Open Access

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.

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.

Diese Studie untersucht die Aneignung und Nutzung von Sprachassistenten wie Google Assistant oder Amazon Alexa in Privathaushalten. Unsere Forschung basiert auf zehn Tiefeninterviews mit Nutzern von Sprachassistenten sowie der Evaluation bestimmter Interaktionen in der Interaktionshistorie. Unsere Ergebnisse illustrieren, zu welchen Anlässen Sprachassistenten im heimischen Umfeld genutzt werden, welche Strategien sich die Nutzer in der Interaktion mit Sprachassistenten angeeignet haben, wie die Interaktion abläuft und welche Schwierigkeiten sich bei der Einrichtung und Nutzung des Sprachassistenten ergeben haben. Ein besonderer Fokus der Studie liegt auf Fehlinteraktionen, also Situationen, in denen die Interaktion scheitert oder zu scheitern droht. Unsere Studie zeigt, dass das Nutzungspotenzial der Assistenten häufig nicht ausgeschöpft wird, da die Interaktion in komplexeren Anwendungsfällen häufig misslingt. Die Nutzer verwenden daher den Sprachassistenten eher in einfachen Anwendungsfällen und neue Apps und Anwendungsfälle werden gar nicht erst ausprobiert. Eine Analyse der Aneignungsstrategien, beispielsweise durch eine selbst erstellte Liste mit Befehlen, liefert Erkenntnisse für die Gestaltung von Unterstützungswerkzeugen sowie die Weiterentwicklung und Optimierung von sprachbasierten Mensch-Maschine-Interfaces.

This paper aspires to develop a deeper understanding of the sharing/collaborative/platform economy, and in particular of the technical mechanisms upon which the digital platforms supporting it are built. In surveying the research literature, the paper identifies a gap between studies from economical, social or socio-technical angles, and presentations of detailed technical solutions. Most cases study larger, ‘monotechnological’ platforms, rather than local platforms that lend components from several technologies. Almost no literature takes a design perspective. Rooted in Sharing & Caring, an EU COST Action (network), the paper presents work to systematically map out functionalities across domains of the sharing economy. The 145 technical mechanisms we collected illustrate how most platforms are depending on a limited number of functionalities that lack in terms of holding communities together. The paper points to the necessity of a better terminology and concludes by discussing challenges and opportunities for the design of future and more inclusive platforms.

Software offshoring has been established as an important business strategy over the last decade. While research on such forms of Global Software Development (GSD) has mainly focused on the situation of large enterprises, small enterprises are increasingly engaging in offshoring, too. Representing the biggest share of the German software industry, small companies are known to be important innovators and market pioneers. They often regard their flexibility and customer-orientation as core competitive advantages. Unlike large corporations, their small size allows them to adopt software development approaches that are characterized by a high agility and flat hierarchies. At the same time, their distinct strategies make it unlikely that they can simply adopt management strategies that were developed for larger companies. Flexible development approaches like the ones preferred by small corporations have proven to be problematic in the context of offshoring, as their strong dependency on constant communication is strongly affected by the various barriers of international cooperation between companies. Cooperating closely over companies’ borders in different time zones and in culturally diverse teams poses complex obstacles for flexible management approaches. It is still a matter of discussion in fields like Software Engineering and Computer Supported Cooperative Work how these obstacles can be tackled and how they affect companies in the long term. Hence, it is agreed that we need a more detailed understanding of distributed software development practices in order to come to feasible technological and organizational solutions. This dissertation presents results from two ethnographically-informed case studies of software offshoring in small German enterprises. By adopting Anselm Strauss’ concept of articulation work, we want to deepen the understanding of managing distributed software development in flexible, customer-oriented organizations. In doing so, we show how practices of coordinating inter-organizational software development are closely related to aspects of organizational learning in small enterprises. By means of interviews with developers and project managers from both parties of the cooperation, we do not only take into account the multiple perspectives of the cooperation, but also include the socio-cultural background of international software development projects into our analysis.

Innovations in the mobility industry such as automated and connected cars could significantly reduce congestion and emissions by allowing the traffic to flow more freely and reducing the number of vehicles according to some researchers. However, the effectiveness of these sustainable product and service innovations is often limited by unexpected changes in consumption: some researchers thus hypothesize that the higher comfort and improved quality of time in driverless cars could lead to an increase in demand for driving with autonomous vehicles. So far, there is a lack of empirical evidence supporting either one or other of these hypotheses. To analyze the influence of autonomous driving on mobility behavior and to uncover user preferences, which serve as indicators for future travel mode choices, we conducted an online survey with a paired comparison of current and future travel modes with 302 participants in Germany. The results do not confirm the hypothesis that ownership will become an outdated model in the future. Instead they suggest that private cars, whether conventional or fully automated, will remain the preferred travel mode. At the same time, carsharing will benefit from full automation more than private cars. However, the findings indicate that the growth of carsharing will mainly be at the expense of public transport, showing that more emphasis should be placed in making public transport more attractive if sustainable mobility is to be developed.

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.

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.