers in their daily practices of dealing with invoices to make the invisible administrative work involved in this process visible. We identified and described the meaningful artifacts that were used in a spatial-temporal process within various storage locations such as input, reminding, intermediate (for postponing cases) buffers, and archive systems. Furthermore, we identified three different working styles that consumers exhibited: direct completion, at the next opportunity, and postpone as far as possible. This study contributes to our understanding of household economics and domestic workplace studies in the tradition of CSCW and has implications for the design of electronic invoicing systems.
Improved Thermal Comfort Model Leveraging Conditional Tabular GAN Focusing on Feature Selection
(2024)
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.
Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation Systems
(2023)
Peer-to-peer sharing platforms become increasingly important in the platform economy. From an HCI-perspective, this development is of high interest, as those platforms mediate between different users. Such mediation entails dealing with various social issues, e.g., building trust between peers online without any physical presence. Peer ratings have proven to be an important mechanism in this regard. At the same time, scoring via car telematics become more common for risk assessment by car insurances. Since user ratings face crucial problems such as fake or biased ratings, we conducted a design case study to determine whether algorithm-based scoring has the potential to improve trust-building in P2P-carsharing. We started with 16 problem-centered interviews to examine how people understand algorithm-based scoring, we co-designed an app with scored profiles, and finally evaluated it with 12 participants. Our findings show that scoring systems can support trust-building in P2P-carsharing and give insights how they should be designed.
Background
Consumers rely heavily on online user reviews when shopping online and cybercriminals produce fake reviews to manipulate consumer opinion. Much prior research focuses on the automated detection of these fake reviews, which are far from perfect. Therefore, consumers must be able to detect fake reviews on their own. In this study we survey the research examining how consumers detect fake reviews online.
Methods
We conducted a systematic literature review over the research on fake review detection from the consumer-perspective. We included academic literature giving new empirical data. We provide a narrative synthesis comparing the theories, methods and outcomes used across studies to identify how consumers detect fake reviews online.
Results
We found only 15 articles that met our inclusion criteria. We classify the most often used cues identified into five categories which were (1) review characteristics (2) textual characteristics (3) reviewer characteristics (4) seller characteristics and (5) characteristics of the platform where the review is displayed.
Discussion
We find that theory is applied inconsistently across studies and that cues to deception are often identified in isolation without any unifying theoretical framework. Consequently, we discuss how such a theoretical framework could be developed.
Trust your guts: fostering embodied knowledge and sustainable practices through voice interaction
(2023)
Despite various attempts to prevent food waste and motivate conscious food handling, household members find it difficult to correctly assess the edibility of food. With the rise of ambient voice assistants, we did a design case study to support households’ in situ decision-making process in collaboration with our voice agent prototype, Fischer Fritz. Therefore, we conducted 15 contextual inquiries to understand food practices at home. Furthermore, we interviewed six fish experts to inform the design of our voice agent on how to guide consumers and teach food literacy. Finally, we created a prototype and discussed with 15 consumers its impact and capability to convey embodied knowledge to the human that is engaged as sensor. Our design research goes beyond current Human-Food Interaction automation approaches by emphasizing the human-food relationship in technology design and demonstrating future complementary human-agent collaboration with the aim to increase humans’ competence to sense, think, and act.
Due to expected positive impacts on business, the application of artificial intelligence has been widely increased. The decision-making procedures of those models are often complex and not easily understandable to the company’s stakeholders, i.e. the people having to follow up on recommendations or try to understand automated decisions of a system. This opaqueness and black-box nature might hinder adoption, as users struggle to make sense and trust the predictions of AI models. Recent research on eXplainable Artificial Intelligence (XAI) focused mainly on explaining the models to AI experts with the purpose of debugging and improving the performance of the models. In this article, we explore how such systems could be made explainable to the stakeholders. For doing so, we propose a new convolutional neural network (CNN)-based explainable predictive model for product backorder prediction in inventory management. Backorders are orders that customers place for products that are currently not in stock. The company now takes the risk to produce or acquire the backordered products while in the meantime, customers can cancel their orders if that takes too long, leaving the company with unsold items in their inventory. Hence, for their strategic inventory management, companies need to make decisions based on assumptions. Our argument is that these tasks can be improved by offering explanations for AI recommendations. Hence, our research investigates how such explanations could be provided, employing Shapley additive explanations to explain the overall models’ priority in decision-making. Besides that, we introduce locally interpretable surrogate models that can explain any individual prediction of a model. The experimental results demonstrate effectiveness in predicting backorders in terms of standard evaluation metrics and outperform known related works with AUC 0.9489. Our approach demonstrates how current limitations of predictive technologies can be addressed in the business domain.
