Challenges in Implementing Service-Oriented and Data-Driven Business Models in Mechanical and Plant Engineering: A Literature Review and Empirical Analysis

The digital transformation of the mechanical and plant engineering industry poses a significant challenge. As a consequence of increasing market saturation, companies struggle to differentiate themselves from their competitors. In this context, the adoption of service-oriented and data-driven business models may prove an effective approach for companies seeking to maintain their competitive advantage. However, the spread of these innovative business models within the industry has been relatively slow so far. Several factors have already been identified on both the supply and demand sides, but a comprehensive examination of the available data from a theoretical and practical perspective is still missing. This paper consolidates fragmented theoretical and empirical evidence on these barriers by integrating a structured literature review with expert interviews, workshops, and a sector-specific survey in mechanical and plant engineering. The resulting catalogue of challenges and requirements provides a systematic response to the question of why service-oriented and data-driven business models continue to exhibit low adoption rates in practice and identifies the areas where firms must take action. Situated within a broader design-science research program, these findings delineate the empirically grounded problem and requirement space for the subsequent development of the TPG framework, which is designed to support firms in the systematic implementation and scaling of such business models within digital ecosystems.