Crowd-Delivery als neues Lieferkonzept zur Stärkung des „Lokalen Marktplatzes“

Für viele Menschen scheint es mittlerweile komfortabler beim Shopping das Internet zu nutzen anstatt mehrere Geschäfte aufzusuchen. Studien belegen, dass Kunden immer häufiger den Onlinehandel wählen, der die Waren dann auch gleich bis vor die Haustür liefert. Dieser Trend hat dazu beigetragen, dass einerseits der stationäre Einzelhandel in den vergangenen Jahren erheblich unter Druck geraten ist und dass sich andererseits gleichzeitig eine höchst aufwändige Logistik entwickelte. Produkte werden einzeln von entlegenen Logistikzentren zu Kunden gebracht, anstatt sie gesammelt an den innerstädtischen Handel zu schicken. Ein virtueller Zusammenschluss innerstädtischer Händler zu einem „Lokalen Marktplatz“ scheint zukunftsweisend, können doch so die Vorzüge des Online-Handels mit dem physischen Shopping-Erlebnis verknüpft, sowie neue Absatzkanäle für innerstädtische Unternehmen geschaffen werden. Doch wie lassen sich die Vorzüge der Zulieferung durch den Online-Handel mit dem lokalen Marktplatz in der Innenstadt verbinden? Neue Konzepte in Bezug auf kollaborativen Konsum und nachhaltigen Güterverkehr versuchen dieser Hürde mit einem Lieferkonzept durch Privatpersonen zu begegnen - die Rede ist von Crowd-Delivery als eine spezifische Form des Crowdsourcings. Hierbei wird die Crowd-Delivery meist im Einkaufsverkehr vom Einzelhändler zum Kunden auf der letzten Meile angewandt: Dieser Mitbringservice ermöglicht KundInnen zu Hause, Einkäufe von anderen KundInnen mitbringen zu lassen, die ohnehin einkaufen gehen. Die Herausforderung besteht darin, die „Crowd“ als Zusteller zu gewinnen und deren Motive zu verstehen. Dies verlangt eine fundierte empirische Analyse zu Bedürfnissen, Anforderungen und potenziellen Anreizen dieser NutzerInnengruppen

The Data Product Canvas - A Visual Collaborative Tool for Designing Data-Driven Business Models

The availability of data sources and advances in analytics and artificial intelligence offers the opportunity for organizations to develop new data-driven products, services and business models. Though, this process is challenging for traditional organizations, as it requires knowledge and collaboration from several disciplines such as data science, domain experts, or business perspective. Furthermore, it is challenging to craft a meaningful value proposition based on data; whereas existing research can provide little guidance. To overcome those challenges, we conducted a Design Science Research project to derive requirements from literature and a case study, develop a collaborative visual tool and evaluate it through several workshops with traditional organizations. This paper presents the Data Product Canvas, a tool connecting data sources with the user challenges and wishes through several intermediate steps. Thus, this paper contributes to the scientific body of knowledge on developing data-driven business models, products and services

Use Your Data: Design and Evaluation of a Card-Based Ideation Tool for Data-Driven Services

Using data can significantly improve service design and development. However, for businesses, developing data-driven services can be challenging. To address this, we have developed the Data Service Cards (DSCs), a card-based tool to inspire the design of data-driven services. This paper presents two cycles of a design science research (DSR) project, focusing on the second cycle of redesign and evaluation of the DSCs. We conducted a two-step evaluation, including surveys and external expert ratings of data-driven service ideas. Survey results indicate that the DSCs are a valuable tool for developing data-driven services and external experts consider services designed using DSCs to be of higher quality. With the DSCs, we provide practitioners with a tool that facilitates and improves service design and supports digital transformation. Further, we contribute to DSR literature with a rigorous experimental procedure and to service innovation by supporting the early stages of data-driven service innovation

The Data-Driven Business Value Matrix - A Classification Scheme for Data-Driven Business Models

