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.

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.

Across all industries, smart, connected products-such as connected cars, smart home appliances, smart fitness trackers and connected drilling machines-are altering how manufacturing companies interact with their customers and ultimately how they conduct business. This new generation of offerings results from the merger of the physical and digital worlds, often referred to as the Internet of Things (IoT). IoT solutions involve equipping physical objects and devices with sensors, actuators, and connectivity, and applying data analytics to the digital data streams (DDSs) flowing from the devices to offer complementary digital services. 3 The DDSs provide real-time data on usage and device behavior, as well as on environmental parameters. 4 Figure 1 shows there are four areas (domains) where IoT digital data streams can be used for innovation. These four domains are determined by the DDS source (supply-chain data or field data) and the focus of innovation (product/service or process). Manufacturing companies' early initiatives to benefit from IoT field data focused mainly on offering new digital services 1 Gabriele Piccoli is the accepting senior editor for this article. 2 The authors thank the 46 practitioners who participated in this study for providing interesting insights. Thanks also to Gabriele Piccoli, and two anonymous reviewers, and to Professor Stefan H. Thomke (Harvard Business School), Timo Gessmann (Bosch Digital Solutions) and Dr. Jannis Beese (SAP) for their valuable input and constructive feedback.

There is a broad consensus that the transformative power of the Internet of Things (IoT) will affect all kinds of industries; or, to put it in a more optimistic light, that almost no domain is excluded from the opportunities to leverage the IoT. But, what does this mean for the future of industrial processes? This article introduces the concept of high-resolution management (HRM). IoT enables the collection of high-resolution data for the physical world where, as in the digital world, every aspect of business operations can be measured in real-time. This capability facilitates high-resolution management, such as short optimization cycles in industrial production, logistics and equipment efficiency, comparable to methods like A/B-Testing or Search Engine Optimization, which are state of the art in digital business. We take the following two perspectives on leveraging high-resolution management. First, through greater insights into their industrial processes, companies that apply HRM in their operations are able to achieve higher efficiency, quality and flexibility. The example of vehicle fleet management illustrates this effect. Second, we build upon the St. Gallen Business Model Navigator in order to look in greater detail on how the IoT affects industrial processes. Gassmann et al.

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.

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Unternehmensberatungen, Marktforschungsinstitute und Technologie-Evangelisten haben sich in den letzten Jahren mit positiven Prognosen zum enormen Geschäftspotenzial im Internet der Dinge (aus dem Englischen Internet of Things, IoT) gegenseitig übertroffen. In der Tat eröffnen vernetzte IoT-Lösungen neue Geschäftspotenziale und sind derzeit im Begriff ganze Branchen zu disruptieren. Jedoch zeigen neuste empirische Forschungsergebnisse und bisherige Erfahrungen, dass Unternehmen diese Chancen viel langsamer realisieren als erwartet. Selbst IoT-Pioniere wie General Electric sind mit ihren ambitionierten Digitalisierungsinitiativen vorerst gescheitert und sehen sich gezwungen, ihre kommunizierten IoT-Pläne deutlich anzupassen. In Anbetracht dieses Digitalisierungs-Paradox, mit hohen Investitionskosten und niedrigen Erträgen, suchen Unternehmen heute mehr denn je nach neuen Möglichkeiten, die über bekannte Produkt-und Dienstleistungsinnovationen hinausgehen. Der vorliegende Grundlagenbeitrag widmet sich den Fragen, wie das IoT die zentralen Geschäftsmodellelemente von Unternehmen beeinflusst und wie das Digitalisierungs-Paradox