In recent years, process mining has emerged as the leading big data technology for business process analysis. By extracting knowledge from event logs in information systems, process mining provides unprecedented transparency of business processes while being independent of the source system. However, despite its practical relevance, there is still a limited understanding of how organizations act upon the pervasive transparency created by process mining and how they leverage it to benefit from increased process awareness. Addressing this gap, this study conducts a multiple case study to explore how four organizations achieved increased process awareness by using process mining. Drawing on data from 24 semi-structured interviews and archival sources, this study reveals seven sociotechnical mechanisms based on process mining that enable organizations to create either standardized or shared awareness of sub-processes, end-to-end processes, and the firm’s process landscape. Thereby, this study contributes to research on business process management by revealing how process mining facilitates mechanisms that serve as a new, data-driven way of creating process awareness. In addition, the findings indicate that these mechanisms are influenced by the governance approach chosen to conduct process mining, i.e., a top-down or bottom-up driven implementation approach. Last, this study also points to the importance of balancing the social complications of increased process transparency and awareness. These results serve as a valuable starting point for practitioners to reflect on measures to increase organizational process awareness through process mining.

While business process automation through information technology has progressed over the last decades, smart contracts have recently emerged as a promising new means of automation. However, in practice, the adoption of smart contract-based automation is in its infancy, raising the question if the technology genuinely offers a unique approach to process automation. Drawing on an exploratory case study of four start-ups, we investigate the potentials for automation that organizations achieve through smart contracts and how smart contracts differ from established automation technologies, such as workflow management systems, enterprise resource planning systems, and robotic process automation. We contribute to the literature on process automation by unveiling transparent and immutable, crossorganizational, and decentralized automation as characteristics that differentiate smart contracts from established automation technologies. Besides, we provide practitioners with an understanding of application scenarios, potentials, and drawbacks of smart contracts for process automation.

Process mining is a big data technology, which focuses on the discovery, monitoring, and improvement of business processes, based on real data from information systems. This teaching case describes the objectives of a German airline as it introduces process mining and discusses current and future value potentials of this technology. The case is particularly useful for executive MBA courses on Strategy (the value of IT investments) or master’s-level courses on Business Process Management. This case has three main learning objectives. First, students will evaluate the capabilities of different (technological) approaches to reaching the airline’s business goals and will make a justified decision on the feasibility of implementing process mining. Second, students will analyze the airline’s approach to implementing process mining and the challenges along the way. They will derive lessons learned and discuss approaches to solving challenges. Third, students will evaluate the value potentials of process mining. This will enable the students to make well-informed decisions on technology investments and to discover how these decisions can contribute to business goals. The case is designed to be taught in two formats. In a 90-min lecture, students need to prepare short assignments for classroom discussions. In a 180-min lecture, the assignments are included as group work during the lecture, but they require the students to read the case before class. Teaching Notes, including videos and additional study material to support group work, are available to eligible lecturers upon request.