Increasing digitization is generating more and more data in all areas of business. Modern analytical methods open up these large amounts of data for business value creation. Expected business value ranges from process optimization such as reduction of maintenance work and strategic decision support to business model innovation. In the development of a data-driven business model, it is useful to conceptualise elements of data-driven business models in order to differentiate and compare between examples of a data-driven business model and to think of opportunities for using data to innovate an existing or design a new business model. The goal of this paper is to identify a conceptual tool that supports data-driven business model innovation in a similar manner: We applied three existing classification schemes to differentiate between data-driven business models based on 30 examples for data-driven business model innovations. Subsequently, we present the strength and weaknesses of every scheme to identify possible blind spots for gaining business value out of data-driven activities. Following this discussion, we outline a new classification scheme. The newly developed scheme combines all positive aspects from the three analysed classification models and resolves the identified weaknesses

Designing an ICT tooling platform to support SME business model innovation: Results of a first design cycle

Business model innovation (BMI) is becoming increasingly relevant for enterprises as they are faced with profound changes like digitalization. While business model thinking in academia has advanced, practical tooling that supports business model innovation for small and medium sized enterprises (SMEs) is still lacking. In this paper, we design, implement and evaluate an online platform with ICT-enabled tooling that supports business model innovation by SMEs. Based on interviews with ten SMEs and SME helpers, we define requirements for the BMI tooling platform. The implemented platform offers downloadable tools, decision support for finding the proper tooling, and interactive features for building communities of SMEs. Evaluation through log data analysis and informal interviews shows that the platform is usable and provides a relevant overview of BMI tooling, although several improvements are still suggested. As next steps, we will (1) create prefilled tools and templates to speed up the process of BMI; (2) create educational videos on how to use the tooling; (3) define paths on how to move from one tool to another; and (4) enhance the community features on the platform. The paper contributes to understanding how academic conceptualizations of BMI can be transferred into practically valuable artefacts for SMEs

Data Service Cards - A supporting tool for Data-Driven Business

In the future, every successful company must have a clear idea of what data means to it. The necessary transformation to a data-driven company places high demands on companies and challenges management, organization and individual employees. In order to generate concrete added value from data, the collaboration of different disciplines e.g. data scientists, domain experts and business people is necessary. So far few tools are available which facilitate the creativity and co-creation process amongst teams with different backgrounds. The goal of this paper is to design and develop a hands-on and easy to use card-based tool for the generation of data service ideas that supports the required interdisciplinary cooperation. By using a Design Science Research approach we analysed 122 data service ideas and developed an innovation tool consisting of 38 cards. The first evaluation results show that the developed Data Service Cards are both perceived as helpful and easy to use

GOTRIPLE:a user-centric process to develop a discovery platform

Social sciences and humanities (SSH) research is divided across a wide array of disciplines, sub-disciplines and languages. While this specialization makes it possible to investigate the extensive variety of SSH topics, it also leads to a fragmentation that prevents SSH research from reaching its full potential. The TRIPLE project brings answers to these issues by developing an innovative discovery platform for SSH data, researchers’ projects and profiles. Having started in October 2019, the project has already three main achievements that are presented in this paper: (1) the definition of main features of the GOTRIPLE platform; (2) its interoperability; (3) its multilingual, multicultural and interdisciplinary vocation. These results have been achieved thanks to different methodologies such as a co-design process, market analysis and benchmarking, monitoring and co-building. These preliminary results highlight the need for respecting diversity of practices and communities through coordination and harmonization

Addendum: Dumouchel, S., et al. GOTRIPLE: A User-Centric Process to Develop a Discovery Platform. Information 2020, 11, 563

The authors would like to add the following reference to the “Reference” section of their paper [...

Innovation Milieus for Mobility – Analysis of Innovation Lab Approaches for the Establishment of Urban Mobility Labs in Austria

The initiative „Urban Mobility Labs“ (UML), promoted by the Austrian Ministry of Transport, Innovation and Technology, was initiated to support the setup of innovative and experimental environments for research, testing, implementation and transfer of mobility solutions. This should happen by incorporating the scientific community, citizens and stakeholders in politics and administration as well as other groups. The emerging structural frame shall enhance the efficiency and effectivity of the innovation process. This paper gives insights and in-depth analysis of the approaches and experiences gained in the eight UML exploratory projects. These projects were analysed, systematized and enriched with further considerations. Furthermore, knowledge growth about user-centred innovation environments was documented during the exploratory phase