Monetizing Car Data: A Literature Review on Data-Driven Business Models in the Connected Car Domain

The amount of data generated by a single modern vehicle is exploding. Consequently, the entire global automotive industry is facing the question of how to monetize this valuable data. Triggered by the connectivity trend, data-driven business models disrupt the automotive ecosystem by changing mobility behavior, proliferation of technical enablers, new strategic collaborations, and shifting revenue streams. In this study, we analyze the existing body of literature on data-driven business models in the connected car domain and structure it according to four dimensions---value proposition, value architecture, value network, and value finance. Thereby, we contribute to the business model research by providing a comprehensive overview and categorization of existing works in this area and laying the foundation for future research

Understanding Car Data Monetization: A Taxonomy of Data-Driven Business Models in the Connected Car Domain

Data monetization has proven to be one of the most viable profit pools across industries. As vehicles become increasingly connected, leveraging their collected data through novel business models is the most promising value driver for automotive enterprises. Despite the increasing practical relevance, theoretical and conceptual insights on connected cars and their associated business models are still scarce. Thus, we develop a taxonomy of data-driven business models in theconnected car domain according to four perspectives—value proposition, value architecture, value network, and value finance. Further, we apply the taxonomy to analyze the business model of 70 companies acting under the realm of connected cars. A subsequent evaluation indicates both the robustness and general feasibility of our taxonomy. Our taxonomy contributes to descriptive knowledge in this emerging field and enables researchers and practitioners to analyze, design, andconfigure data-driven business models for connected cars

Utilizing Fleet Data: Towards Designing a Connected Fleet Management System for the Effective Use of Multi-Brand Car Data

The connected car has recently evolved from a theoretical concept to reality. Especially in professionally managed fleets, car connectivity promises additional benefits in terms of costs, environment, and maintenance. However, many fleet managers are unaware of using connected car data and still associate telematics with retrofitting each vehicle. Thus, we aim to develop a connected fleet management system to increase fleet operations’ efficiency and effectiveness by utilizing multi-brand data from car manufacturers’ backend shared by data marketplaces. Thereby, we follow a design science research approach using inputs from the existing body of knowledge and the practical problem domain. Drawing on the theory of effective use, we propose meta-requirements and tentative design principles and instantiate them in a prototype artifact

Fostering Value Co-Creation in Incumbent Firms: The Case of Bosch’s IoT Ecosystem Landscape

The advent of the Internet of Things (IoT) forces incumbent firms to reshape their organizational structures toward platform ecosystems. However, prior research lacks concrete insights about how incumbent firms can foster value co-creation to become ecosystem orchestrators. In particular, it only sheds little light on the complex challenges incumbents face in designing and governing IoT platform ecosystems. In response, we present a single case study describing how the departments of Robert Bosch GmbH, a leading IoT company, overcame these challenges in three dimensions—IoT ecosystem, IoT platform, and value co-creation. We tie in our research with the existing body of literature, identify four prevailing tensions in ecosystem establishment, and provide actionable design and governance recommendations to resolve them

Reallocating Uncertainty in Incumbent Firms through Digital Platforms: The Case of Google’s Automotive Ecosystem Involvement

This research examines how incumbent firms decide on the degree of involvement of technology players in their digital strategies, by integrating insights from digital innovation and digital platform research. We conducted an embedded case study on the adoption of Google’s Android Automotive OS and Google Automotive Services by the automotive industry, using semi-structured interviews with industry experts and senior decision-makers. We build on affordance-actualization theory to develop a grounded model of uncertainty reallocation consisting of five aggregate dimensions: (1) external digital platform by tech firm, (2) incumbent firm and its goals, (3) uncertainty tradeoffs and affordance of reallocation, (4) strategic actions by incumbent firm, and (5) short- and long-term outcomes. Our results provide valuable insights into the selection of non-binary platform strategies and the effects of various levels of technology firm involvement. This addition to the knowledge base of the information systems discipline provides practical guidance for incumbent firms navigating digital transformation

