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Exploring the enablers of data-driven business models: A mixed-methods approach

Author

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  • Dabestani, Reza
  • Solaimani, Sam
  • Ajroemjan, Gazar
  • Koelemeijer, Kitty

Abstract

One of the critical objectives underlying the digital transformation initiatives of numerous enterprises is the introduction of novel data-driven business models (DDBMs) aimed at facilitating the creation, delivery, and capture of value. While DDBMs has gained immense traction among scholars and practitioners, the implementation and scaling leave much to be desired. One widely argued reason is our poor understanding of the factors that enable DDBM's effective implementation. Using a mixed-methods approach, this study identifies a comprehensive set of enablers, explores the enablers' interdependencies, and discusses how the empirical findings are of value in DDBMs' implementation from theoretical and practical viewpoints.

Suggested Citation

  • Dabestani, Reza & Solaimani, Sam & Ajroemjan, Gazar & Koelemeijer, Kitty, 2025. "Exploring the enablers of data-driven business models: A mixed-methods approach," Technological Forecasting and Social Change, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:tefoso:v:213:y:2025:i:c:s0040162525000678
    DOI: 10.1016/j.techfore.2025.124036
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