While the recent discussion on Art. 25 GDPR often considers the approach of data protection by design as an innovative idea, the notion of making data protection law more effective through requiring the data controller to implement the legal norms into the processing design is almost as old as the data protection debate. However, there is another, more recent shift in establishing the data protection by design approach through law, which is not yet understood to its fullest extent in the debate. Art. 25 GDPR requires the controller to not only implement the legal norms into the processing design but to do so in an effective manner. By explicitly declaring the effectiveness of the protection measures to be the legally required result, the legislator inevitably raises the question of which methods can be used to test and assure such efficacy. In our opinion, extending the legal compatibility assessment to the real effects of the required measures opens this approach to interdisciplinary methodologies. In this paper, we first summarise the current state of research on the methodology established in Art. 25 sect. 1 GDPR, and pinpoint some of the challenges of incorporating interdisciplinary research methodologies. On this premise, we present an empirical research methodology and first findings which offer one approach to answering the question on how to specify processing purposes effectively. Lastly, we discuss the implications of these findings for the legal interpretation of Art. 25 GDPR and related provisions, especially with respect to a more effective implementation of transparency and consent, and provide an outlook on possible next research steps.
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.
Der technische Fortschritt im Bereich der Erhebung, Speicherung und Verarbeitung von Daten macht es erforderlich, neue Fragen zu sozialverträglichen Datenmärkten aufzuwerfen. So gibt es sowohl eine Tendenz zur vereinfachten Datenteilung als auch die Forderung, die informationelle Selbstbestimmung besser zu schützen. Innerhalb dieses Spannungsfeldes bewegt sich die Idee von Datentreuhändern. Ziel des Beitrags ist darzulegen, dass zwischen verschiedenen Formen der Datentreuhänderschaft unterschieden werden sollte, um der Komplexität des Themas gerecht zu werden. Insbesondere bedarf es neben der mehrseitigen Treuhänderschaft, mit dem Treuhänder als neutraler Instanz, auch der einseitigen Treuhänderschaft, bei dem der Treuhänder als Anwalt der Verbraucherinteressen fungiert. Aus dieser Perspektive wird das Modell der Datentreuhänderschaft als stellvertretende Deutung der Interessen individueller und kollektiver Identitäten systematisch entwickelt.
Current research in augmented, virtual, and mixed reality (XR) reveals a lack of tool support for designing and, in particular, prototyping XR applications. While recent tools research is often motivated by studying the requirements of non-technical designers and end-user developers, the perspective of industry practitioners is less well understood. In an interview study with 17 practitioners from different industry sectors working on professional XR projects, we establish the design practices in industry, from early project stages to the final product. To better understand XR design challenges, we characterize the different methods and tools used for prototyping and describe the role and use of key prototypes in the different projects. We extract common elements of XR prototyping, elaborating on the tools and materials used for prototyping and establishing different views on the notion of fidelity. Finally, we highlight key issues for future XR tools research.
An der Hochschule Bonn-Rhein-Sieg fand am Donnerstag, den 23.9.21 das erste Verbraucherforum für Verbraucherinformatik statt. Im Rahmen der Online-Tagesveranstaltung diskutierten mehr als 30 Teilnehmer:innen über Themen und Ideen rund um den Bereich Verbraucherdatenschutz. Dabei kamen sowohl Beiträge aus der Informatik, den Verbraucher- und Sozialwissenschaften sowie auch der regulatorischen Perspektive zur Sprache. Der folgende Beitrag stellt den Hintergrund der Veranstaltung dar und berichtet über Inhalte der Vorträge sowie Anknüpfungspunkte für die weitere Konstituierung der Verbraucherinformatik. Veranstalter waren das Institut für Verbraucherinformatik an der H-BRS in Zusammenarbeit mit dem Lehrstuhl IT-Sicherheit der Universität Siegen sowie dem Kompetenzzentrum Verbraucherforschung NRW der Verbraucherzentrale NRW e. V. mit Förderung des Bundesministeriums der Justiz und für Verbraucherschutz.
