Refine
H-BRS Bibliography
- yes (30)
Departments, institutes and facilities
Document Type
- Conference Object (14)
- Part of a Book (9)
- Article (6)
- Report (1)
Keywords
- Peer-to-Peer (3)
- Privacy (3)
- Trust (3)
- Adoption (2)
- Blockchain (2)
- Digital Sovereignty (2)
- Sharing Economy (2)
- Adoption Factors (1)
- Advances in Design Science Research (1)
- Appropriation (1)
Sequencing Problems
(2024)
Dynamic Programming
(2024)
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.
Trust is the lubricant of the sharing economy, especially in peer-to-peer carsharing where you leave a valuable good to a stranger in the hope of getting it backunscathed. Central mechanisms for handling this information gap nowadays are ratings and reviews of other users. The rising of connected car technology opens new possibilities to increase trust by collecting and providing e.g. driving behavior data. At the same time, this means an intrusion into the privacy of the user. Therefore, in this work we explore technological approaches that allow building trust without violating the privacy of individuals. We evaluate to what extent blockchain technology and smart contracts are suitable technologies to meet these challengesby setting upa prototype implementation of a block-chain-based carsharing approach. In this context, we present our research approachand evaluate the prototype in terms of trust and privacy.
Spätestens seit der Belegausgabepflicht in Deutschland ist der digitale Kassenbon in aller Munde. Neben der Reduzierung umweltschädlichen Thermopapiers ergeben sich mit dieser Technologie auch neue Schnittstellen zwischen Kunde:in und Handel. Diese können für eine stärkere Digitalisierung und ein gesteigertes Kund:innen-Erlebnis genutzt werden.
Vor diesem Hintergrund betrachtet dieses Whitepaper die Perspektiven der verschiedenen Stakeholder, Architekturen sowie mögliche Mehrwertdienste zur Steigerung des Kund:innen-Erlebnis, aber auch zur Optimierung der Handelsprozesse.
Bei genauer Betrachtung heutiger Sharing Plattformen wie AirBnB, Uber, Drivy oder Fairleihen fällt auf, dass diese eines gemein haben. Als Plattformökonomien basieren sie auf mindestens zwei Nutzergruppen, Anbietern und Nachfragern für Güter oder Dienstleistungen. Ein Problem solcher zweioder mehrseitigen Märkte ist jedoch häufig, dass der Wertezuwachs, der durch die Nutzer generiert wird, nicht gleichmäßig unter der Plattform und den aktiven Nutzern verteilt wird, sondern meist ausschließlich als Gewinn an die Plattformen geht. Mit der Blockchain-Technologie könnte dieses Problem gelöst werden, indem der Informations- und Wertetransfer sicher und dezentral organisiert wird und viele Funktionen traditioneller Intermediäre dadurch obsolet werden. Diese Arbeit bietet einen Überblick über Anwendungsfelder und das Grundkonzept der Sharing Economy. Wir zeigen auf, wie sich Geschäftsmodelle und Infrastrukturen in einer Blockchain abbilden lassen, welche Potentiale eine Blockchain-basierte Infrastruktur bietet, wann diese in der Sharing Economy sinnvoll sein kann und welche Probleme dadurch gelöst werden können.
Bedingt durch die fortlaufende Digitalisierung und den Big Data-Trend stehen immer mehr Daten zur Verfügung. Daraus resultieren viele Potenziale – gerade für Unternehmen. Die Fähigkeit zur Bewältigung und Auswertung dieser Daten schlägt sich in der Rolle des Data Scientist nieder, welcher aktuell einer der gefragtesten Berufe ist. Allerdings ist die Integration von Daten in Unternehmensstrategie und -kultur eine große Herausforderung. So müssen komplexe Daten und Analyseergebnisse auch nicht datenaffinen Stakeholdern kommuniziert werden. Hier kommt dem Data Storytelling eine entscheidende Rolle zu, denn um mit Daten eine Veränderung hervorrufen zu können, müssen vorerst Verständnis und Motivation für den Sachverhalt zielgruppenspezifisch geschaffen werden. Allerdings handelt es sich bei Data Storytelling noch um ein Nischenthema. Diese Arbeit leitet mithilfe einer systematischen Literaturanalyse die Erfolgsfaktoren von Data Storytelling für eine effektive und effiziente Kommunikation von Daten her, um Data Scientists in Forschung und Praxis bei der Kommunikation der Daten und Ergebnisse zu unterstützen.
Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation Systems
(2024)
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.
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.
Due to ongoing digitalization, more and more cloud services are finding their way into companies. In this context, data integration from the various software solutions, which are provided both on-premise (local use or licensing for local use of software) and as a service, is of great importance. In this regard, Integration Platform as a Service (IPaaS) models aim to support companies as well as software providers in the context of data integration by providing connectors to enable data flow between different applications and systems and other integration services. Since previous research has mostly focused on technical or legal aspects of IPaaS, this article focuses on deriving integration practices and design-related barriers and drivers regarding the adoption of IPaaS. Therefore, we conducted 10 interviews with experts from different software as a services vendors. Our results show that the main factors regarding the adoption of IPaaS are the standardization of data models, the usability and variety of connectors provided, and the issues regarding data privacy, security, and transparency.
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.
The corporate landscape is experiencing an increasing change in business models due to digitization. An increasing availability of data along the business processes enhance the opportunities for process automation. Technologies such as Robotic Process Automation (RPA) are widely used for business process optimization, but as a side effect an increase in stand-alone solutions and a lack of holistic approaches can be observed. Intelligent Process Automation (IPA) is said to support more complex processes and enable automated decision-making, but due to the lack of connectors makes the implementation difficult. RPA marketplaces can be a bridging technology to help companies implement Intelligent Process Automation. This paper explores the drivers and challenges for the adoption of RPA marketplaces to realize IPA. For this purpose, we conducted ten expert interviews with decision makers and IT staff from the process automation sector.
In the course of growing online retailing, recommendation systems have become established that derive recommendations from customers’ purchase histories. Recommending suitable food products can represent a lucrative added value for food retailers, but at the same time challenges them to make good predictions for repeated food purchases. Repeat purchase recommendations have been little explored in the literature. These predict when a product will be purchased again by a customer. This is especially important for food recommendations, since it is not the frequency of the same item in the shopping basket that is relevant for determining repeat purchase intervals, but rather their difference over time. In this paper, in addition to critically reflecting classical recommendation systems on the underlying repeat purchase context, two models for online product recommendations are derived from the literature, validated and discussed for the food context using real transaction data of a German stationary food retailer.
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.
Trust is the lubricant of the sharing economy. This is true especially in peer-to-peer carsharing, in which one leaves a highly valuable good to a stranger in the hope of getting it back unscathed. Nowadays, ratings of other users are major mechanisms for establishing trust. To foster uptake of peer-to-peer carsharing, connected car technology opens new possibilities to support trust-building, e.g., by adding driving behavior statistics to users' profiles. However, collecting such data intrudes into rentees' privacy. To explore the tension between the need for trust and privacy demands, we conducted three focus group and eight individual interviews. Our results show that connected car technologies can increase trust for car owners and rentees not only before but also during and after rentals. The design of such systems must allow a differentiation between information in terms of type, the context, and the negotiability of information disclosure.
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.
