Fernandez De Arroyabe Fernandez, Juan Carlos and Arranz, Nieves and F. Arroyabe, Marta and F. A. Arranz, Carlos (2023) Digitalisation dynamics in SMEs: an approach from Systems Dynamics and Artificial Intelligence. Technological Forecasting and Social Change, 196. p. 122880. DOI https://doi.org/10.1016/j.techfore.2023.122880
Fernandez De Arroyabe Fernandez, Juan Carlos and Arranz, Nieves and F. Arroyabe, Marta and F. A. Arranz, Carlos (2023) Digitalisation dynamics in SMEs: an approach from Systems Dynamics and Artificial Intelligence. Technological Forecasting and Social Change, 196. p. 122880. DOI https://doi.org/10.1016/j.techfore.2023.122880
Fernandez De Arroyabe Fernandez, Juan Carlos and Arranz, Nieves and F. Arroyabe, Marta and F. A. Arranz, Carlos (2023) Digitalisation dynamics in SMEs: an approach from Systems Dynamics and Artificial Intelligence. Technological Forecasting and Social Change, 196. p. 122880. DOI https://doi.org/10.1016/j.techfore.2023.122880
Abstract
This paper addresses the study of digitalisation dynamics in SMEs. Improving on existing research and its methodological limitations, we provide an understanding of the digital transformation in SMEs by approaching the research from a non-linear and complex perspective. We empirically test our hypotheses using the Eurostat Flash Eurobarometer No. 486 data set, with a final sample of 16,365 SMEs. Our first contribution shows that an adequate understanding of digital transformation not only implies the identification of drivers of digitalisation but also a grasp of how these drivers act, highlighting the differential effect that internal capabilities and external support of the company in interaction have on digital transformation. Moreover, the results show that the effect of interactions between variables is transferred to the output variable in a non-linear process, which may contain an optimum produced by a differential combination of input variables. Second, the paper extends the research methodology, emphasising the importance of combining classic regression analysis with machine-learning techniques. Thus, using a systemic approach, we conclude that the combination of the explanatory power of regression models and machine learning allows us to quantify and explain how variables act, solving complex and non-linear problems.
Item Type: | Article |
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Uncontrolled Keywords: | Digitalisation; Dynamics; SME; System dynamics; Artificial intelligence |
Divisions: | Faculty of Social Sciences > Essex Business School Faculty of Social Sciences > Essex Business School > Strategy, Operations and Entrepreneurship |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 11 Jul 2025 14:25 |
Last Modified: | 11 Jul 2025 14:26 |
URI: | http://repository.essex.ac.uk/id/eprint/36445 |
Available files
Filename: Digitalisation dynamics in SMEs.pdf
Licence: Creative Commons: Attribution 4.0