Elsevier
Technological Forecasting and Social Change
,  volume 213 ,  pages 124036

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

Reza Dabestani 1
, 
Sam Solaimani 1, 2
, 
Gazar Ajroemjan 1
, 
Kitty Koelemeijer 1
1
 
Center for Marketing & Supply Chain Management, Nyenrode Business University, the Netherlands | 
2
 
Department of Business, American University in Bulgaria, Sofia, Bulgaria | 
Publication type: Journal Article
Publication date: 2025-04-01
Elsevier
scimago Q1
wos Q1
SJR: 3.472
CiteScore: 26.3
Impact factor: 13.3
ISSN: 00401625, 18735509
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.
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Dabestani R. et al. Exploring the enablers of data-driven business models: A mixed-methods approach // Technological Forecasting and Social Change. 2025. Vol. 213. p. 124036.
GOST all authors (up to 50) Copy
Dabestani R., Solaimani S., Ajroemjan G., Koelemeijer K. Exploring the enablers of data-driven business models: A mixed-methods approach // Technological Forecasting and Social Change. 2025. Vol. 213. p. 124036.
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RIS Copy
TY - JOUR
DO - 10.1016/j.techfore.2025.124036
UR - https://linkinghub.elsevier.com/retrieve/pii/S0040162525000678
TI - Exploring the enablers of data-driven business models: A mixed-methods approach
T2 - Technological Forecasting and Social Change
AU - Dabestani, Reza
AU - Solaimani, Sam
AU - Ajroemjan, Gazar
AU - Koelemeijer, Kitty
PY - 2025
DA - 2025/04/01
PB - Elsevier
SP - 124036
VL - 213
SN - 0040-1625
SN - 1873-5509
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2025_Dabestani,
author = {Reza Dabestani and Sam Solaimani and Gazar Ajroemjan and Kitty Koelemeijer},
title = {Exploring the enablers of data-driven business models: A mixed-methods approach},
journal = {Technological Forecasting and Social Change},
year = {2025},
volume = {213},
publisher = {Elsevier},
month = {apr},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0040162525000678},
pages = {124036},
doi = {10.1016/j.techfore.2025.124036}
}