Data or business first? Manufacturers' transformation toward data-driven business models

Artikel

Driven by digital technologies, manufacturers aim to tap into data-driven business models, in which value is generated from data as a complement to physical products. However, this transformation can be complex, as different archetypes of data-driven business models require substantially different business and technical capabilities. While there are manifold contributions to research on technical capability development, an integrated and aligned perspective on both business and technology capabilities for distinct data-driven business model archetypes is needed. This perspective promises to enhance research's understanding of this transformation and offers guidance for practitioners. As maturity models have proven to be valuable tools in capability development, we follow a design science approach to develop a maturity model for the transformation toward archetypal data-driven business models. To provide an integrated perspective on business and technology capabilities, the maturity model leverages a layered enterprise architecture model. By applying and evaluating in use at two manufacturers, we find two different transformation approaches, namely 'data first' and 'business first'. The resulting insights highlight the model's integrative perspective's value for research to improve the understanding of this transformation. For practitioners, the maturity model allows a status quo assessment and derives fields of action to develop the capabilities required for the aspired data-driven business model.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Schmalenbach Journal of Business Research (SBUR) ; ISSN: 2366-6153 ; Volume: 75 ; Year: 2023 ; Issue: 3 ; Pages: 303-343

Klassifikation und Themen

• Klassifikation: Management ; Industry Studies: Manufacturing: General ; Industrialization; Manufacturing and Service Industries; Choice of Technology ; Management of Technological Innovation and R&D
• Thema: Data analytics ; Data-driven business models ; Data-driven services ; Enterprise architecture ; Manufacturing

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Stahl, Bastian ; Häckel, Björn ; Leuthe, Daniel ; Ritter, Christian
• Ereignis: Veröffentlichung
• (wer): Springer
• (wo): Heidelberg
• (wann): 2023

Weitere Informationen

• DOI: doi:10.1007/s41471-023-00154-2
• Letzte Aktualisierung: 26.03.2025, 04:46 MEZ