The DOI matches its Crossref record; 143 of the 144 references it cites were found in a registry or an index.
The new normal: The status quo of AI adoption in SMEs
Open accessAbstract
The recent surge in the adoption of artificial intelligence (AI) by small and medium-sized enterprises (SMEs) has garnered significant research attention. However, the existing literature reveals a fragmented landscape that hinders our understanding of how SMEs use AI. We address this through a systematic literature review wherein we analyze 106 peer-reviewed articles on AI adoption in SMEs and categorize states and trends into eight clusters: (1) compatibility, (2) infrastructure, (3) knowledge, (4) resources, (5) culture, (6) competition, (7) regulation, and (8) ecosystem: according to the technology–organization–environment model. Our research provides valuable insights and identifies significant gaps in existing literature, notably overlooking trends identification as a pivotal driver and neglecting legal requirements. Our study clarifies AI implementation within SMEs, offering a holistic and theoretically grounded perspective to empower researchers and practitioners to facilitate more effective adoption and application of AI within the SME sector.
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“This staged adoption perspective is particularly pertinent for SMEs in resource-constrained developing economies, where incremental AI adoption strategies help manage risks and gradually build their capabilities (Schwaeke et al., 2024).”
From Artificial Intelligence Adoption for Sustainable Development in SMEs: Challenges, Strategic Pathways, and Lessons for Developing Economies (2026) · cites this paper as Schwaeke et al., 2024
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Cites
1 retracted
RETRACTED ARTICLE: Small and medium-sized enterprises' contribution in digital technology
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Cited by
Sorted by citations
Sánchez, E., Calderón, R., & Herrera, F. (2025) · Applied Sciences · doi:10.3390/app15126465 · Cited by 123
Ayinaddis, S. G. (2025) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2025.100682 · Cited by 208
Jorzik, P., Antonio, J. L., Kanbach, D. K., Kallmuenzer, A., & Kraus, S. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123653 · Cited by 121
Self-citationBevilacqua, S., Masárová, J., Perotti, F. A., & Ferraris, A. (2025) · Review of Managerial Science · doi:10.1007/s11846-025-00836-7 · Cited by 139
Machucho-Cadena, R., & González, O. (2025) · Systems · doi:10.3390/systems13040264 · Cited by 120
Metadata
- Authors
- Schwaeke, J., Peters, A., Kanbach, D. K., Kraus, S., & Jones, P.
- Affiliations
- HHL Leipzig Graduate School of Management (DE)
- Free University of Bozen-Bolzano (IT)
- Swansea University (GB)
- Corresponding author
- Jones, Paul
- Venue
- Journal of Small Business Management
- Publisher
- Informa UK Limited
- Published
- 2024-08-13
- Volume
- 63
- Issue
- 3
- Pages
- 1297-1331
- ISSN
- 0047-2778 (ISSN-L)
- 1540-627X
- OpenAlex
- W4401536151
- Cited by
- 241
- References
- 144
- Citations by year
- 2026: 144 · 2025: 93 · 2024: 4
- FWCI
- 73.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Small Business Management (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (acceptedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- artificial intelligence adoption, small and medium-sized enterprises, technology-organization-environment framework, AI implementation, compatibility, infrastructure, knowledge, resources, organizational culture, competition, regulation, AI ecosystem, legal requirements
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