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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.
Digitisation has brought a major upheaval to the mobility sector, and in the future, self-driving cars will probably be one of the transport modes. This study extends transport and user acceptance research by analysing in greater depth how the new modes of autonomous private cars, autonomous carsharing and autonomous taxis fit into the existing traffic mix from today's perspective. It focuses on accounting for relative added value. For this purpose, user preference theory was used as a base for an online survey (n=172) on the relative added value of the new autonomous traffic modes. Results show that users see advantages in the autonomous modes for driving comfort and time utilization whereas, in comparison to conventional cars, in many other areas – especially in terms of driving pleasure and control – they see no advantages or even relative disadvantages. Compared to public transport, the autonomous modes offer added values in almost all characteristics. This analysis at the partwor th level provides a more detailed explanation for user acceptance of automated driving.
Shared Autonomous Vehicles: Potentials for a Sustainable Mobility and Risks of Unintended Effects
(2018)
Automated and connected cars could significantly reduce congestion and emissions through a more efficient flow of traffic and a reduction in the number of vehicles. An increase in demand for driving with autonomous vehicles is also conceivable due to higher comfort and improved quality of time using driverless cars. So far, empirical evidence supporting this hypothesis is missing. To analyze the influence of autonomous driving on mobility behavior and to uncover user preferences, which serve as an indicator for future travel mode choices, we conducted an online survey with a paired comparison of current and future travel modes with 302 German participants. The results do not confirm the hypothesis that ownership will become an outdated model in the future. Instead they suggest that private cars, whether traditional or fully automated, will remain the preferred travel mode. At the same time, carsharing will benefit from full automation more than private cars. However, findings indicate that the growth of carsharing will mainly be at the expense of public transport, showing that more effort should be placed in making public transportation more attractive if sustainable mobility is to be developed.
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.
Vertrauen ist das Schmiermittel der Shareconomy. Einen zentralen Mechanismus hierfür stellen Crowd-basierte Reputationssysteme dar, bei denen Informationen und Bewertungen anderer Nutzer dazu dienen Vertrauen aufzubauen. Die Vernetzung zu teilender Gegenstände bietet hierbei neue Potentiale, um die Reputation eines Anbieters oder Nachfragers zu bewerten und einzuschätzen. In diesem Beitrag untersu-chen wir daher das Potential eines IoT-basierten Reputationssystems im Kontext von Peer-to-Peer Car-sharing, bei dem Informationen und Bewertungen mittels Sensorik während der Nutzung des Fahrzeugs erhoben und ausgewertet werden. Hierzu wurden zwei Fokusgruppen mit insgesamt 12 Personen durch-geführt. Die Ergebnisse deuten an, dass datenbasierte Reputationssysteme das Vertrauen nicht nur vor, sondern auch während der Vermietung und in der Nachkontrolle für Ver- und Entleiher steigern können. Jedoch sollten bei der Gestaltung solcher Systeme die Prinzipien der mehrseitigen Sicherheit wie Spar-samkeit, Verhältnismäßigkeit, Transparenz und Reziprozität beachtet werden.
Mobilitäts- und Nachhaltigkeitsforscher sehen sich bei der Erforschung des Mobilitätsverhaltens von Personen mit einer bunten Palette an Erhebungsmethoden konfrontiert. Erweitert wird diese Vielfalt in der letzten Zeit durch die Möglichkeit, dieses Verhalten direkt über die Smartphones der Probanden zu erfassen. Um die Auswahl geeigneter Methoden zu erleichtern, liefert die vorliegende Literaturstudie einen detaillierten Überblick zu Fragestellungen, Daten und Erhebungsmethoden, die im Bereich der Mobilitätsforschung zur Erfassung von Alltagsmobilität eingesetzt werden.
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.
Who do you trust: Peers or Technology? A conjoint analysis about computational reputation mechanisms
(2020)
Peer-to-peer sharing platforms are taking over an increasingly important role in the platform economy due to their sustainable business model. By sharing private goods and services, the challenge arises to build trust between peers online mostly without any kind of physical presence. Peer rating has been proven as an important mechanism. In this paper, we explore the concept called Trust Score, a computational rating mechanism adopted from car telematics, which can play a similar role in carsharing. For this purpose, we conducted a conjoint analysis where 77 car owners chose between fictitious user profiles. Our results show that in our experiment the telemetric-based score slightly outperforms the peer rating in the decision process, while the participants perceived the peer rating more helpful in retrospect. Further, we discuss potential benefits with regard to existing shortcomings of user rating, but also various concerns that should be considered in concepts like telemetric-based reputation mechanism that supplements existing trust factors such as user ratings.