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.

To sustain competitive advantage in dynamic business environments, organizations have to constantly adapt, innovate, and recombine their business models. As some configurations of business model design options are more successful than others, it is crucial to have a holistic understanding of the (current) solution space of those options and their dependencies. To be aware of and manage the set of possible design options, one can rely on classification tools, including taxonomies, typologies, and classification schemes. Given the availability of several tool types, different underlying assumptions for each type need to be considered when designing and applying a tool. Following a descriptive literature review approach, this paper structures the diverse body of classification research by presenting a repository of tools and deriving an analytical grid to disclose the similarities and differences between selected tool types. Thereby, this paper (1) raises awareness for the plurality of tools and their underpinning concepts, (2) provides a status quo overview across tool types, and (3) derives design-relevant knowledge for the tools, points to current challenges, and paves the ground for future research on the building, evaluation, and use of this class of tools.

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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