An der Hochschule Bonn-Rhein-Sieg fand am Donnerstag, den 23.9.21 das erste Verbraucherforum für Verbraucherinformatik statt. Im Rahmen der Online-Tagesveranstaltung diskutierten mehr als 30 Teilnehmer:innen über Themen und Ideen rund um den Bereich Verbraucherdatenschutz. Dabei kamen sowohl Beiträge aus der Informatik, den Verbraucher- und Sozialwissenschaften sowie auch der regulatorischen Perspektive zur Sprache. Der folgende Beitrag stellt den Hintergrund der Veranstaltung dar und berichtet über Inhalte der Vorträge sowie Anknüpfungspunkte für die weitere Konstituierung der Verbraucherinformatik. Veranstalter waren das Institut für Verbraucherinformatik an der H-BRS in Zusammenarbeit mit dem Lehrstuhl IT-Sicherheit der Universität Siegen sowie dem Kompetenzzentrum Verbraucherforschung NRW der Verbraucherzentrale NRW e. V. mit Förderung des Bundesministeriums der Justiz und für Verbraucherschutz.
Frequently the main purpose of domestic artifacts equipped with smart sensors is to hide technology, like previous examples of a Smart Mirror show. However, current Smart Homes often fail to provide meaningful IoT applications for all residents’ needs. To design beyond efficiency and productivity, we propose to realize the potential of the traditional artifact for calm and engaging experiences. Therefore, we followed a design case study approach with 22 participants in total. After an initial focus group, we conducted a diary study to examine home routines and developed a conceptual design. The evaluation of our mid-fidelity prototype shows, that we need to study carefully the practices of the residents to leverage the physical material of the artifact to fit the routines. Our Smart Mirror, enhanced by digital qualities, supports meaningful activities and makes the bathroom more appealing. Thereby, we discuss domestic technology design beyond automation.
Recent publications propose concepts of systems that integrate the various services and data sources of everyday food practices. However, this research does not go beyond the conceptualization of such systems. Therefore, there is a deficit in understanding how to combine different services and data sources and which design challenges arise from building integrated Household Information Systems. In this paper, we probed the design of an Integrated Household Information System with 13 participants. The results point towards more personalization, automatization of storage administration and enabling flexible artifact ecologies. Our paper contributes to understanding the design and usage of Integrated Household Information Systems, as a new class of information systems for HCI research.
With the debates on climate change and sustainability, a reduction of the share of cars in the modal split has become increasingly prevalent in both public and academic discourse. Besides some motivational approaches, there is a lack of ICT artifacts that successfully raise the ability of consumers to adopt sustainable mobility patterns. To further understand the requirements and the design of these artifacts within everyday mobility adopted a practice-lens. This lens is helpful to get a broader perspective on the use of ICT artifacts along consumers’ transformational journey towards sustainable mobility practices. Based on 12 retrospective interviews with car-free mobility consumers, we argue that artifacts should not be viewed as ’magic-bullet’ solutions but should accompany the complex transformation of practices in multifaceted ways. Moreover, we highlight in particular the difficulties of appropriating shared infrastructures and aligning own practices with them. This opens up a design space to provide more support for these kinds of material-interactions, to provide access to consumption infrastructures and make them usable, rather than leaving consumers alone with increased motivation.
Over the last decades, different kinds of design guides have been created to maintain consistency and usability in interactive system development. However, in the case of spatial applications, practitioners from research and industry either have difficulty finding them or perceive such guides as lacking relevance, practicability, and applicability. This paper presents the current state of scientific research and industry practice by investigating currently used design recommendations for mixed reality (MR) system development. We analyzed and compared 875 design recommendations for MR applications elicited from 89 scientific papers and documentation from six industry practitioners in a literature review. In doing so, we identified differences regarding four key topics: Focus on unique MR design challenges, abstraction regarding devices and ecosystems, level of detail and abstraction of content, and covered topics. Based on that,we contribute to the MR design research by providing three factors for perceived irrelevance and six main implications for design recommendations that are applicable in scientific and industry practice.
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.