Artificial Intelligence Adoption for Sustainable Growth in
                    SMEs
                    : An Extended Dynamic Capability Framework

Antonio Cimino; Vincenzo Corvello; Ciro Troise; ASHA THOMAS; Mario Tani

doi:10.1002/csr.70019

journal article Open Access Jun 15, 2025

Artificial Intelligence Adoption for Sustainable Growth in SMEs : An Extended Dynamic Capability Framework

Antonio Cimino

Corporate Social Responsibility and Environmental Management Vol. 32 No. 5 pp. 6120-6138 · Wiley

View at Publisher Save 10.1002/csr.70019

Abstract

ABSTRACT
The adoption of Artificial Intelligence is transforming enterprises worldwide, influencing various aspects of business operations and affecting all dimensions of the triple bottom line. Companies ready to leverage the potential of this technology can significantly improve their performance. Therefore, it is crucial to understand the relationship between internal capabilities and contextual factors on one hand, and Artificial Intelligence adoption and its impact on performance on the other. Within this research framework, this study introduces an extended dynamic capability framework to analyze the interplay between internal factors, Artificial Intelligence adoption, and companies performance. The proposed model is tested using Partial Least Squares—Structural Equation Modeling on survey data from 210 Italian innovative startups. The findings indicate that companies with well‐developed dynamic capabilities, enabling them to adapt more effectively to environmental changes, are also better equipped to adopt Artificial Intelligence, leading to positive social, economic, and environmental performance.

Topics

No keywords indexed for this article. Browse by subject →

References

125

[1]

10.1108/jeee‐07‐2022‐0207

[2]

10.1002/csr.2654

[3]

10.1111/j.1540‐5915.2009.00236.x

[4]

10.1017/jmo.2014.5

[5]

Factors influencing the adoption of artificial intelligence in e-commerce by small and medium-sized enterprises

Soliman Aljarboa

International Journal of Information Management Da... 10.1016/j.jjimei.2024.100285

[6]

Measuring Institutions’ Adoption of Artificial Intelligence Applications in Online Learning Environments: Integrating the Innovation Diffusion Theory with Technology Adoption Rate

Mohammed Amin Almaiah, Raghad Alfaisal, Said A. Salloum et al.

Electronics 10.3390/electronics11203291

[7]

10.1002/csr.2261

[8]

10.1016/j.techfore.2023.122880

[9]

10.1016/j.techsoc.2024.102733

[10]

10.1016/j.indmarman.2021.09.003

[11]

10.3390/su16051864

[12]

10.1017/jmo.2019.88

[13]

Dynamic Capabilities: A Review of Past Research and an Agenda for the Future

Ilídio Barreto

Journal of Management 10.1177/0149206309350776

[14]

10.1111/1467‐6451.00112

[15]

10.1016/j.jclepro.2012.07.007

[16]

10.1108/ejim‐08‐2021‐0378

[17]

10.1007/s10551‐0121422‐3

[18]

10.1016/j.jclepro.2017.10.277

[19]

10.1108/jsma‐02‐2021‐0049

[20]

10.1016/j.techsoc.2019.101210

[21]

10.1007/s10799‐024‐00422‐5

[22]

Chen J. "The Study of System on Technological Innovation Performance in Enterprises" Management Science, S&T (2006)

[23]

10.1080/08874417.2023.2277751

[24]

10.1016/j.giq.2020.101520

[25]

10.1108/09534811211228148

[26]

10.1108/00251740710745089

[27]

10.1002/csr.1673

[28]

10.1016/s0169-7218(10)01004-x

[29]

10.1016/j.indmarman.2025.01.016

[30]

10.1016/j.joitmc.2023.100104

[31]

10.1002/bse.1932

[32]

Davenport T. H. "Artificial Intelligence for the Real World" Harvard Business Review (2018)

[33]

10.1016/s0883‐9026(98)00013‐5

[34]

10.1016/j.lrp.2014.02.004

[35]

10.25300/misq/2015/39.2.02

[36]

10.1002/smj.882

[37]

10.1016/j.ijinfomgt.2019.01.021

[38]

Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations

Rameshwar Dubey, Angappa Gunasekaran, Stephen J. Childe et al.

International Journal of Production Economics 10.1016/j.ijpe.2019.107599

[39]

Edquist C. (2005)

[40]

10.1016/j.jclepro.2019.07.013

[41]

10.1080/00207543.2021.1966850

[42]

Fagerberg J. (2005)

[43]

10.1016/j.ibusrev.2017.04.002

[44]

10.1108/jbim‐12‐2016‐0289

[45]

10.1111/jpim.12687

[46]

10.1057/jibs.2008.96

[47]

10.1111/j.1745‐493x.2010.03213.x

[48]

Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

Claes Fornell, David F. Larcker

Journal of Marketing Research 10.1177/002224378101800104

[49]

10.1016/j.jretai.2005.11.007

[50]

Thinking about U : Theorizing and testing U ‐ and inverted U ‐shaped relationships in strategy research

Richard F. J. Haans, Constant Pieters, Zi‐Lin He

Strategic Management Journal 10.1002/smj.2399

Showing 50 of 125 references

Cited By

28

Entropy-Based Assessment of AI Adoption Patterns in Micro and Small Enterprises: Insights into Strategic Decision-Making and Ecosystem Development in Emerging Economies

Gelmar García-Vidal, Alexander Sánchez-Rodríguez · 2025

Information

Metrics

28

Citations

125

References

Details

Published: Jun 15, 2025
Vol/Issue: 32(5)
Pages: 6120-6138
License: View

Authors

A

Antonio Cimino

Department of Engineering University of Messina Messina Italy

V

Vincenzo Corvello

Department of Engineering University of Messina Messina Italy

C

Ciro Troise

Department of Management University of Turin Turin Italy

A

ASHA THOMAS

Department of Operations Research and Business Intelligence Wrocław University of Science and Technology Wrocław Poland

M

Mario Tani

Department of Economics, Management, Institutions University of Naples Federico II Naples Italy

Cite This Article

Antonio Cimino, Vincenzo Corvello, Ciro Troise, et al. (2025). Artificial Intelligence Adoption for Sustainable Growth in SMEs : An Extended Dynamic Capability Framework. Corporate Social Responsibility and Environmental Management, 32(5), 6120-6138. https://doi.org/10.1002/csr.70019

Artificial Intelligence Adoption for Sustainable Growth in SMEs : An Extended Dynamic Capability Framework

You May Also Like