Dominik Bilgeri

A constantly growing pool of smart, connected Internet of Things (IoT) devices poses completely new challenges for business regarding security and privacy. In fact, the widespread adoption of smart products might depend on the ability of organizations to offer systems that ensure adequate sensor data integrity while guaranteeing sufficient user privacy. In light of these challenges, previous research indicates that blockchain technology may be a promising means to mitigate issues of data security arising in the IoT. Building upon the existing body of knowledge, we propose a design theory, including requirements, design principles, and features, for a blockchain-based sensor data protection system (SDPS) that leverages data certification. We then design and develop an instantiation of an SDPS (CertifiCar) in three iterative cycles that prevents the fraudulent manipulation of car mileage data. Furthermore, we provide an ex-post evaluation of our design theory considering CertifiCar and two additional use cases in the realm of pharmaceutical supply chains and energy microgrids. The evaluation results suggest that the proposed design ensures the tamper-resistant gathering, processing, and exchange of IoT sensor data in a privacy-preserving, scalable, and efficient manner.

The Internet of Things (loT) promises to deliver enormous business value. More specifically, loT solutions disrupt existing business models by opening up novel service opportunities. In order to help companies understand the opportunities and challenges of this development, we shed light on different loT revenue models. Based on an inductive case study approach, we identify nine direct and indirect revenue patterns. The different types of revenue patterns all use loT-enabled services to create value for customers; the extent and the monetization of services, however, vary.

An ever growing variety of smart, connected Internet of Things (IoT) devices poses completely new challenges for businesses regarding security and privacy. In fact, the adoption of smart products may depend on the ability of organizations to offer systems that ensure adequate sensor data integrity while guaranteeing sufficient user privacy. In light of these challenges, previous research indicates that blockchain technology could be a promising means to mitigate issues of data security arising in the IoT. Building upon the existing body of knowledge, we propose a design theory, including requirements, design principles, and features, for a blockchain-based sensor data protection system (SDPS) that leverages data certification. To support this, we designed and developed an instantiation of an SDPS (CertifiCar) in three iterative cycles intented to prevent the fraudulent manipulation of car mileage data. Following the explication of our SDPS, we provide an ex post evaluation of our design theory considering CertifiCar and two additional use cases in the areas of pharmaceutical supply chains and energy microgrids. Our results suggest that the proposed design ensures the tamper-resistant gathering, processing, and exchange of IoT sensor data in a privacy-preserving, scalable, and efficient manner.

Large manufacturing companies will in future be continuously challenged to develop and implement new IoT-related business models. Existing research offers interesting insights on high-level stages of business model innovation (BMI) processes in general. However, only little is known about the presence of main gates in BMI processes and even less about the underlying decision criteria applied at these gates. To shed more light on this research field, 27 expert interviews with employees from eight companies across the IoT ecosystem were conducted. The expert interviews reveal that, despite the increasing popularity of (radically) new innovation approaches, two main decision points can be identified across BMI processes. These findings are a first explorative step towards a better understanding of IoT adoption and provide a starting point for interesting future research avenues.

In light of emerging digital technologies, executives across industries are rethinking their companies’ business models and organizational structures. To meet future customer expectations, large manufacturing companies in particular are challenged to integrate two distinct worlds: the physical world (i.e. the design, production, and maintenance of complex hardware products) and the digital world (i.e. software, data analytics and digital services). Large manufacturing companies often possess various business units, a diversified business model portfolio, and complex IT landscapes including traditional, embedded, and digital IT types. Hence, they face specific organizational issues, which so far have only received limited attention among professionals. Based on 16 in-depth expert interviews with companies across the Internet of Things (IoT) ecosystem, we have identified six main issues regarding how digital transformation will affect large manufacturing companies’ overall organizational structure.