Artificial intelligence adoption dynamics and knowledge in SMEs and large firms: A systematic review and bibliometric analysis
Artificial intelligence (AI) has quickly emerged as a top technological priority for companies in various sectors, radically altering business operations. However, the existing literature reveals a fragmented and inconsistent understanding of AI adoption dynamics between small and medium enterprises...
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| Language: | English |
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Elsevier
2025-05-01
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| Series: | Journal of Innovation & Knowledge |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2444569X25000320 |
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| author | Samuel Godadaw Ayinaddis |
| author_facet | Samuel Godadaw Ayinaddis |
| author_sort | Samuel Godadaw Ayinaddis |
| collection | DOAJ |
| description | Artificial intelligence (AI) has quickly emerged as a top technological priority for companies in various sectors, radically altering business operations. However, the existing literature reveals a fragmented and inconsistent understanding of AI adoption dynamics between small and medium enterprises (SMEs) and larger, well-established firms. This dichotomy of the existing research raises important questions about whether the AI tools and application modalities used by these companies are inherently similar or if significant differences exist in their implementation and outcomes due to varying organizational sizes. This study evaluates whether small and large firms’ efforts toward implementing AI differ significantly using bibliometric analysis and a systematic literature review from the Web of Science and Scopus databases. A total of 78 peer-reviewed articles were analyzed and categorized states and trends into 10 dimensions: (1) technology readiness, (2) customization, (3) AI tools and needs, (4) data requirements, (5) skills and competencies, (6) financial readiness, (7) management support, (8) market and competitive pressure, (9) partnership and collaboration, and (10) regulatory compliance, based on the technology–organization–environment (TOE) theoretical model. A bibliometric mapping approach was adopted to visualize bibliometric data using VOSviewer. The review brings together collective insights from several leading expert contributors to emphasize areas where SMEs need additional support to fully leverage AI technologies. The results provide pragmatic insights for policymakers, helping them develop tailored approaches for both SMEs and large enterprises to meet their unique needs while acknowledging AI's undeniable role in competitiveness and growth. |
| format | Article |
| id | doaj-art-d2fbf4916c0a43fdbec933e088dfa307 |
| institution | Kabale University |
| issn | 2444-569X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Innovation & Knowledge |
| spelling | doaj-art-d2fbf4916c0a43fdbec933e088dfa3072025-08-20T03:39:57ZengElsevierJournal of Innovation & Knowledge2444-569X2025-05-0110310068210.1016/j.jik.2025.100682Artificial intelligence adoption dynamics and knowledge in SMEs and large firms: A systematic review and bibliometric analysisSamuel Godadaw Ayinaddis0Corresponding author.; Department of Economics and Management, University of Pisa, ItalyArtificial intelligence (AI) has quickly emerged as a top technological priority for companies in various sectors, radically altering business operations. However, the existing literature reveals a fragmented and inconsistent understanding of AI adoption dynamics between small and medium enterprises (SMEs) and larger, well-established firms. This dichotomy of the existing research raises important questions about whether the AI tools and application modalities used by these companies are inherently similar or if significant differences exist in their implementation and outcomes due to varying organizational sizes. This study evaluates whether small and large firms’ efforts toward implementing AI differ significantly using bibliometric analysis and a systematic literature review from the Web of Science and Scopus databases. A total of 78 peer-reviewed articles were analyzed and categorized states and trends into 10 dimensions: (1) technology readiness, (2) customization, (3) AI tools and needs, (4) data requirements, (5) skills and competencies, (6) financial readiness, (7) management support, (8) market and competitive pressure, (9) partnership and collaboration, and (10) regulatory compliance, based on the technology–organization–environment (TOE) theoretical model. A bibliometric mapping approach was adopted to visualize bibliometric data using VOSviewer. The review brings together collective insights from several leading expert contributors to emphasize areas where SMEs need additional support to fully leverage AI technologies. The results provide pragmatic insights for policymakers, helping them develop tailored approaches for both SMEs and large enterprises to meet their unique needs while acknowledging AI's undeniable role in competitiveness and growth.http://www.sciencedirect.com/science/article/pii/S2444569X25000320L22L25L26O30-O33M15 |
| spellingShingle | Samuel Godadaw Ayinaddis Artificial intelligence adoption dynamics and knowledge in SMEs and large firms: A systematic review and bibliometric analysis Journal of Innovation & Knowledge L22 L25 L26 O30-O33 M15 |
| title | Artificial intelligence adoption dynamics and knowledge in SMEs and large firms: A systematic review and bibliometric analysis |
| title_full | Artificial intelligence adoption dynamics and knowledge in SMEs and large firms: A systematic review and bibliometric analysis |
| title_fullStr | Artificial intelligence adoption dynamics and knowledge in SMEs and large firms: A systematic review and bibliometric analysis |
| title_full_unstemmed | Artificial intelligence adoption dynamics and knowledge in SMEs and large firms: A systematic review and bibliometric analysis |
| title_short | Artificial intelligence adoption dynamics and knowledge in SMEs and large firms: A systematic review and bibliometric analysis |
| title_sort | artificial intelligence adoption dynamics and knowledge in smes and large firms a systematic review and bibliometric analysis |
| topic | L22 L25 L26 O30-O33 M15 |
| url | http://www.sciencedirect.com/science/article/pii/S2444569X25000320 |
| work_keys_str_mv | AT samuelgodadawayinaddis artificialintelligenceadoptiondynamicsandknowledgeinsmesandlargefirmsasystematicreviewandbibliometricanalysis |