CoChat
Sign Up
Cited by 241Impact of AI and Big Data on Business and SocietyArtificial Intelligence ApplicationsTechnology Adoption and User Behaviour

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 access
Schwaeke, Julia, Peters, Anna, Kanbach, Dominik K., Kraus, Sascha, Jones, Paul·2025·Journal of Small Business Management
DOI 10.1080/00472778.2024.2379999
Open this paper in CoChat — free account

Abstract

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/00472778.2024.2379999

Cites

143 of 143 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

1 retracted

Show all 144 references

Research works printed but not linked: 10

  1. Babucea, A. G., & Rabontu, C. I. (2022). Big data analytics in the context of business enterprises “constantin Brâncuşi” din târgu-jiu seria economie. In C.-E. Stegaroiu & C. Enea (Eds.), Economy series. Analele Universităţii. Annals of “constantin brancusi” (Vol. 1, pp. 67–74). University of Targu-Jiu.
  2. bin Ahmad, A. M., Masri, R. B., & Lee, C. A. (2019). Future challenges to the entrepreneur in relation to the changing business environment. Global Business & Management Research, 11(2), 197–205.
  3. Dedhia, S. (2021). Impact of big data & analytics (BD&A) learning intentions with moderating effect of transformational leadership as SME perspective. Turkish Online Journal of Qualitative Inquiry, 12(7), 14015–14022.
  4. 10.1108/IJSE-12-2021-0752 · DOI
  5. 10.29302/oeconomica.2020.22.2.15 · DOI
  6. Koppel, T., & Tšernikova, O. (2023). Factors affecting the SME ability to enter the AI revolution. EUAS Conference Proceedings, Tallinn, Estonia. (p. 12).
  7. Lee, S. W., & Lee, H. S. (2014). A study of an integrated model for the introduction of a big data system: TOE. The Journal of the Korea Society of Information Technology Applications, 21(4), 463–483.
  8. Mittal, N., Saif, I., & Ammanath, B. (2022). Fueling the AI transformation: Four key actions powering widespread value from AI, right now. In Deloitte’s state of AI in the enterprise (5th [Report]. Retrieved August 20, 2023, from. https://www2.deloitte.com/content/dam/Deloitte/us/Documents/deloitte-analytics/us-ai-institute-state-of-ai-fifth-edition.pdf
  9. Whitson G. M. (2024). Artificial intelligence. Salem Press Encyclopedia of Science.
  10. Worek, M., & Aaltonen, P. (2023). AI adoption challenges in manufacturing family firms: Case study. Proceedings of the ISPIM Conferences (pp. 1–10), Ljubljana, Slovenia.

Cited by

Sorted by citations

Related

See all

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
ORCID
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
DOI
10.1080/00472778.2024.2379999
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review