UNDERSTANDING THE ANATOMY OF DATA-DRIVEN BUSINESS MODELS – TOWARDS AN EMPIRICAL TAXONOMY

As a consequence of the increasing digitization, massive amounts of data are created every day. While scholars and practitioners suggest that organizations can use this data to develop new data-driven business models, many organizations struggle to systematically develop such models. A fundamental challenge in this regard is presented by the limited research on data-driven business models. Accordingly, the goal of this research is to better understand data-driven business models by identifying key dimensions that can be used to distinguish them and to develop a taxonomy. As our taxonomy aims to guide future studies in a way that ultimately serves organizations, it is based on dimensions regarded to be most relevant from the practitioners’ perspective. To develop this taxonomy, we utilize an established empirical approach based on a combination of multidimensional scaling (MDS), property fitting (ProFit), and qualitative data. Our results reveal that the most important dimensions distinguish data-driven business models based on the data source utilized, the target audience, and the technological effort required. Based on these dimensions, our taxonomy distinguishes eight ideal-typical categories of data-driven business models. By providing an increased understanding regarding the topic, our results form the foundation for subsequent investigations in this new field of research

What constitutes a machine-learning-driven business model? A taxonomy of B2B start-ups with machine learning at their core

Artificial intelligence, specifically machine learning (ML), technologies are powerfully driving business model innovation in organizations against the backdrop of increasing digitalization. The resulting novel business models are profoundly shaped by ML, a technology that brings about unique opportunities and challenges. However, to date, little research examines what exactly constitutes these business models that use ML at their core and how they can be distinguished. Therefore, this study aims to contribute to an increased understanding of the anatomy of ML-driven business models in the business-to-business segment. To this end, we develop a taxonomy that allows researchers and practitioners to differentiate these ML-driven business models according to their characteristics along ten dimensions. Additionally, we derive archetypes of ML-driven business models through a cluster analysis based on the characteristics of 102 start-ups from the database Crunchbase. Our results are cross-industry, providing fertile soil for expansion through future investigations

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

Strategic considerations for construction in the People’s Republic of China: the case of German contractors in the 1990s

The construction industry has been struggling to integrate business strategies that are anticipating the internationalisation and infiltration of international markets. This article attempts to evaluate the China operations of German contractors from a strategic management decision perspective in the period between 1990 and 2000. Existing internationalisation theories have appeared to be inappropriate to explain international construction due to the unique project nature of construction business. The Ownership, Location and Internationalisation (OLI)-paradigm was initially developed to explain international production pattern was revised to form the basis for the evaluation of the Construction contractors’ market activities. The interviews indicate an industry-specific culture that affects how companies approach foreign markets. Some exceptional companies illustrated a higher degree of openness towards a more strategic and consistent approach in terms of the development of overseas markets

Unlashing the next Wave of Business Models in the Internet of Things Era: New Directions for a Research Agenda based on a Systematic Literature Review

Pervasive digitization of products and services open additional avenues for the next wave of business model opportunities. Most of firms are aware of the monetization potentials that the Internet of Things has to offer, however, they still struggle to create a compelling value propositions. Despite the attention of both research and practice onto business models and the IoT, only few concepts and research endeavors regarding their intersections exist. This paper tends to unleash the specificity of the business models within the IoT technologies, and motivate new, ecosystem, perspective for upcoming research. Following a rigorous methodology for a comprehensive and systematic literature review, we develop five literature clusters related to the IoT-driven business model research, evaluate and analyze the papers within clusters, and finally identify the gaps and propose directions for future research

Show me the Money: How to monetize data in data-driven business models?

Increasing digitization and the associated tremendous usage of technology have led to data of unprecedented quantity, variety, and speed, which is generated, processed, and required in almost all areas of industry and life. The value creation and capturing from data presents companies with numerous challenges, as they must create or adapt appropriate structures and processes. As a link between corporate strategy and business processes, business models are a suitable instrument for meeting these challenges. However, few research has been conducted focusing on data-based monetization in the context of data-driven business models so far. Based on a systematic literature review the paper identifies five key components and 23 characteristics of data-driven business models having crucial influence on data-based value creation and value capturing and thus on monetization. The components represent key factors for achieving commercial benefits from data and serve as guidance for exploring and designing suitable data-driven business models

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure