Fernandez De Arroyabe Arranz, Carlos ORCID: 0000-0002-6866-0684, Arroyabe, Marta F., Arranz, Nieves and de Arroyabe, Juan Carlos Fernandez (2023) Digitalisation dynamics in SMEs: an approach from systems dynamics and Artificial intelligence. Technological Forecasting and Social Change, 196:122880. pp. 1-18. ISSN 0040-1625 (Print), 1873-5509 (Online) (doi:https://doi.org/10.1016/j.techfore.2023.122880)

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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
Uncontrolled Keywords:	digitalisation; dynamics; SME; system dynamics; Artificial intelligence
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HF Commerce Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Business
Last Modified:	26 Sep 2023 10:26
URI:	http://gala.gre.ac.uk/id/eprint/44326

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