Know Center Research

Publikationen

Leski Florian, Fruhwirth Michael, Pammer-Schindler Viktoria
2021
The increasing volume of available data and the advances in analytics and artificial intelligence hold the potential for new business models also in offline-established organizations. To successfully implement a data-driven business model, it is crucial to understand the environment and the roles that need to be fulfilled by actors in the business model. This partner perspective is overlooked by current research on data-driven business models. In this paper, we present a structured literature review in which we identified 33 relevant publications. Based on this literature, we developed a framework consisting of eight roles and two attributes that can be assigned to actors as well as three classes of exchanged values between actors. Finally, we evaluated our framework through three cases from one automotive company collected via interviews in which we applied the framework to analyze data-driven business models for which our interviewees are responsible.
Breitfuß Gert, Fruhwirth Michael, Wolf-Brenner Christof, Riedl Angelika, Ginthör Robert, Pimas Oliver
2020
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.
Fruhwirth Michael, Breitfuß Gert, Pammer-Schindler Viktoria
2020
The availability of data sources and advances in analytics and artificial intelligence offers the opportunity for organizationsto develop new data-driven products, services and business models. Though, this process is challenging for traditionalorganizations, as it requires knowledge and collaboration from several disciplines such as data science, domain experts, orbusiness perspective. Furthermore, it is challenging to craft a meaningful value proposition based on data; whereas existingresearch can provide little guidance. To overcome those challenges, we conducted a Design Science Research project toderive requirements from literature and a case study, develop a collaborative visual tool and evaluate it through severalworkshops with traditional organizations. This paper presents the Data Product Canvas, a tool connecting data sources withthe user challenges and wishes through several intermediate steps. Thus, this paper contributes to the scientific body ofknowledge on developing data-driven business models, products and services.
Fruhwirth Michael, Rachinger Michael, Prlja Emina
2020
The modern economy relies heavily on data as a resource for advancement and growth. Data marketplaces have gained an increasing amount of attention, since they provide possibilities to exchange, trade and access data across organizations. Due to the rapid development of the field, the research on business models of data marketplaces is fragmented. We aimed to address this issue in this article by identifying the dimensions and characteristics of data marketplaces from a business model perspective. Following a rigorous process for taxonomy building, we propose a business model taxonomy for data marketplaces. Using evidence collected from a final sample of twenty data marketplaces, we analyze the frequency of specific characteristics of data marketplaces. In addition, we identify four data marketplace business model archetypes. The findings reveal the impact of the structure of data marketplaces as well as the relevance of anonymity and encryption for identified data marketplace archetypes.
Fruhwirth Michael, Pammer-Schindler Viktoria, Thalmann Stefan
2019
Data plays a central role in many of today's business models. With the help of advanced analytics, knowledge about real-world phenomena can be discovered from data. This may lead to unintended knowledge spillover through a data-driven offering. To properly consider this risk in the design of data-driven business models, suitable decision support is needed. Prior research on approaches that support such decision-making is scarce. We frame designing business models as a set of decision problems with the lens of Behavioral Decision Theory and describe a Design Science Research project conducted in the context of an automotive company. We develop an artefact that supports identifying knowledge risks, concomitant with design decisions, during the design of data-driven business models and verify knowledge risks as a relevant problem. In further research, we explore the problem in-depth and further design and evaluate the artefact within the same company as well as in other companies.
Breitfuß Gert, Fruhwirth Michael, Pammer-Schindler Viktoria, Stern Hermann, Dennerlein Sebastian
2019
Increasing digitization is generating more and more data in all areas ofbusiness. Modern analytical methods open up these large amounts of data forbusiness value creation. Expected business value ranges from process optimizationsuch as reduction of maintenance work and strategic decision support to businessmodel innovation. In the development of a data-driven business model, it is usefulto conceptualise elements of data-driven business models in order to differentiateand compare between examples of a data-driven business model and to think ofopportunities for using data to innovate an existing or design a new businessmodel. The goal of this paper is to identify a conceptual tool that supports datadrivenbusiness model innovation in a similar manner: We applied three existingclassification schemes to differentiate between data-driven business models basedon 30 examples for data-driven business model innovations. Subsequently, wepresent the strength and weaknesses of every scheme to identify possible blindspots for gaining business value out of data-driven activities. Following thisdiscussion, we outline a new classification scheme. The newly developed schemecombines all positive aspects from the three analysed classification models andresolves the identified weaknesses.
Fruhwirth Michael, Breitfuß Gert, Pammer-Schindler Viktoria
2018
The increasing amount of generated data and advances in technology and data analytics and are enablers and drivers for new business models with data as a key resource. Currently established organisations struggle with identifying the value and benefits of data and have a lack of know-how, how to develop new products and services based on data. There is very little research that is narrowly focused on data-driven business model innovation in established organisations. The aim of this research is to investigate existing activities within Austrians enterprises with regard to exploring data-driven business models and challenges encountered in this endeavour. The outcome of the research in progress paper are categories of challenges related to organisation, business and technology, established organisations in Austria face during data-driven business model innovation