Strategic Sourcing of Digital Platforms in the Industrial Internet of Things

Edoardo Abate, Max Brandt & Michele Franco Scarperi

Student thesis: Master thesis

Abstract

The Industrial Internet of Things (IIoT) consists of the Internet of Things (IoT) implemented in industrial settings. Use cases range from the monitoring of manufacturing processes to the servitization of physical industrial products, which are supported by digital platforms. Through more pervasive information, businesses can benefit not only from minimizing inefficiencies in operations and reducing costs, but also unlock novel revenue streams. Although the literature on the IoT has flourished over recent years, the research area is still in its infancy and the IIoT, as a subset of the IoT, remains relatively unexplored. In particular, extant literature provides no theoretical understanding of the strategic sourcing decisions of IIoT platforms. With this identified research gap, this paper ties back to previous literature on sourcing decisions with theories such as resource-based view (RBV) and transaction cost theory (TCT). In addition to the more classical theories, this research encompassed recent literature on platform-driven ecosystems, with the goal to develop a modern model for make or buy decisions in the context of the IIoT. The conceptual model was evaluated through a multi-case study, based on data gathered via indepth interviews with 25 research participants, representing 12 different stakeholder companies in the IIoT. The resulting theoretical model, derived through a synthesis of TCT, RBV, and the ecosystem view of interfirm relationships, provides substantial contributions for both scholars and practitioners.

EducationsMSc in Business Administration and E-business, (Graduate Programme) Final Thesis
LanguageEnglish
Publication date2021
Number of pages149
SupervisorsBen Eaton