Abstract:In the context of logistics, blockchain can help to increase end-to-end visibility along global supply chains. Thus, it can lead to improved tracking of goods and offer tamper-proof data to build trust among parties. Although a variety of blockchain use cases already exists, not all of them seem to rely on blockchain-specific features, but could rather be solved with traditional technologies. The purpose of this paper is, therefore, to identify characteristic use cases described for blockchain in the field of LSCM and to analyze them regarding their mindful technology use based on five mindful technology adoption principles: engagement with the technology; Technological novelty seeking; awareness of local context; cognizance of alternative technologies; and anticipation of technology alteration. The authors identified five blockchain case clusters and chose one case for each category to be analyzed in detail. Most cases demonstrate high engagement with the technology, but there are significant differences when it comes to the other mindful use principles. This paper highlights the need to understand the problem and to apply the right technology in order to solve it. When solving a problem, care should be taken to address a technology's unique features to ensure effectiveness and cost-efficiency.

The purpose of this paper is to provide a theory-based explanation for the generation of competitive advantage from Analytics and to examine this explanation with evidence from confirmatory case studies. A theoretical argumentation for achieving sustainable competitive advantage from knowledge unfolding in the knowledge-based view forms the foundation for this explanation. Literature about the process of Analytics initiatives, surrounding factors, and conditions, and benefits from Analytics are mapped onto the knowledge-based view to derive propositions. Eight confirmatory case studies of organizations mature in Analytics were collected, focused on Logistics and Supply Chain Management. A theoretical framework explaining the creation of competitive advantage from Analytics is derived and presented with an extensive description and rationale. This highlights various aspects outside of the analytical methods contributing to impactful and successful Analytics initiatives. Thereby, the relevance of a problem focus and iterative solving of the problem, especially with incorporation of user feedback, is justified and compared to other approaches. Regarding expertise, the advantage of cross-functional teams over data scientist centric initiatives is discussed, as well as modes and reasons of incorporating external expertise. Regarding the deployment of Analytics solutions, the importance of consumability, users assuming responsibility of incorporating solutions into their processes, and an innovation promoting culture (as opposed to a data-driven culture) are described and rationalized. Further, this study presents a practical manifestation of the knowledge-based view.

While Big Data and Analytics are arguably rising stars of competitive advantage, their application is often presented and investigated as an overall approach. A plethora of methods and technologies combined with a variety of objectives creates a barrier for managers to decide how to act, while researchers investigating the impact of Analytics oftentimes neglect this complexity when generalizing their results. Based on a cluster analysis applied to 46 case studies of Supply Chain Analytics (SCA) we propose 6 archetypes of initiatives in SCA to provide orientation for managers as means to overcome barriers and build competitive advantage. Further, the derived archetypes present a distinction of SCA for researchers seeking to investigate the effects of SCA on organizational performance.