The nature of business conduct is changing due to emerging digital technologies and the ever-increasing role of data as a critical resource. Traditional industry branches such as logistics need to adapt accordingly to keep up with change through digitization and to design adequate business models using data. The present article focuses on investigating the anatomy of these data-driven business models in the logistics sector. In order to achieve this goal, the study develops a taxonomy of data-driven business models in logistics. Start-ups serve as the frame of reference, as they are particularly suitable for deriving explicitly novel and vital business models. The study focuses on two particular types of data-driven business models, namely those offering visibility or optimization services in logistics. The goal of the taxonomy is to uncover the structural composition of such business models and to make the results usable as a morphology for innovation.
Classification is an essential approach in business model research. Empirical classifications, termed taxonomies, are widespread in and beyond Information Systems (IS) and enjoy high popularity as both stand-alone artifacts and the foundation for further application. In this article, we focus on the study of empirical business model taxonomies for two reasons. Firstly, as these taxonomies serve as a tool to store empirical data about business models, we investigate their coverage of different industries and technologies. Secondly, as they are emerging artifacts in IS research, we aim to strengthen rigor in their design by illustrating essential design dimensions and characteristics. In doing this, we contribute to research and practice by synthesizing the diffusion of business model taxonomies that helps to draw on the available body of empirical knowledge and providing artifact-specific guidance for building taxonomies in the context of business models.
The open-source paradigm offers a plethora of opportunities for innovative business models (BMs) as the underlying codebase of the technology is accessible and extendable by external developers. However, finding the proper configuration of open-source business models (OSBMs) is challenging, as existing literature gives guidance through commonly used BMs but does not describe underlying design elements. The present study generates a taxonomy following an iterative development process based on established guidelines by analyzing 120 OSBMs to complement the taxonomy's conceptually-grounded design elements. Then, a cluster-based approach is used to develop archetypes derived from dominant features. The results show that OSBMs can be classified into seven archetypical patterns: open-source platform BM, funding-based BM, infrastructure BM, Open Innovation BM, Open Core BM, proprietary-like BM, and traditional open-source software (OSS) BM. The results can act as a starting point for further investigation regarding the use of the open-source paradigm in the era of digital entrepreneurship. Practitioners can find guidance in designing OSBMs.
Digital platforms disrupt today’s industries with novel offers and business models. Despite their revolutionizing impact, over 80% of platforms fail. To better understand what differentiates failing from successful platforms, we identified 30 platform success factors. These range from corporate value integration, where the platform strategy gets defined and the platform’s value definition, to the platform architecture covering all essential IT considerations and best practices making, e.g., innovation and personalization possible. To make these success factors usable in practice, we integrated all 30 success factors into the wellestablished Business Model Canvas (BMC) widely adopted for business model design. This method was chosen to test the applicability and usefulness of the platform success factors in a widely used vehicle and thus ease our workshop study. The success factors are promising, as demonstrated in our workshop study.
Data are a valuable asset for companies in the logistics sector to optimize internally and develop new business models. They can be like a magnifying glass, making previously opaque logistical processes transparent and finding previously hidden optimization potentials. Typical applications are tracking the transport status, route optimization, monitoring pharmaceutical products, or monitoring shocks for fragile cargo along the trade lanes. One way to use data is to tap into publicly or commercially available Application Programming Interfaces (APIs). As a result, logistics service providers can get or provide data automatically via a machine-tomachine interface. However, the landscape of API service providers is vast, unstructured, and intransparent in terms of potential data that companies can leverage. Given their high potential for logistics, the paper proposes a taxonomy of API services in logistics based on the inductive analysis of three API databases.
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