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Despite the anticipated benefits and the numerous announcements of pilot cases, we have seen very few successful implementations of blockchain technology (BCT) solutions in supply chains. Little is empirically known about the obstacles to blockchain adoption, particularly in a supply chain’s inter-organizational setting. In supply chains, blockchains’ benefits, for example BCT-based tracking and tracing, are dependent on a critical mass of supply chain actors’ adopting the technology. While previous research has mainly been conceptual and has lacked both theory and empirical data, we propose a theory-based model for inter-organizational adoption of BCT. We use the proposed model to analyze a unique, in-depth revelatory case study. Our case study confirms previous conceptual work and reveals a paradox as well as several tensions between drivers for and against (positive and negative determining factors, respectively) of BCT adoption that must be managed in an inter-organizational setting. In this vertical context, the adoption and integration decision of one supply chain actor recursively affects the adoption and integration decisions of the other supply chain actors. This paper contributes mid-range theory on BCT in supply chain management (SCM), future research directions, and managerial insights on BCT adoption in supply chains.

The focus of this paper is on the neural network modelling approach that has gained increasing recognition in GIScience in recent years. The novelty about neural networks lies in their ability to model non-linear processes with few, if any, a priori assumptions about the nature of the data-generating process. The paper discusses some important issues that are central for successful application development. The scope is limited to feedforward neural networks, the leading example of neural networks. It is argued that failures in applications can usually be attributed to inadequate learning and/or inadequate complexity of the network model. Parameter estimation and a suitably chosen number of hidden units are, thus, of crucial importance for the success of real world neural network applications. The paper views network learning as an optimization problem, reviews two alternative approaches to network learning, and provides insights into current best practice to optimize complexity so to perform well on generalization tasks.

This paper presents a framework for developing tool support for the design and management of new business models. Existing IT tools supporting the process of designing, innovating, and evaluating a company’s business model are currently not leveraging the full potential of tool support, since they do not make use of theoretical and empirical knowledge around business model development. Against this backdrop, we analyze existing knowledge on business model design and management, resulting in a first systematization of the activities that are necessary for developing and managing new business models. In order to complement this knowledge and to identify the requirements for sup-porting these activities, a series of expert interviews is conducted. Based on the results of the interview series, a new business model development tool (BMDT) is created and evaluated. The learnings of this development process are then consolidated in a unified framework. This framework constitutes a new solution for systematically designing tool support for business model development, and extends existing literature by highlighting the importance of collaboration between participants in a business model development project. It also provides designers of new BMDTs with an empirically based conceptualization to guide their efforts.