Select strengths and biases of models in representing the Arctic winter boundary layer over sea ice: the Larcform 1 single column model intercomparison

Weather and climate models struggle to represent lower tropospheric temperature and moisture profiles and surface fluxes in Arctic winter, partly because they lack or misrepresent physical processes that are specific to high latitudes. Observations have revealed two preferred states of the Arctic winter boundary layer. In the cloudy state, cloud liquid water limits surface radiative cooling, and temperature inversions are weak and elevated. In the radiatively clear state, strong surface radiative cooling leads to the build-up of surface-based temperature inversions. Many large-scale models lack the cloudy state, and some substantially underestimate inversion strength in the clear state. Here, the transformation from a moist to a cold dry air mass is modeled using an idealized Lagrangian perspective. The trajectory includes both boundary layer states, and the single-column experiment is the first Lagrangian Arctic air formation experiment (Larcform 1) organized within GEWEX GASS (Global atmospheric system studies). The intercomparison reproduces the typical biases of large-scale models: some models lack the cloudy state of the boundary layer due to the representation of mixed-phase microphysics or to the interaction between micro- and macrophysics. In some models, high emissivities of ice clouds or the lack of an insulating snow layer prevent the build-up of surface-based inversions in the radiatively clear state. Models substantially disagree on the amount of cloud liquid water in the cloudy state and on turbulent heat fluxes under clear skies. Observations of air mass transformations including both boundary layer states would allow for a tighter constraint of model behavior

Spotlight on DeFi Centerpieces: Towards an Economic Perspective on Asset Tokenization Services

Experts consider tokenization a potentially disruptive blockchain-based innovation. Cryptographic tokens can represent ownership of tangible and intangible assets in the digital space, serve as a store of value and proof of ownership, and enable investments in historically illiquid assets. While there are promising use cases for these new technological capabilities, research on economic perspectives is still in its infancy. Therefore, we focus on asset tokenization services, develop a taxonomy following Nickerson et al. (2013), and align our analysis with established business model dimensions. Our dataset is based on a three-stage approach incorporating academic literature, consulting reports, and real-world projects. As a result, we identify 16 dimensions, 14 sub-dimensions, and 101 characteristics that improve our understanding of asset tokenization services and provide a starting point for further research

Spotlight on DeFi Centerpieces Towards an Economic Perspective on Asset Tokenization Services

Experts consider tokenization a potentially disruptive blockchain-based innovation. Cryptographic tokens can represent ownership of tangible and intangible assets in the digital space, serve as a store of value and proof of ownership, and enable investments in historically illiquid assets. While there are promising use cases for these new technological capabilities, research on economic perspectives is still in its infancy. Therefore, we focus on asset tokenization services, develop a taxonomy following Nickerson et al. (2013), and align our analysis with established business model dimensions. Our dataset is based on a three-stage approach incorporating academic literature, consulting reports, and real-world projects. As a result, we identify 16 dimensions, 14 sub-dimensions, and 101 characteristics that improve our understanding of asset tokenization services and provide a starting point for further research

Fostering Value Co-Creation in Incumbent Firms: The Case of Bosch’s IoT Ecosystem Landscape

The advent of the Internet of Things (IoT) forces incumbent firms to reshape their organizational structures toward platform ecosystems. However, prior research lacks concrete insights about how incumbent firms can foster value co-creation to become ecosystem orchestrators. In particular, it only sheds little light on the complex challenges incumbents face in designing and governing IoT platform ecosystems. In response, we present a single case study describing how the departments of Robert Bosch GmbH, a leading IoT company, overcame these challenges in three dimensions IoT ecosystem, IoT platform, and value co-creation. We tie in our research with the existing body of literature, identify four prevailing tensions in ecosystem establishment, and provide actionable design and governance recommendations to resolve them