Data-driven business models: a blueprint for new product development
Babak Ziyae, Irfan Saleem, Amir AbiriPurpose
This study aims to develop and validate a comprehensive data-driven business model (DDBM) framework that serves as a strategic blueprint for new product development (NPD).
Design/methodology/approach
It uses a mixed-methods approach that combines qualitative and quantitative techniques, enabling the modeling of complex, feedback-driven, and nonlinear relationships among variables. In the qualitative phase, the study conducts semi-structured interviews with industry experts to identify the model's key dimensions. During the quantitative phase, the research applies the fuzzy cognitive map (FCM) method to test relationships among the identified DDBM dimensions.
Findings
The qualitative results identify 17 key variables within the DDBM, explaining how data influences activities ranging from understanding customer preferences to implementing smart logistics and optimizing revenue streams. The quantitative stage models the causal interdependencies among data assets, analytics capabilities, and mechanisms for the diffusion of innovation. Ultimately, by presenting a holistic framework that connects data to the NPD process, this research offers entrepreneurs insights into how to leverage data as a vital resource for value creation.
Originality/value
This study enhances the theoretical understanding of how businesses adopt data-driven approaches and provides a practical view of innovation processes. It also offers a comprehensive look at how the different parts of a DDBM interact, emphasizing the vital role of data in creating value and supporting NPD. Methodologically, this study is valuable for combining FCM and thematic analysis, offering a unique approach to addressing a gap in the entrepreneurship literature. This approach captures dynamic interactions among data flows, decision mechanisms, and innovation stages.